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Indian Society of GeomaticsExecutive Council 2011 - 2014

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Journal of Geomatics ii Vol.6, No.2 October 2012

Vol. 6, No. 2 October 2012

Research articles

1. Environmental monitoring of vegetation cover at Kalpakkam through NDVI approach 65 M. Sankar Ram, C. Anandan and P. Sasidhar

2. Klobuchar model for ionospheric delay correction in Saudi Arabia 71 Ashraf Farah

3. Fuzzy logic model for desertification vulnerability risk assessment - A case study, 76 district Bellary, Karnataka Arunima Dasgupta, K .L.N. Sastry, P.S. Dhinwa, S.K. Pathan and M.S. Nathawat

4. Monitoring of vegetation using multi-sensor temporal satellite data 85R.K. Chingkhei and Arun Kumar

5. Modeling of spatio-temporal dynamics of land use land cover – a review and assessment 93 M. Surabuddin Mondal, Nayan Sharma, Martin Kappas and P. K. Garg

6. Quantitative morphometric analysis of Bilrai watershed, Shivpuri district, Madhya Prades 104using remote sensing and GISD.D. Sinha , S.N. Mohapatra and Padmini Pani

7. Web-GIS based monitoring of vegetation using NDVI profiles 109 Shashikant A. Sharma and Shweta Mishra

8. Geo-spatial technology based landslide vulnerability assessment and zonation in Sikkim 113 Himalayas in India L.P. Sharma, Nilanchal Patel, M.K. Ghose and P. Debnath

9. Inventory and change detection of wetlands in Barak Valley, Northeast India: A remote 120 Sensing and GIS approach

Anwarul Alam Laskar and Parag Phukon 10. Chlorophyll variability in the Arabian Sea and Bay of Bengal during last decade (1997 – 2007) 127 M. Shah, N. Chaturvedi, Y. T. Jasrai and Ajai

11. Development of 3D rural geospatial database using high resolution satellite images, GIS, 132total station and GPS Y. Navatha, K. Venkata Reddy, Deva Pratap, D.C Prashanth Babu and A. Jayatheja

12. Development of Coastal Zone Information Systems (CZIS) using ARC objects 138 Aviral Kulshreshtha, H.B. Chauhan, Rajnikant J. Bhanderi and Anjana Vyas

Short Note:

13. Implications and risks of technology change in the geomatics curriculum 142 Obade Vincent de Paul and Ogenga Anyango Masela Reviewers for Journal of Geomatics, Volume 6 vAuthor Index,Volume 6 vi National Geomatics Awards vii Format for nomination for National Geomatics Awards viii Fellows and Patron Members ix Instructions for Authors x Journal of Geomatics: Advertisement Rates xii Indian Society of Geomatics xiii ISG Membership Form xiv

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Journal of Geomatics 65 Vol.6 No.2 October 2012

Environmental monitoring of vegetation cover at Kalpakkam through NDVI approach

M. Sankar Ram, C. Anandan and P. Sasidhar Safety Research Institute, Atomic Energy Regulatory Board, Kalpakkam - 603 102, India

Email: [email protected]

(Received: September 22, 2011; in final form June 01, 2012)

Abstract: This paper presents an environmental monitoring method for spatial and seasonal changes in vegetation cover for the post monsoon seasons (Jan/Feb) and pre monsoon seasons (Apr/May/Jun) using remote sensing based Normalised Difference Vegetation Index (NDVI). An attempt has been made to map the vegetation cover using the passively sensed satellite imageries Landsat 7 Enhanced Thematic Mapper+ (ETM+), for 5km around the Kalpakkam plant site for the period 2006 - 2010. The NDVI based vegetation cover was also analyzed and found that the highest percent vegetation cover was recorded in January 2008 (post monsoon season) and the lowest percent vegetation cover was recorded in June 2010 (pre monsoon season). A similar trend in variation of vegetation cover within and around the plant site for post and pre monsoon seasons suggested that the parameters responsible for sustenance of vegetation cover are identical. For post monsoon seasons, a tendency to linearity in correlation of NDVI based vegetation cover with rainfall was obtained for cumulative rainfall of preceding 90 days with a regression coefficient of 0.811. However for the pre monsoon seasons, no tendency in correlation was obtained for cumulative rainfall of preceding 90 days. The accuracy of the results was assessed by statistical methods and found a total accuracy of 90 percent with a Kappa coefficient of 0.79. The present study highlights the application of NDVI approach to monitor the seasonal change in vegetation cover and its relation to climatic factors like rainfall. Keywords: Seasonal change, Correlation with rainfall, Accuracy assessment, Kappa Coefficient 1. Introduction In order to ensure the environmental safety around industries, the environmental monitoring is essential to characterize and monitor the quality of the natural environment. Monitoring the vegetation cover area helps to assess the ecological impacts of industrial sites on the natural environment. The remote sensing satellites are providing the passively sensed images, which play a vital role in the monitoring of temporal changes in vegetation cover. The remote sensing techniques are capable of providing near-real-time indication of onset, extent, intensity and duration of vegetation content (Peters et al., 2002) which is a very significant development from environmental and industrial safety perspective. The combination of Remote Sensing and Geographical Information Systems (RSGIS) in recent years has proved the vital role in mapping and synthesis of this spatial data. The present study has made an attempt to evaluate the applicability of the Normalised Difference Vegetation Index (NDVI) in mapping of vegetation content (Schowengerdt, 1997; Lillesand and Kiefer, 2000; Panda, 2005). The aim of the study is to examine the seasonal variation of the green cover area in and around Kalpakkam plant site using RSGIS techniques. The present study attempts to monitor and assess the vegetation cover changes during pre monsoon seasons (Apr/May/Jun) and post monsoon seasons (Jan/Feb) for the period 2006 - 2010. The NDVI results were correlated with rainfall data for post and pre monsoon seasons of the years 2006 - 2010 and the statistical

accuracy of NDVI based vegetation cover was estimated using RSGIS techniques.

2. Study area and the data The study area includes a 5 km area around the Kalpakkam plant site (Figure 1) which lies between 80� 09' to 80� 13' E longitude and 12� 42' to 12� 29' N latitude. It is bounded by Bay of Bengal in the east and includes the nearby villages.

Figure 1: Study area The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite imageries have been acquired for the two seasons viz., (i) Post-monsoon (Jan/Feb), (ii) Pre-monsoon (Apr/May/Jun) for the years 2006 - 2010 from USGS Global Visualization Viewer (GloVis - http://glovis.usgs.gov). The grayscale bands viz., band 3 (0.63 - 0.69 μm) and band 4 (0.75 - 0. 9 μm) with the resolution of 30 m, were used in this study. The

©Indian Society of Geomatics _________________________________________________________________________________________________________


grayscale bands received from USGS site (http://glovis.usgs.gov) were already preprocessed through the Level-1 Product Generation System (LPGS) to provide radiometric and geometric accuracy to the data by incorporating ground control points and employing a Digital Elevation Model (DEM) for topographic accuracy (USGS, 2012). The detailed information about the data acquired is tabulated in Table 1.

Table 1: Information about Landsat data acquired

Image ID Date of satellite pass Season

L71142051_0512006021 Feb 15, 2006 Post monsoonL71142051_0512006052 May 22, 2006 Pre monsoonL71142051_0512007010 Jan 01, 2007 Post monsoonL71142051_0512007042 Apr 23, 2007 Pre monsoonL71142051_0512008012 Jan 20, 2008 Post monsoonL71142051_0512008051 May 11, 2008 Pre monsoonL71142051_0512009010 Jan 06, 2009 Post monsoonNo data available for Apr/May/Jun 2009 Pre monsoonL71142051_0512010012 Jan 25, 2010 Post monsoonL71142051_0512010060 Jun 02, 2010 Pre monsoon The landuse map for the present study was developed at RSGIS laboratory of Safety Research Institute (SRI), AERB, Kalpakkam using high resolution image (Quickbird data acquired on 25 April 2008). The rainfall data used in the study for the correlation purpose was collected from Environmental Survey Laboratory at the Kalpakkam site. 3. Approach to estimate vegetation cover using NDVI The NDVI and geoprocessing methods are employed for identifying the changes in vegetation cover for the 5 km area around the Kalpakkam plant site. The GIS software package ArcGIS 10 and ERDAS 9.1 were used in the study for carrying out the geoprocessing techniques. 3.1 NDVI The NDVI is a numerical measure of vegetation (amount of greenness), based on the difference in the reflectance of visible (Red wavelength) and near-infrared wavelengths sensed in the satellite image. NDVI calculations are estimating based on the reflectance in Near Infrared (NIR) Band 4 (0.75 to 0.9 μm) and reflectance in Red Band 3 (0.63 - 0.69 μm) for a given pixel and the NDVI is computed based on Eq-1. Mathematically, the NDVI is represented as

where, NIR - Reflectance in Near Infrared Band (Band 4)

Red - Reflectance in Red band (Band 3) +ve value of NDVI - Vegetation content -ve value of NDVI - Other than vegetation content The NDVI results for a given pixel always result in a number that ranges from minus one (-1) to plus one (+1). The positive values representing vegetation areas and the negative values indicating other than vegetation areas. The positive values close to +1 indicates higher vegetation content. This NDVI provides a meaningful metrics that describe the temporal changes in vegetation. 3.2 Geoprocessing The required data for the 5 km radius around Kalpakkam plant site, viz., Band 3 (Red) and Band 4 (Near infrared) were extracted from the grayscale bands of Landsat satellite data. ArcGIS spatial analyst tools were employed for NDVI computations. The DN values from Landsat satellite data were converted to reflectance (Firl and Carter, 2011). The NDVI evaluated using Eq-1 for each pixel and these pixels were reclassified as vegetation and other than vegetation categories. The NDVI results for plant site alone were derived to monitor the vegetation changes due to human interventions or other site activities if any. The NDVI based vegetation cover area was correlated with rainfall data and the regression fit was analyzed for both post monsoon and pre monsoon seasons statistically. Percent accuracy statistic using error matrix and Kappa coefficient were computed using remote sensing based accuracy assessment techniques for validation purposes. 4. Results and Discussion The NDVI results obtained using the geoprocessing techniques for identifying the changes in 5 km area around the Kalpakkam plant site are shown in Figure 2 for a typical year 2008. Based on the landuse map developed at SRI, the landuse categories viz., Agriculture land and Forest land which are generally having large vegetation content were overlaid above the NDVI values in Figure 2 for better understanding purposes. 4.1 NDVI Results The NDVI maps were generated (Figure 2) for post monsoon seasons (Jan/Feb) and pre monsoon seasons (Apr/May/Jun) for the years 2006 - 2010. The NDVI based vegetation cover for study area and plant site are tabulated in Table 2a and Table 2b respectively. The NDVI based vegetation cover was also analyzed and found that the highest vegetation cover was recorded in January 2008 and the lowest percent vegetation cover was recorded in June 2010. Jan 2008 experienced the highest cumulative rainfall of 1095mm for the preceding 90 days and Jun 2010 experienced the driest period of 113 days since 2005 (08 Jan 2010 to 01 May

NDVI = (NIR) - (Red)

(NIR) + (Red) Eq. (1)


2010). During the years 2008 and 2010, the pre monsoon vegetation cover was lesser than the post monsoon vegetation cover. This is attributed to low rainfall, climatic conditions etc. Whereas during the years 2006 and 2007, the pre monsoon vegetation cover was higher than the post monsoon vegetation cover. This anomaly was analyzed by the GIS based overlay analysis and inferred that the increase in the vegetation cover was attributed to farming activities where the water were drawn from the nearby water bodies. On the other hand, the increase in vegetation cover during pre monsoon seasons at plant site is attributed due to promotion of horticulture activities. It was observed from column (c) of Table 2a and Table 2b that the percent vegetation cover followed a similar trend for post and pre monsoon period during 2006 - 2010. This suggests that the parameters responsible for sustenance of vegetation cover within and around the plant site are identical.

(a) Jan 2008 (post monsoon season)

(b) May 2008 (pre monsoon season) Figure 2: NDVI based vegetation for 5 km area in and around the Kalpakkam plant site for a typical year 2008

4.2 Correlation of NDVI based vegetation cover with rainfall

Table 2a: NDVI based vegetation cover area for 5 km in and around Kalpakkam plant site

Season NDVI based

vegetation cover area (km2)

Percent vegetation cover with respect

to total area (a) (b) (c)

Feb-06 11.58 28.8 May-06 18.76 46.7 Jan-07 13.86 34.5 Apr-07 15.95 39.7 Jan-08 30.53 76.0

May-08 26.18 65.1 Jan-09 11.32 28.2

May-09 - - Jan-10 13.87 34.5 Jun-10 7.80 19.4

Table 2b: NDVI based vegetation cover area within

the Kalpakkam plant site

Season NDVI based

vegetation cover area (km2)

Percent vegetation cover with respect to

total area (a) (b) (c)

Feb-06 2.66 26.1 May-06 4.73 46.3 Jan-07 3.14 30.8 Apr-07 4.57 44.8 Jan-08 7.77 76.1

May-08 6.02 59.0 Jan-09 2.72 26.6

May-09 - - Jan-10 3.74 36.6 Jun-10 2.09 20.5

4.2.1 Correlation of NDVI with cumulative rainfall of preceding 60 days The NDVI based vegetation cover was correlated with cumulative rainfall of preceding 60 days, corresponding to the date of Landsat satellite data acquired (Table 1). This correlation of NDVI with rainfall for the post and pre monsoon seasons for 5 years is tabulated in Table 3. The correlation of NDVI classified vegetation area with rainfall for post monsoon seasons is presented in Figure 3. It was observed that the correlation between rainfall and vegetation cover did not show a satisfactory linearity for post monsoon seasons (Figure 3). The data are not adequate to derive conclusions. However, a linear relationship was reported by du Plessis (1999), Weiss et al. (2001) and Prince et al. (2007) and a non-linear relationship was reported by Hein et al. (2011). A random variation was observed for the pre monsoon


(dry) seasons between rainfall and vegetation cover (Table 3b). It was reported that in dry season, outliers have to be removed or averaged for understanding stronger underlying relationship (du Plessis, 1999).

Table 3a: Rainfall data vs NDVI based vegetation cover area for post monsoon seasons (Jan/Feb)

Month & Year Rainfall for

preceding 60 days (mm)

NDVI based vegetation

(km2)

Feb - 2006 88.7 11.58 Jan - 2007 312.8 13.86 Jan - 2008 551.6 30.53 Jan - 2009 450.5 11.32 Jan - 2010 177.0 13.87

Table 3b: Rainfall data vs NDVI based vegetation

cover area for pre monsoon seasons (Apr/May/Jun)

Month & Year

Rainfall for preceding 60 days (mm)


(km2)

May - 2006 8.0 18.76 Apr - 2007 12.8 15.95 May - 2008 112.9 26.18 May - 2009 - - Jun - 2010 115.0 7.80

Figure 3: Correlation between cumulative rainfall of 60 days and NDVI based vegetation cover for the post

monsoon seasons 4.2.2 Correlation of NDVI with cumulative rainfall of preceding 90 days

In order to understand the underlying relationship with rainfall, an additional attempt has been made to correlate the vegetation cover with the cumulative rainfall of preceding 90 days for the post and pre monsoon seasons and is tabulated in Table 4a and Table 4b respectively. The correlation of NDVI classified vegetation area with rainfall for post monsoon seasons is presented in Figure 4. Similarly, it can be seen that a satisfactory linear fit was not obtained for post monsoon seasons inspite of a regression coefficient of 0.811 (Figure 4). Similar observation was reported (Herrmann et al., 2005; Nicholson et al., 1990) for the 3 month period of

cumulative rainfall. As in the earlier case, no satisfactory correlation was obtained with rainfall for the pre monsoon seasons.

Table 4a: Rainfall data vs NDVI based vegetation cover area for post monsoon seasons (Jan/Feb)

Month & Year

Rainfall for preceding 90 days

(mm)


(km2)

Feb - 2006 541.1 11.58

Jan - 2007 784.4 13.86

Jan - 2008 1095.0 30.53

Jan - 2009 778.3 11.32

Jan - 2010 715.5 13.87

Table 4b: Rainfall data vs NDVI based vegetation cover area for pre monsoon seasons (Apr/May/Jun)

Month & Year Rainfall for

preceding 90 days (mm)


(km2) May - 2006 28.0 18.76 Apr - 2007 38.8 15.95 May - 2008 114.9 26.18 May - 2009 - - Jun - 2010 125.0 7.80

Figure 4: Correlation between cumulative rainfall of 90 days and NDVI based vegetation cover for the post

monsoon seasons The comparison of Figure 3 and Figure 4 reveals that a linear tendency in correlation between NDVI based vegetation and rainfall for preceding 60 and 90 days with respect to the date of satellite data acquired. We understand that the correlation between NDVI based vegetation and the duration of cumulative rainfall of preceding days/months cannot be generalized to 90 days/3 months. This period is a site specific feature which depends on climatic conditions, nature of soil, topography etc. 4.3 Statistical assessment of NDVI For remote sensing based studies, assessment of accuracy is essential to address the errors caused by preprocessing, image interpretation techniques


Eq. (3) N xii - ( x i+ x +i ) �

i = 1

r

� i = 1

r

N2 - ( x i+ x +i ) �i = 1

rk̂ =

followed, image processing software etc. An attempt is made in this paper to assess the results obtained from NDVI by two statistical approaches viz., accuracy assessment based on error matrix and Kappa statistic. The available high resolution imagery (Quickbird data) acquired on 25 April 2008 was used to validate the NDVI map derived for May 2008 (Figure 2). 4.3.1 Accuracy assessment based on error matrix Accuracy assessment techniques based on an error matrix is a traditional method to calculate accuracy of a thematic map (Story and Congalton, 1986). By applying this technique, a set of 50 locations were randomly generated using GIS based data management tools. These locations were overlaid on NDVI map for May 2008 (Figure 2) and high resolution imagery (Quickbird data). Each location is assigned a category under vegetation or other than vegetation type and this data are tabulated in Table 5. Among the 50 sample points, 31 fell on vegetation area and 19 fell on other than vegetation area as per NDVI map. Similarly 32 sample points fell on vegetation area and 18 fell on other than vegetation area as per Quickbird high resolution image. Based on the above tabulation, an error matrix was generated and is presented in Table 5. Table 5 provides the user and producer accuracy (Story and Congalton, 1986) of the data with respect to NDVI map and high resolution Quickbird imagery. The accuracy for vegetation and other than vegetation type with respect to NDVI map were 93.5 percent (29 out of 31 points) and 84.2 percent (16 out of 19 points) respectively, whereas the accuracy for vegetation and other than vegetation area with respect to high resolution imagery were 90.6 percent (29 out of 32 points) and 88.9 percent (16 out of 18 points) respectively. The above statistical data revealed that NDVI based assessment is in good agreement with data obtained from satellite imagery. The total accuracy is determined by Eq-2 using diagonal in error matrix comprising of 29, 16 and 50.

4.3.2 Kappa or k̂ statistic The kappa coefficient or k̂ statistic is a measure of the difference between the actual agreement and the agreement expected by chance (Lillesand and Kiefer, 2000; Congalton, 1991). It is very useful representation of performance compared to percent accuracy statistic (Cohen, 1960) and which is also a commonly used discrepancy metric to quantify the agreement of

thematic maps (Lhermitte et al., 2008). The k̂ statistic is represented mathematically in Eq-3. where, r = No. of rows in the error matrix xii = No. of observations in row i and column i x i+ = Total no. of observations in row i x +i = Total no. of observations in column i

N = Total no. of observations included in

matrix

Based on the error matrix data from Table 5, the k̂ statistic is evaluated using Eq. 3.

The predictive performance using statistical methods provide useful indicators regarding reliability of NDVI approach. It is suggested (Lillesand and Kiefer, 2000) to compute both total accuracy based on error matrix and Kappa statistic for statistical assessment of accuracy. It is reported (Ko et al., 2011) that k̂ value > 0.4 and total accuracy close to 1 provide reliable predictive results. Table 5: The error matrix

In this study, we obtained a k̂ value of 0.79 and total accuracy of 0.9 indicates the suitability of applying NDVI for estimating seasonal changes in vegetation cover.

Type

Quickbird Image User accuracy

w.r.t. NDVI

map (%) Vegetation

Other than

vegetation Total

NDVI thematic

map

Vegetation 29 2 31 93.5

Other than vegetation 3 16 19 84.2

Total 32 18 50

Producer accuracy w.r.t. Quickbird

image (%) 90.6 88.9 90

k̂ =

( 50 x 45 ) ( 31 x 32 ) + ( 19 x 18 ) )

( 502) - ( ( 31 x 32 ) + ( 19 x 18 ) ) = 0.79

Accuracy Total

=

29 + 16�

50 = = 0.9 or 90%

Eq. (2) (vegetation + other than vegetation)�

True points

Total number of points

-

-


5. Salient conclusions The present study highlights the role of RSGIS in mapping and synthesis of this spatial data for effective environmental monitoring of vegetation cover. Our results showed that the remote sensing based NDVI was very useful in the assessment of seasonal changes in vegetation cover during post monsoon and pre monsoon seasons for the period 2006 - 2010. � Only a tendency to linear correlation of NDVI

based vegetation area with rainfall (preceding 90 days) was obtained for post monsoon seasons.

� The NDVI based vegetation cover obtained was assessed statistically and found that the total accuracy is 90 percent and Kappa coefficient is 0.79.

Thus the present study demonstrates the usefulness of NDVI and application of NDVI as a tool to monitor the seasonal change in vegetation cover with respect to rainfall. Acknowledgement

One of the authors, M. Sankar Ram gratefully acknowledges the research fellowship extended by Atomic Energy Regulatory Board (AERB), Government of India to pursue the present study. The authors thank Dr. B.P.C. Rao, Head, EMS&I Section of Indira Gandhi Centre for Atomic Research, Kalpakkam, India for his valuable comments and suggestions in this study.

References Cohen, J. (1960). A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20 (1), pp. 37-46. Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data, Remote Sensing of Environment, 37, pp. 35-46. Du Plessis, W.P. (1999). Linear regression relationships between NDVI, vegetation and rainfall in Etosha National Park, Namibia, Journal of Arid Environments, 42, 235-260. Firl, G.J. and L. Carter (2011). Calculating Vegetation Indices from Landsat 5 TM and Landsat 7 ETM+ Data. http://ibis.colostate.edu/WebContent/WS/ColoradoView/TutorialsDownloads/CO_RS_Tutorial10.pdf. Hein, L., N. de Rider, P. Hiernaux, R. Leemans, A. de Wit and M. Schaepman (2011). Desertification in the Sahel: Towards better accounting for ecosystem dynamics in the interpretation of remote sensing images, Journal of Arid Environments, 75, 1164-1172.

Herrmann., S.M., A. Anyamba and C.J. Tucker (2005). Recent trends in vegetation dynamics in the African Sahel and their relationship to climate, Global Environmental Change, 15, pp 394-404. Ko., C.Y., T.L. Root and P.F. Lee (2011). Movement distances enhance validity of predictive models, Ecological Modelling, 222, pp. 947-954. Lhermitte, S., J. Verbesselt, I. Jonckheere, K. Nackaerts, J.A.N.V. Aardt, W.W. Verstraeten and P., P. Coppin (2008). Hierarchical image segmentation based on similarity of NDVI time series. Remote Sensing of Environment, 112, pp. 506-521. Lillesand, T.M. and R.W. Kiefer (2000). Remote sensing and image interpretation, John Wiley & Sons, New-York. 724pp. Nicholson, S.E., M.L. Davenport and A.R. Malo (1990). A comparison of the vegetation response to rainfall in the Sahel and East Africa, using Normalized Difference Vegetation Index from NOAA AVHRR. Climatic Change, 17, 209-241. Panda, B.C. (2005). Remote sensing principles and applications, Viva books private limited, New Delhi. 288pp. Peters, A.J., E.A. Walter-Shea, L. Ji, A. Vina, M. Hayes and M.D. Svoboda (2002). Drought monitoring with NDVI-based standardized vegetation index, Photogrammetric Engineering and Remote Sensing, 68, pp. 71-75. Prince, S.D., K.J. Wessels, C.J. Tucker and S.E. Nicholson (2007). Desertification in the Sahel: a reinterpretation of a reinterpretation, Global Change Biology, 13, pp. 1308-1313. Schowengerdt, R.A. (1997). Remote Sensing Models and Methods for Image Processing (second ed), Academic Press, San Diego, CA. 522pp. Story, M. and R.G. Congalton (1986). Accuracy assessment: A user's perspective, Photogrammetric Engineering and Remote Sensing, 52, pp. 397-399. USGS, (2012). Landsat Processing Details. http://landsat.usgs.gov/Landsat_Processing_Details.php. Weiss, E., S.E. Marsh and E.S. Pfirman (2001). Application of NOAA-AVHRR NDVI time-series data to assess changes in Saudi Arabia’s rangelands, International Journal of Remote Sensing, 22, 1005–1027.


Klobuchar model for ionospheric delay correction in Saudi Arabia

Ashraf Farah College of Engineering, King Saud University, Kingdom of Saudi Arabia


(Received: July 15, 2011; in final form August 27, 2012) Abstract: The ionospheric delay is the major source of potential range delay for single-frequency GNSS users (Kunches and Klobuchar, 2001). Single-frequency GNSS users are in critical need of an ionospheric model to eliminate the ionospheric delay to a high degree of accuracy. GPS system uses the Klobuchar model for this task, its coefficients are sent through the GPS navigation message to GPS users. Klobuchar model uses the Ionospheric Corrections Algorithm (ICA) (Klobuchar, 1987) designed to account for approximately 50% (rms) of the ionospheric range delay. Center for Orbit Determination in Europe (CODE) is currently generating ionospheric coefficients compatible with the Klobuchar model by making use of global TEC map information. Global TEC maps are processed routinely by the CODE analysis center and they are available in Ionosphere map Exchange (IONEX) format (Schaer et al., 1998). A comparison study between the behaviour of the Klobuchar model using the GPS broadcast coefficients and the same model using CODE-coefficients has been presented in this paper. The zenith range delay correction by the two models has been assessed using the highly accurate IGS-Global Ionospheric Maps for Riyadh station situated in Riyadh capital of Saudi Arabia. The study was carried out over three different months that each of them reflects a different state of solar activity, which is a major indication for the ionospheric activity. It can be concluded that for middle-latitude geographic region, the behaviour of Klobuchar-CODE model is better than the Klobuchar-GPS model as it reflects the day-to-day variation of the ionospheric delay and provides similar or better accuracy than the Klobuchar-GPS model for different states of ionospheric activity. Keywords: Ionosphere, Klobuchar, GPS, CODE, Middle latitude 1. Introduction Global Navigation Satellite Systems (GNSS) users face many error sources that affect the quality of GNSS operations. These errors have different sources namely; satellite dependent errors (satellite orbital error, satellite clock error and relativistic effects), receiver dependent errors (receiver clock error and antenna phase centre variations) and signal path dependent errors (ionospheric errors, tropospheric errors, cycle slips and multipath). The ionospheric error is the major source of error faced by single-frequency GNSS users. However use of double–frequency GNSS measurements could eliminate the ionospheric error to a high degree of accuracy. The urgent need to eliminate the ionospheric error by single-frequency GNSS users creates the necessity of different models used for this purpose. GPS, the American GNSS system uses the Klobuchar model (Klobuchar, 1982) to eliminate the ionospheric error to a certain degree of accuracy. Center for Orbit Determination in Europe (CODE) is currently generating ionospheric coefficients compatible with the Klobuchar model by making use of global Total Electron Content (TEC) map information. Global TEC maps are processed routinely by the CODE analysis center and they are available in IONosphere map Exchange (IONEX) format (Schaer et al., 1998). This paper presents a comparison study between the behaviour of the Klobuchar model using the GPS broadcast coefficients and the same model using

CODE-coefficients with respect to the highly accurate IGS-Global Ionospheric Maps (GIM’s) for one station over a period of three months. The zenith range delay correction offered by both models was assessed using the range delay extracted using the IGS-GIM’s. The study involved one station (Riyadh) which is situated in middle latitude region, and is having different ionospheric activity states with respect to the solar activity (quiet, medium and active ionospheric activity states). IGS- based Global Ionospheric Maps (GIM’s) are describing the Earth’s vertically integrated TEC and produced at the CODE and other IGS centers. IGS GIM’s are derived from, geometry-free linear combination of phase leveled to code measurements of about 200 globally distributed IGS ground stations. These global maps are represented by a spherical harmonic expansion of degree 12 and order 8 referenced to a solar-geomagnetic frame. Since June, 1998 the Ionosphere maps are given in an earth fixed reference frame with a resolution of 5o, 2.5o in longitude and latitude respectively. Total accuracy of GIM’s/CODE data was evaluated around 3.7 – 3.9 Total Electron Content Unit (TECU) compared with Very Long Baseline Interferometry (VLBI) data (Sekido et al., 2003). For more informations about IGS-GIM’S, the reader is advised with (Schaer, 1999). The paper starts with a short description of the Klobuchar model using two different sets of coefficients. The analysis over test site is presented in subsequent section. This is followed by discussion and finally the main conclusions are drawn.



2. Klobuchar-GPS Model The Klobuchar model (Klobuchar, 1982), was designed based on the Bent model (Llewellyn and Bent, 1973). The model is built on a simple cosine representation of the ionospheric delay, with a fixed phase-zero at 14.00 hours local time and a constant night time offset of 5 nanoseconds. The period and amplitude of the ionospheric delay are represented as third degree polynomials in local time and geomagnetic latitude. The eight time-varying coefficients of the two polynomials are broadcast in the GPS navigation message, and are updated daily. These coefficients are selected from 370 possible sets of constants by the GPS master control station and placed in the satellite upload message for downlink to the user. These coefficients are based on two parameters, day of the year and average solar 10.7-cm flux value (the solar flux density at 10.7cm wavelength) for the previous five days. The model assumes an ideal smooth behaviour of the ionosphere, therefore any significant fluctuations from day to day will not modelled properly. The accuracy of the model is limited to 50-60% of the total effect (Dodson, 1988). Under special circumstances, such as severe ionosphere activity at low elevations, the range error can be of order of 50 m (Newby et al., 1990). This model has one main advantage, which is its simplicity and the low computation time but it also has many shortcomings: - Low accuracy for computing the ionospheric delay

correction (50-60%) (Dodson, 1988)

- The algorithm does not properly represent the behaviour of the ionosphere in the near-equatorial region of the world, where the highest values of the ionospheric delay occurred (Klobuchar, 1982).

- The algorithm is very poor in high latitude regions where the ionospheric variability is high due to auroral processes.

- The model is unable to represent the behaviour of the ionosphere when the ionosphere differs by substantial amounts from its average behaviour.

A summary of the Klobuchar model algorithm equations is as follows; Given : user approximate location � u ,� u , elevation

( E ) and Azimuth ( A ) to the GPS satellite for which you wish to calculate the ionospheric time delay. Also given the coefficients � n , � n transmitted as part of

the satellite message. Note : All angles are in units of semi-circles; time is in seconds

1. Calculate earth centered angle

022.011.0

0137.0�

E� (1)

2. Compute sub-ionospheric latitude A

uIcos�� , if � u

< 0.416

416.0� I , if � u

> 0.416 (2)

3. Compute Sub-ionospheric longitude

��

I

uI

Acos

sin (3)

4. Find geomagnetic latitude � 617.1cos064.0 � �� IIm

(4)

5. Find local time (sec)32.4 4 GPStimex10 t I � if t > 86400 , use t = t – 86400 (5) 6. Compute slant factor � 353.00.160.1 EF � (6) 7. Compute ionospheric time delay

, x < 1.57 (7)

10 95 �� FT iono , x � 1.57 (8)

where : (9) Note : T iono is referred to the L1 frequency. If the user is operating on the L2 frequency, the correction term must be multiplied by the constant 1.65. 3. Klobuchar-CODE Model CODE, the Center for Orbit Determination in Europe, acts as one of five so-called Ionosphere Associated Analysis Centers of the International GNSS Service (IGS), currently generating ionospheric coefficients compatible with the Klobuchar model and the related algorithm as declared by the GPS ICD (Rockwell International Corporation, 1993). This method takes advantage of global TEC map information in IONosphere map EXchange (IONEX) format (Schaer et al., 1998) that is derived by the CODE analysis center. A working study using this model has shown that these new CODE coefficients increase the

�

�� n

mn

n

tx�

� 3

0

50400

��

�

��

�

��

�

�

��

�

��

�

��

��

�

24215

423

0

910 xxTn

mn

niono F ��


accuracy of the Klobuchar model from 50% (with GPS-coefficients) to 75% of the total effect (Schaer, 2001). 4. Behaviour Test Study The study’s objective is to compare the behaviour of the Klobuchar model using GPS-coefficients (IGS, 2012) and CODE-coefficients (CODE, 2012) with respect to the IGS-GIM’s under different ionospheric-activity circumstances. The test station was Riyadh station situated in the capital of Saudi Arabia which represents a middle latitude geographic region station (Table 1). The study compared the zenith range delay corrections offered by the two models with respect to the IGS-GIM’s for (GPS-L1 frequency) (1575.42 MHz) over three different months (Table 2) each of these months reflects a different state of solar activity based on (AP) monthly mean index (index used to determine the level of geomagnetic activity) and maximum-Sun Spot Number (SSN) which are major indicators of ionospheric activity states (quiet, medium and active ionospheric activity states). Table 1: the geographical position of the tested station

Table 2: the dates and activity states of the tested periods

Activity state Quiet Medium Active Month February

1998 April 1999

September 2001

Max. Sun Spot no. (SIDC, 2012).

76 104 200

AP monthly mean index (NGDC, 2012)

8 12 13

The findings of the study for Riyadh station, which represents middle latitude region, are shown in Figures 1, 2 and 3. These figures show the zenith range delay correction offered by the Klobuchar-GPS model, Klobuchar-CODE model and the highly accurate IGS-GIM’s. The range delay differences between both models and the IGS-GIM’s are shown in Figures 4, 5 and 6. The statistical analysis for the L1 range delay difference for the tested station for different ionospheric activity states (tested time periods) are shown in Table 3. where the maximum, minimum, mean and root man square (rms) values are shown for each tested month.

Figure 1: L1 Range delay correction (Riyadh station) (Feb, 1998)

Figure 2: L1 Range delay correction (Riyadh station) (April, 1999)

Figure 3: L1 Range delay correction (Riyadh station) (September, 2001)

Figure 4: L1 Range delay correction difference (Riyadh station) (Feb, 1998)

Figure 5: L1 Range delay correction difference (Riyadh station) (April, 1999)

Figure 6: L1 Range delay correction difference (Riyadh station) (September, 2001)

Station ID

Latitude degree

Longitude degree

Height meters

Country

Riyadh 24o 31\ N 46o 46\ E 661.00 Saudi Arabia

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Time (days)

Del

ay (m

)

IGS-Klobuchar-GPS modelIGS- Klobuchar-CODE model

0

1

2

3

4

5

6

7

8

9

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Time (days)

Del

ay (m

)

IGS-GIM'SKlobuchar-GPS modelKlobuchar-CODE model

0123456789

1011121314151617181920

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Time (days)

Del

ay (m

)


0123456789

1011121314151617181920

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Time (days)

Del

ay (m

)


-10-9-8-7-6-5-4-3-2-10123456789

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Time (days)

Del

ay (m

)


-10-9-8-7-6-5-4-3-2-10123456789

10

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Time (days)

Del

ay (m

)



Table 3: The statistical analysis for the zenith L1 range delay difference for tested Riyadh station

Klobuchar-GPS Model Klobuchar-CODE Model

Max. Min. mean rms Max. Min. mean rms

February 1998

Quiet 0.90

-1.72

-0.64

0.69 -0.74

-2.29

-1.50

0.40

April 1999

Medium 5.89

1.49

3.27

1.02 1.86

-3.00

-0.20

1.20

September 2001

Active 7.44

1.65

4.78

1.29 7.10

2.20

3.70

1.04

5. Discussion It can be concluded from Figures 1, 2 and 3 that for middle-latitude geographic region, the Klobuchar-CODE model is offering better performance than the Klobuchar-GPS model as it provides range corrections more closely to the IGS-GIM’s corrections. Klobuchar-CODE model is able to show day-to-day variations in the zenith range delay corrections due to its dependence on day-to-day varying coefficients which depends on daily TEC CODE-maps while Klobuchar-GPS model is unable to show day-to-day variations as the ionospheric coefficients sent in the GPS navigation message is not updated on daily bases. It can be seen that the Klobuchar-GPS model-ionospheric coefficients sent in the GPS navigation message were updated four times during February 1998 (quiet ionospheric state) (Figure 1), eight times during April 1999 (medium ionospheric state) and fourteen times during September 2001 (active ionospheric state). It can be concluded from Table 3 that, the behaviour of Klobuchar-CODE model is similar to the Klobuchar-GPS model for quiet ionospheirc activity state. For medium ionospheric activity state, the behaviour of Klobuchar-CODE model is much better than the Klobuchar-GPS model with an average deviation of only 0.20 m comparing to 3.27 m as an average deviation for Klobuchar-GPS model.. Also for the active ionospheric state, the behaviour of Klobuchar-CODE model is much better than the Klobuchar-GPS model with an average deviation of only 3.70 m comparing to 4.78 m as an average deviation for Klobuchar-GPS model. Generally, Klobuchar-CODE model offers better behaviour in correcting range delay comparing with Klobuchar-GPS model in middle-latitude geographic regions under any activity state for the ionosphere whether the ionosphere is in active state, medium state or quiet state. Klobuchar-CODE model offers more realistic behaviour for the range delay caused by the ionosphere as the range corrections are varying on daily basis as the CODE-coefficients vary from day to day due to its dependence on the CODE Global Ionospheric Maps (GIM’s). CODE-GIM’s offers daily global TEC values based on double-difference carrier-phase observations collected from globally distributed IGS stations. While the Klobuchar-GPS model is not

offering this behaviour due to the limitation in the GPS navigation message-ionospheric coefficients updating. 6. Conclusions Klobuchar-CODE model offers better behaviour in correcting zenith range delay comparing with Klobuchar-GPS model, the used GPS model for single-frequency ionospheric operations in middle-latitude geographic regions. Klobuchar-CODE model is able to show day-to-day variations in the range delay corrections due to its dependence on daily CODE-GIM’s while Klobuchar-GPS model is unable to show day-to-day variations due to the limitations in the GPS navigation message- ionospheric coefficients updating. The improvement of Klobuchar-CODE model over Klobuchar-GPS model for middle-latitude regions is about 48% in medium ionospheric activity state while the improvement is about 12% in active ionospheric activity state. Using Klobuchar-CODE model for the correction of ionospheric-range delay will offer high accuracy compared with the use of Klobuchar-GPS model. Klobuchar-CODE model is suitable for real-time operations with the advantage of more realistic and accurate solutions over Klobuchar-GPS model. References CODE (2012). The web site for CODE ionospheric coefficients compatible with Klobuchar model. Centre for Orbit Determination in Europe, CODE. ftp://ftp.unibe.ch/aiub/CODE/2001/CGIM2730.01N Dodson, A. H. (1988). The Effects of Atmospheric Refraction on GPS Measurements. Seminar on the Global Positioning System, Nottingham University. United Kingdom. IGS (2012). The web site for GPS ionospheric coefficients sent in the GPS navigation message compatible with Klobuchar model. ftp://cddis.gsfc.nasa.gov/gps/data/daily/2001/brdc/ Klobuchar, J. A. (1982). Ionospheric Corrections for the Single Frequency User of the Global Positioning System. National Telesystems Conference, NTC’82. Systems for the Eighties. Galveston, Texas, USA (New York: IEEE, 1982). Klobuchar, J. A. (1987). Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users. IEEE Transactions on Aerospace ad Electronic Systems. Vol. AES-23, No. 3, pp. 325-331. Kunches, J. M. and J. A. Klobuchar (2001). Eye on The Ionosphere: GPS after SA. GPS Solutions 4(3), PP. 52-54.


Llewellyn, S. K. and R. B. Bent (1973). Documentation and Description of the Bent Ionospheric Model. IAFCRL-TR-73-0657, July 1973, AD772733. Newby, S. P., R. B. Langely and H. W. Janes (1990). Ionospheric Modelling for Single Frequency Users of the Global Positioning System: A Status Report. In Proceeding of the 2nd International Symposium on Precise Positioning with GPS. Ottawa, Canada. NGDC (2012). The web site for AP monthly mean index from the National Geophysical Data Centre, NGDC, National Oceanic and Atmospheric Administration, NOAA, USA. ftp://ftp.ngdc.noaa.gov/STP/GEOMAGNETIC_DATA/INDICES/KP_AP/MONTHLY.DAT Rockwell International Corporation (1993). Interface Control Document (ICD-GPS200): NAVSTAR GPS Space Segment /Navigation User Interfaces, October 10, 1993. Schaer, S. W. Gurtner and J. Feltens (1998). IONEX: The IONosphere Map EXchange Format Version 1.

Proceeding of the IGS AC Workshop, Darmstadt, Germany, February 9-11. Schaer, S. (1999). Mapping and predicting the Earth_s ionosphere using the Global Positioning System, Ph.D. thesis, Bern University, Bern, 1999. Schaer, S. (2001). Generating Klobuchar-Style Ionospheric Coefficients for Single-Frequency Real-Time and Post-Processing Users. Astronomical Institute, University of Berne, Switzerland. August 21, 2001. Sekido, M., T. Kondo and E. Kawai (2003). Evaluation of GPS-based ionospheric TEC map by comparing with VLBI data. RADIO SCIENCE, VOL. 38, NO. 4, 1069, 2003. SIDC (2012). The Solar Influences Data Analysis Center (SIDC), which is the solar physics research department of the Royal Observatory of Belgium. The Source of Sun Spot Numbers observations. http://sidc.oma.be/sunspot-data/.

Journal of Geomatics 76 Vol.6 No.2 October 2012 �

Fuzzy logic model for desertification vulnerability risk assessment – A case study, district Bellary, Karnataka

Arunima Dasgupta1, K L N Sastry1, P S Dhinwa1, S K Pathan1 and M.S.Nathawat2 1Space Applications Centre, ISRO, Ahmedabad, India

2Indira Gandhi National Open University, New Delhi, India Email: [email protected]

(Received: October 21, 2010; in final form June 12, 2012)

Abstract: It is important to estimate the risk of desertification in order to take proper measures for its prevention. The paper intends to identify the areas under the risk of desertification, through an integrated geo-statistical model with fuzzy classification system for natural parameters and cumulative weighted method for socio-economic components. Assuming the property of distribution of natural variables as Gaussian, normal probability density function was used to derive the membership of a particular variable into suitable class. The vulnerable areas were recognized from the infinite distribution tails of each normal curve. Socio economic risks were obtained using cumulative weighted method (Sastry, 2010). The risk areas in terms of individual parameters were then combined to locate the areas under desertification risk using geo-statistical multi-criteria base analysis. Various anthropogenic pressures are accelerating land deterioration, coupled with natural erosive forces. The study examines four major sources of land degradation in Bellary, namely water erosion, degradation due to salinity and alkalinity, deforestation due to illegal mining within the forest, and growing urbanization. Key words: Desertification risk, Fuzzy membership function analysis. 1. Introduction Desertification is “Land degradation in arid, semiarid and dry sub-humid areas resulting from various factors including climatic variations and human activities”, where “Land” is defined as terrestrial bio-productive system and “land degradation” is defined as reduction or loss in biological and economic productivity (UNCCD , 1994). Very recently, LADA has further developed this definition as “the reduction in the capacity of the land to provide ecosystem goods and services, over a period of time, for its beneficiaries”. “Ecosystem goods” are products of land, which have an economic and/or social value, including land availability, animal and plant production, soil health and water quantity and quality. “Ecosystem services” include biodiversity and the maintenance of hydrological, nutrient and carbon cycles (LADA, 2011). In particular, dryland ecosystems, which are the areas having aridity index less than 0.65 (UNEP, 1997), are more prone to desertification. Drylands cover around one third of the world's land area that are extremely vulnerable to over-exploitation and inappropriate land use. Drylands are home to more than 38% of the total global population (MEA , 2005, Reynolds et al., 2007, UNCCD, 2008). Most likely, the true level of degradation in drylands lies somewhere between the 10% (MEA, 2005) and 20% (GLASOD ,1990), and over 250 million people are directly affected by land degradation (Reynolds et al., 2007, UNCCD, 2008). The conventional way to classify parameters for environmental degradation or land degradation is to define classes with certain lower and upper limit values and assign the parameter values to suitable classes. For socio economic classification this system

may suit as most of them are discrete values derived for certain administrative boundaries. But natural parameters are continuous and there remain some representative values in transitional areas between two classes. If observed in GIS environment, a considerable number of polygons can be found out which do not exactly represent a particular class of value, but a transitional zone. These are the most important areas in terms of degradation, as they have the lowest probability to be in a certain class, hence highest probability to be extended or narrowed down in next or previous class respectively. Corrective methods should be applied to these vulnerable areas to make them less vulnerable. Thus, to embrace the variability of natural parameters, fuzzy membership approach is a viable option for risk categorization (Sasikala et al., 2001). The present research was carried out with an objective to identify the areas under the risk of desertification along with their severity. The study specifically focused on fuzzy membership function analysis for natural parameters to find out the areas under risk in terms of natural parameters. Cumulative weighted method was used to derive the risk in for socio-economic parameters. Final desertification risk was obtained by integrating the risk of natural as well as socio-economic parameters using multicriteria based analysis in GIS environment. Considering degradation agent in Bellary alarming vegetal degradation is reported due to indiscriminate cut-down tree for mining within the forest and spreading of agricultural land as well as settlement, over the time, particularly in the middle part of the district stretching from Hospet in west to Bellary in east. Salinity and alkalinity is reported in the areas prevailed by Tugabhadra canal network. Slight to moderate water erosion and sand

_________________________________________________________________________________________________________ ©Indian Society of Geomatics

Journal of Geomatics 77 Vol.6 No.2 October 2012 �mining activity are also noticeable in certain places. SOI toposheets, LISS III images of 2006-07, Census data 2001 and other collateral data were used for the study.

2. Study area Bellary district, Karnataka, India has been selected as study area. Bellary district is spread from south-west to north–east and is situated on the eastern side of Karnataka state. The district is situated between 14° 30' and 15°50' north latitude and 75°40' and 77°11' east longitude. 3. Methodology Natural factors accountable for desertification are broadly of four types, namely climatic factor, terrain characteristics, soil type, and vegetation. The present study included specifically, slope, soil pH, soil depth and NDVI for terrain, soil and vegetation analysis. First, the parameters were classified into three risk classes, according to their deviation from mean. The risk severity was derived for each class of individual parameter using membership analysis. Then the areas under different risk severity were located, combining the all the natural parameters’ risks using multicriteria based geostatistics. The overall methodology of the study is described in the flowchart below (Fig. 1).

Figure 1: Methodology Flowchart On the whole, the methodology involves three steps. First the parameters were classified according to their deviation from mean. The total range of values for each individual parameter were classified into three risk classes namely “Low”, “Moderate and “High”. Eventually the classes are: LOW: � - � < x <=� +�MODERATE: � + � < x <=� +2� OR

� - 2� < x<=� -�HIGH: x>� +2� OR x<=� -2� The parameters, which are inversely related to desertification, such as soil depth and NDVI, were

classified following the descending order of values. In the contrary, the slope values, being positively related to the risk, were classified following the ascending order. On the other hand, both the lower and higher values of soil pH are unfavorable for cultivation indicating low productivity. pH values are classified into three classes following the ascending order. Then both the class Low and High are classified in High risk and the values near neutral range are classified in Low risk class. Second the risks of each class of individual parameter were assessed from membership analysis of normal density function. The distribution of each class of a single parameter was assumed to be Gaussian with � mean and � standard deviation. Normal probability density function was used to obtain the membership of individual variable to be in a particular class, following the statistical formula as follows:

� � 2 2/21, ,

2xf x e � ��

� ��

Figure 2: Classes of Normal distribution

where x is the value or an individual member of a class of chosen parameter, � is the arithmetic mean and � is the standard deviation of the class. Choice of normal probability density function implies that there is infinite distribution tail, represented typically by the values beyond �+2� and �-2� (Fig 2). These are not representing a certain class, but a transitional zone between two successive classes, indicating the most important areas in terms of degradation, as they have the lowest probability to be in certain class, hence highest probability to be extended or narrowed down in next or previous class respectively. Thus, both the areas of high and low risk could be categorized.

A detailed explanation implied that, for each of the class of a single parameter, there are some values more than �+2� and less than �-2�, not strongly representing that particular class but a transitional zone between two successive classes. If we consider a threshold ranging from �-2� of previous class to �+2� of the next class, the values falling in this threshold are considered to be in high risk. These values are referring a transitional zone, hence are susceptible to be altered easily to a higher value, if unchecked. In other words, these are highly vulnerable areas for degradation. On the other hand, the values in between �-� and �+� are considered in a stable condition and

Journal of Geomatics 78 Vol.6 No.2 October 2012 �not to be altered easily, as they strongly represent their respective class, hence in no risk. The values in between �-2� to �-� and �+� to �+2� are on moderate risk. The areas that are under high risk to be degraded more could be checked. Also the vulnerable areas whose degradation can be narrowed down easily could be spotted and proper corrective methods could be planned and implemented. In case of socio-economic parameters, Cumulative weighted method was used. Thus is a method to give ranks to individual considered parameter and then integrate them to get a cumulative value according to the accountability towards the main cause, which is desertification in this case (Sastry, 2010). Socio economic parameters are discrete values with respect to each administrative unit, which is a village here. The formula to obtain combined socio economic Index is:

SCI EI FI where SCI is Socio Economic Index, EI is Economic Development Index and FI is Facility Development Index. Two different cumulative indices were calculated, namely Economic development Index (EI) and Facility development Index (FI), to derive the risk of degradation for each village. For EI, the parameters which are considered are Population density (PD), proportion of Illiterates (PIL), Non-workers (PNW), SC/ST population (PSC, PST) and Marginal workers (PM) respectively. All the parameters are positively related to desertification. The formula to obtain the EI is :

EI =

,PD PIL PNW PSCST PM� The proportion of each parameter is derived using the following formula:

in10i i

i i

ix

x M xy

Maxx Minx

� �

�

where, is index of the ith development indicator in a settlement (viz. PD, PIL, PNW etc.) and is the size or value of the ith development indicator of the settlement. For FI, the measured parameters are Education facilities, Medical facilities, Transport facilities, Communication facilities, Drinking water facilities and Irrigation facilities. To obtain the FI, the formula is;

1

mccFI I�

where c ranges from 1 to m, FI= Cumulative Index for a particular settlement vis-à-vis all facilities, m is Number of facilities categories (medical, educational, transportation, communication etc.) and Ic = Category Index.

� 1 /c i iI A W W � � � where i ranges from 1 to n, n is number of categories in a facility, c is Index for a particular settlement vis-à-vis facility class, Ai is 0 or 1 (0 = facility not available, 1 is facility available).

Wi = Weight of the facility within a category and is defined as;

*100ii

N fW

N

��

i

where N = Total number of settlements. f = Number of settlements having a particular facility i. Once the risk indices of variables are evaluated, the risk of desertification would be obtained using geospatial analysis techniques. The values obtained from each parameter will be combined in GIS field using geospatial operations and then normalized in a range from 1 to 10. Finally the total range will be classified according to their deviation from mean to locate final severity risk classes. Three classes will be generated. The way to derive overall desertification risk categories in terms of natural parameters is given below;

1 / 4� NPRI=(AI U TI U SI U VI)

= U

= U

where NPRI is Natural Parameter Risk index AI is Aridity value, TI is slope values, SI is Soil values, VI is NDVI values. Similarly, the formula to obtain combined socio economic Index is;

� 1 / 2SCI EI FI �

where SCI is Socio Economic Index, EI is Economic Development Index and FI is Facility Development Index. To locate the area under overall desertification risk is;

� 1 / 2DVI NPI SCI � It is important to incorporate both the natural and socio economic parameters while assessing desertification vulnerability both in individual and in combination. To embrace the actual characteristics and their accountability of each individual variable of two different types, two different methods were adopted. Lastly an integrated framework that is GIS was used to analyze them in combination to get the overall vulnerability with risk categories. 4. Observations and analysis

The analysis was carried out for four natural parameters namely slope, soil depth, soil pH and NDVI following the method mentioned before.

iy

I

Journal of Geomatics 79 Vol.6 No.2 October 2012 �Slope Slope is positively related to land degradation. Soil erosion by water increases as the slope length increases due to the greater accumulation of runoff. Consolidation of small fields into larger ones often results in longer slope lengths with increased erosion potential, due to increased velocity of water which permits a greater degree of scouring (carrying capacity for sediment). Slope was derived from the spot height values, taken from SOI toposheet, using IDW method (Yang & Hodler, 2000). The spot height values ranges between 350 meters to 1020 meters approximately giving a range of percent slope from about 1.1 to 30.1 with a mean value of 18.3 and standard deviation 2.7. While classifying the slope values, it was found that there is only one variable, 1.1 in the class Low, i.e. less than �-� and only two variables, 25.1 and 30.1 in the class High, i.e. more than �+�. Thus, due to lack of data normal density curve of individual classes of slope can not be drawn. The normal density curve of the total range of slope was prepared for the analysis (Fig 3), which shows that the areas with slope values 25.1 and 30.1 percent are having a tendency to increase towards higher value, and are in high risk.

Figure 3: Normal density function of Slope

Depth The soil having high depth shows differentiation between distinct horizontal bands, known as soil horizon, indicating a well develop mature soil. In Bellary, the depth ranges between 7.5 to 38 meters with �=15.84 meter and �=3.53 meter. Soil depth is inversely related to soil degradation. It was classified in descending order, as higher the depth value lower is the risk. The classes as follows; High : ,� � � ��i.e. x� 12.31� Moderate:

,x� � � �� ! ! i.e. 12.31 x! ! 19.36 Low : x ,� �" ��i.e. x " 19.36

One notable observation from the normal density function of the class High (Fig 4.a) and class Moderate

(Fig 4.b), is that there are some values in between �+� of higher class and �-� of lower class, i.e. areas having depth values from 11.8 to 12 meter. These are representing the transitional zone in between High and moderate class. These values have a tendency to be changed into lower value, if unchecked.

Figure 4: Normal density function of individual classes of soil depth; i.e. 4.a High, 4.b Moderate and 4.c Low, showing membership of values in each respective class.

The normal density function of class Moderate (Fig. 4.b) also demonstrates some values more than �+2�, i.e. areas with depth values from 18 to 20 meter with a propensity to shift towards higher values hence in Class low. These areas are referring area under moderate risk with a tendency to get a positive change. Thus both the areas having vulnerability towards negative and positive changes can be recognized in this model.

(a)

(b)

(c)

Journal of Geomatics 80 Vol.6 No.2 October 2012 �Soil pH Soil pH is a measure of the acidity or alkalinity in soils, which is commonly called soil reaction. pH is defined as the negative logarithm (base 10) of the activity of hydrogen ions (H+) in solution. It ranges from 0 to 14, with 7 being neutral. A pH below 7 is acidic and above 7 is basic. Soil pH is considered a master variable in soils as it controls many chemical processes that take place. It specifically affects plant nutrient availability by controlling the chemical forms of the nutrient. The optimum pH range for most plants is between 6 and 7.5, however many plants have adapted to thrive at pH's outside this range (Townsend,1973). Classes of soil based on pH factor are given in table 1 (USDA , 1999).

Table 1: Soil pH classes

Both the higher and lower pH values indicate incompatibility of land for productivity hence degradation. pH values are classified in three successive classes, namely Low, Moderate and High, according to their deviation from mean in ascending order. It is to be noticed that in this case both the class low and Class High are vulnerable and to be considered in high risk for desertification. In Bellary, the pH values of soil ranges between 5.5 to 9.5 with �=7.72 and �= 0.85. Thus, the classes are (Fig. 5. a,b,c) Low : ,� �! � i.e. x < 6.87 Moderate : x ,� � � �� ! i.e. 6.87 x < 8.57 High : x x ,� �" i.e. x 8.57"

The membership analysis of class Low (Fig 5.a) and class Moderate (Fig 5.b) of soil-pH shows a considerable number of values in between μ+2� of the previous class and μ-2� of the later class, i.e. areas with soil ph values from 6.75 to 7.54. These values are referring a intermediary area between Low and Moderate class, mostly represented by the left tail part of moderate class. Thus these values have a tendency to be changed into lower values hence making the soil more acidic.

The areas represented by the pH values falling in Class High are already in High risk of desertification. Additionally the normal density graph of this class (Fig 5.c) shows a considerable amount of values more than μ+2�, i.e. areas with pH value more than 9.2. The pH values of these areas are very much susceptible to be increased, hence need immediate attention to lower down the values.

Figure 5: Normal density function of individual classes of soil pH; i.e. 5.a Low, 5.b Moderate and 5.c High, showing membership of values in each respective class

NDVI Areas affected by desertification processes lose progressively their level of biological quality and productivity. The most important single factor in the protection of soil fertility, hence productivity, is vegetation. Destruction of vegetation, most often by human activities accelerates soil degradation, hence coupling with other responsible factors, leading desertification. Vegetation vigor can be portrayed in NDVI (normalized difference vegetation index), which can be obtained from the NDVI image, following the transformation: NDVI=(IR-R)/(IR+R) , where IR and

Soil type pH range Ultra acid <3.5 Extremely acid 3.5-4.4 Very strongly acid 4.5-5.0 Strongly acid 5.1-5.5 Moderately acid 5.5-6.0 Slightly acid 6.1-6.5 Neutral 6.6-7.3 Slightly alkaline 7.4-7.8 Moderately alkaline 7.9-8.4 Strongly alkaline 8.5-9.0 Very strongly alkaline >9

(a)

(b)

(c)

Journal of Geomatics 81 Vol.6 No.2 October 2012 �R are the reflectance value of pixels in Infra-red and Red band respectively. NDVI values range between -1 to +1, representing different chlorophyll content, hence the health status of vegetation. For the study, the images of Rabi season (6th January, 2006) has been taken, and has converted from DN value image to spectral radiance image, and further to apparent reflectance image (Lillesand and Kiefer, 2000). Negative NDVI values were not considered for the analysis. Thus NDVI values ranges from 0.004 to 1 with �=0.5 and �= 0.3, and are classified into following classes; High : x ,� �! � i.e. x 0.2 Moderate: ,x� � � �� ! ! i.e. 0.2 < x < 0.8�

Low: x " ,� � i.e. x " 0.8

Figure 6: Normal density function of individual classes of NDVI; i.e. 6.a High, 6.b Moderate and 6.c Low, showing membership of values in each respective class The NDVI values from μ+� of class High (Fig 6.a) and μ-� of class Moderate (Fig 6.b), i.e. areas with NDVI values 0.15 to 0.32 are referring a susceptible zone of high risk. Similarly, the NDVI values from μ+� of

class moderate (Fig 6.b) and μ-� of class Low (Fig 6.c), i.e. areas with NDVI values 0.7to 0.84 are referring a susceptible zone of moderate risk. The values have a tendency decrease. Thus these areas need immediate attention for preventive measures to increase the NDVI values and to stable down in a certain class. Once the membership grades to the fuzzy variables are evaluated, the risk of desertification would be obtained using geospatial analysis techniques. The risks of socio economic parameters were derived using cumulative weighted method (Sastry, 2010). To locate the area under different severity of desertification risk, all the parameters were combined in GIS environment using multicriteria based geostatistics. 5. Results and discussion The study results in identification of areas under risk of desertification in terms of four major natural parameters, namely slope, soil depth, soil pH and NDVI. The above observations, as inferred from the normal density functions of individual classes of individual parameters, were portrayed spatially in GIS environment to actually know the areas under risk of desertification with their severity. The overall vulnerable areas for desertification were indentified integrating risks of individual parameters in GIS environment. The study results in several maps (Fig 7, 8, 9, 10 and 11) of individual parameters as well as map showing overall desertification risk area combining all parameters (Fig 12). The areas around Bellary, Hospet and Sandur are most vulnerable areas. These areas are exhibiting high higher slope and soil pH along with low depth and NDVI values (Fig 7, 8, 9). These areas are facing higher rate of degradation from deforestation due to illegal mining, salinization due to over irrigation and increasing urbanization (processes identified from images and confirmed from field study).

Figure 7: Slope map

(a)

(b)

(c)

Journal of Geomatics 82 Vol.6 No.2 October 2012 �

Figure 8: Soil map

Figure 10: Socio-economic Index map

Figure 9: NDVI map

Figure 11: Facility Index map

Figure 12: Overall Desertification Risk map, combining Soil, vegetation, slope, socio-economic parameters and Facility parameters

Journal of Geomatics 83 Vol.6 No.2 October 2012 �Two different maps were made using combined socioeconomic parameters and amenities’ parameters and risk areas were identified. Parameter indices were derived using the cumulative weighted method (Sastry, 2010), using the Census 2001 data. Overall desertification risk map (Fig 12) was prepared incorporating the above considered parameters. Overall Desertification risk Index was derived integrating Individual parameter risks using Geostatistical overlay operations. Final Desertification risk Index was normalized in a range of 1 to 10 and was classified according to their deviation from mean in three risk classes namely, “Low”, “Moderate” and High”. In this paper, we have specifically focused on the fuzzy membership analysis of natural parameters. This study showed how the fuzzy membership functions can represent the distribution of data, identifying the transitional values, indicating vulnerability towards desertification. In particular, we dealt with the case of tail-end boundary conditions of Soil pH and Vegetation Index. It must be highlighted that, we assume the distribution of variable to be Gaussian in nature, which can be considered as more comprehensive and appropriate representation of data distribution, than any other error probability density function. 6. Conclusion and future scope From the study following conclusions can be made: Space technology and Geoinformatics technique along with ancillary data, permits us to have a detailed analysis of desertification vulnerability by integrating all effective factors to take proper decisions. Analysis of various observations made from different natural parameters results to identify the areas under different desertification risk. The model with membership analysis helps to recognize both the areas susceptible for higher and lower risk in terms of natural degrading factors. The normal probability density function is suitable for membership analysis. The cumulative weighted method allows us to incorporate and analyze the effects of socio-economic accountable factors both in individual and in combination. The severity of risk has been recognized analyzing the individual response as well as their combined effect. The middle part of Bellary district, particularly in and around Bellary and Hospet tehsil, along with some areas in the southern part of the district are in high risk of desertification in terms of natural parameters like slope, soil depth, soil pH and NDVI.

The future scope of the study is to predict the risk severity in future and to validate the model efficiency with future status. Acknowledgement Authors are thankful to Dr. R.R. Navalgund, then Director, Space Applications Centre, Ahmedabad and Dr. J.S. Parihar, Dy Director, Earth, Ocean, Atmosphere, Planetary Sciences and Applications area, Space Applications Centre for their continuous support and encouragement. References

GLASOD, Global Assessment of Human-induced Soil degradation (1990). International Soil Reference and Information Centre, Wageningen, Netherlands, and UNEP, Nairobi, Kenya, 2004, http://lime.isric.nl/index.cfm,contentid_158. LADA, Land Degradation Assessment in Drylands (2011). Final draft, Mapping Land Use Systems at global and regional scales for Land degradation Assessment Analysis, Version 1.1.

Lillesand Thomas M. and R. W. Kiefer, (2000);Remote Sensing and image Interpretation”, Fourth edition,Chapter 7,479-482 p.,Pub. John Willey & Sons,Inc.,USA.

MEA,Millennium Ecosystem Assessment (2005). Ecosystems and Human Well-being: Desertification Synthesis, World Resources Institute, Washington, DC.

Reynolds, J.F., D.M.S. Smith and E.F. Lambin (2007). Global Desertification: Building a science for dryland development, Science, 316, 847-851 p.

Sasikala K.R., M. Petrou and J. Kittler (2001). Fuzzy Classification with A GIS As An Aid To Decision Making, Journal of Fuzzy Sets and System, ELSEVIER, 118, 121-137.

Sastry, G.S. (2010). Centre for Ecological Economic & Natural Resources (CEENR), Institute of Social & Economic Research (ISEC), Project report, Desertification vulnerability index model, a case study district Bellary district.

Townsend, W.N. (1973). “An Introduction to the scientific Study of the Soil”, Fifth edition, Chapter 14, 161-172 p., Pub. Edward Arnold, London.

UNCCD, United Nations Convention to Combat Desertification (1994). Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa, U.N.Doc.A/AC/241/27, 33 I.L.M. 1328, United Nations.

Journal of Geomatics 84 Vol.6 No.2 October 2012 �UNCCD, United Nations Convention to Combat Desertification (2008). Desertification- Coping with Today’s Global Challenges in the Context of the Strategy of the UNCCD.

UNEP, United Nations Environmental Programme (1992). Don’t desert dry lands! Facts about deserts and desertification; www. unccd.int. UNEP, United Nations Environmental Programme (1997). The World Atlas of Desertification, Pub. Arnold, Great Britain, ISBN 0340691662.

USDA, United States Department of Agriculture (1999). Soil Taxonomy, A Basic System of Soil classification for Making and Interpreting Soil Surveys, second edition, http://soils.usda.gov/classification.

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Monitoring of vegetation using multi-sensor temporal satellite data

R.K. Chingkhei and Arun Kumar

Department of Earth Sciences, Manipur University, Canchipur, Imphal, Manipur, India Email: [email protected] ; [email protected]

(Received: April 2, 2011; in final form August 22, 2012)

Abstract: The present study deals with the monitoring of vegetation using multi-sensor temporal satellite data in the Kangchup Chiru Reserved Forest of Manipur that lies between latitudes: 24�49� N - 24�52� N and longitudes 93�47� E - 93�49� E. The satellite data used are LANDSAT TM 7 (1988), IRS 1-C LISS III (1998) and IRS P6 LISS III (2008). NDVI (Normalized Difference Vegetation Index) of each year is analysed using Erdas Imagine to study vegetation and finally a composite image of all these NDVI images is produced to analyse the change pattern in the vegetation over the last 20 years (i.e. 1988—2008) of present study. This technique provides a faster means of visualization that makes interpretation easier when compared to other techniques. Advanced RGB clustering technique is used to classify the composite NDVI Image and six classes viz. absent (17.38%), constant (14.08%), gain since 1988 (16.68%), gain since 1998 (28.00%), loss since 1988 (9.61%) and missing in 1998 (14.24%) could be analysed and broadly studied using the Erdas Imagine software and finally a thematic map of the same is generated. Keywords: Multi-sensor temporal satellite data, digital image processing, NDVI, remote sensing, vegetation monitoring 1. Introduction Vegetation has been an important part of our life and its monitoring has also become quite indispensable in the present scenario of climate change. In this regards, scientists have been trying to develop new techniques and methodologies to carry out the task more efficiently. One such area is the science of remote sensing that has been greatly improved in the recent years. It is considered to be one of the powerful tools that provides important information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, phenomenon or area under investigation (Lillesand and Kiefer, 2002). It is a widely accepted and cost-effective approach to monitor and document changes over large areas that has been providing great help in monitoring the changing pattern of vegetation (Lunetta et al., 2004). With the advancement of remote sensing, variety of data from different sensors onboard various satellites are available at our disposal for various applications viz agriculture, forestry, geology, hydrology etc. One such area of extensive use of remote sensing data is monitoring of vegetation both in terms of changes and its health. Monitoring of vegetation can be done from regional to global scale in varying space and time through the long-term time series use of the continuous Earth Observations satellite data. During the last few decades the Normalized Difference Vegetation Index (NDVI) derived from the coarse resolution advanced very high resolution radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellites have been used as a useful index for estimating varying amount of vegetation greenness, health and chlorophyll concentration at regional

(Myneni et al., 1998; Huete et al., 2002; Mutanga and Skidmore, 2004) and global (Huete and Tucker, 1991) scales. 2. Vegetation monitoring using remote sensing data Monitoring of vegetation using remote sensing data can be achieved by a process called “change detection” which means revealing any changes in temporal effects such as variation in spectral response and involves situations where the spectral characteristics of the vegetation or other cover type in a given location change over time (Hoffer, 1978). It is also described as a process that observes the differences of an object or phenomenon at different times (Singh, 1989). Various techniques of change detection have been developed to detect vegetation change using remote sensing data (Cakir et al., 2006). Some of the most commonly used ones are image differencing e.g. NDVI differencing, post-classification comparison, principal component analysis (PCA) and change vector analysis (Byrne et al., 1980; Riordian, 1980; Fung and LeDrew, 1987; Singh, 1989; Fung, 1990; Lambin and Strahler, 1994; Johnson and Kasischke, 1998; Sunar, 1998; Mas, 1999; Sohl, 1999; du Plessis, 1999; Cakir et al., 2006). Despite the varied forms of algorithm used in the change detection techniques, it can usually be grouped into two main categories viz (i) post-classification spectral change detection and (ii) pre-classification change detection or enhancement change detection techniques (Nelson, 1983).

Post classification technique involves the independent production and subsequent comparison of spectral classifications for the same area at two different time periods (Mas, 1999). In this technique the Thematic



maps generated are further compared and analysed to map any types of changes uncovered (Jensen, 1996). On the other hand, enhancement techniques involve the mathematical combination of images from different dates prior to classification which, when displayed as a composite image, show changes in distinctive colors (Pilon et al., 1988). The enhancement change detection techniques have the advantage of generally being more accurate in identifying areas of spectral change (Singh, 1989). The present study uses the application of NDVI composite, one of the enhancement change detection techniques, to monitor the temporal changes in the vegetation using satellite data from different sensors of LANDSAT TM and Indian Remote Sensing (IRS) satellites acquired at different time.

3. Vegetation of North Eastern (NE) India The North Eastern (NE) region of India comprising of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura and Sikkim is a home to various forms of biota with a high level of endemism because of its unique location being at the confluence of the Indo-Malayan, Indo-Chinese and Indian biogeographical realms. The region is endowed with 6 out of 9 important vegetation types of the country and 51 forest types broadly classified into six major forest types (Hegde, 2000) that has some of the largest reserves of tropical and sub-tropical forests of wet evergreen, semi- evergreen, moist deciduous, coniferous forests, mixed forest and shrub land (Globcover, 2008; Roy and Joshi, 2002). According to an official report of Forest Survey of India (FSI) 2009 the total forest area of the region is estimated as 170,423km2, which is 66.81% of the geographical area as against the national average of 21.02% (Anon. 2009). The region is also regarded as one of the global biodiversity centers and a hotspot (Myers et al., 2000) with climate ranging from typical tropical to sub-alpine type (Roy and Joshi, 2002).The lowland and montane moist to wet tropical evergreen forests of the region are considered to be the northernmost limit of true tropical rainforests in the world (Proctor et al., 1998). in the last few years many workers (Anon. 2002a, 2002b; Roy et al., 1991, 2002; Gupta, 2006; Roy and Saran, 2004; Singh et al., 2002; Srivastava et al., 2002; Behera et al., 2002; Roy and Tomar, 2001) have studied the vegetation of the region in various aspects to have a better understanding in developing a good management plan for conservation and preservation of this enormous natural resource of the region. The present study is also an attempt to find out the changing trend in the study area.

4. Significance of change detection The present study has been taken up based on the following points of view.

1. So far no study of this kind has been done in the study area. Despite the importance of being part of the eco sensitive area of Senapati district, no study has been done to monitor the vegetation changes in this area and the other alike.

2. This Reserve Forest (RF) is located very close to

the main city (14km) which is vulnerable to degradation due to human activities like logging, firewood collection and other activities.

3. Manipur like other parts of the northeast region

has also witnessed excessive logging since the colonial days for revenue generation (Handique 2004). The practice continued until the Supreme Court ban on logging in 1995, however clandestinely it is done in some areas in the reserve forests (SBSAP). This practice might still be prevailing in the study area needs to be checked. Although the forest cover in Manipur extends to 78% of the total geographic area, only 22% of forest area is under dense forest cover and the rest has been converted to open forests (Ramakanta et al., 2012) in the recent years.

5. Study Area The present study is carried out in one of the reserve forests of Manipur viz Kangchup Chiru Reserve Forest situated in the Sadar Hills District which is under the administrative control of State Northern Forest Division. It lies between 24�49� and 24�52� North Latitude and 93�47� and 93�49� East Longitude in Senapati District about 14 km from Imphal town at an altitude ranging from 902 m to 944 m above mean sea level with a geographical area of 747 Hectare approximately (Fig 1). The forest in this region falls under East Himalayan Sub-Tropical Wet Hill Forest (Champion and Seth, 1968) and dominated by Castanopsis tribuloides and Quercus dealbata. The study area experiences a warm moist summer and cool dry winter with monsoonal rainfall. The mean maximum temperature varies from 15-89�C (November) to 29.5�C (July) and the mean minimum temperature ranges from 5.2�C (December) to 22.6�C (July). The mean annual rainfall is 1264.3 mm. The average relative humidity of air varied between 65.1% (December) to 84.0% (July). Kangchup, Pheiyeng, Tairenpokpi, Phayeng Khunou, Natap, Kangchup Chiru and Kangchup Makhong are the important villages nearby the Kangchup Chiru Reserve Forest. The village called Kangchup Chiru which is associated with the name of this study area is located in the western side of the reserve forest. The Maklang River which drains the water from this reserve forest divided this reserve forest from the rest of other forests in the west. The village road running in the middle of the study area leads to Hangoipat village and K.Phaizawl village in the midst of the study area.


Figure1: Location Map of the study area. The Pink boundary indicates the Kangchup Chiru Reserve Forest, the present study area 6. Methodology Base map of the study area is prepared from the Survey of India (SOI) toposheet with UTM projection and WGS84 spheroid and datum. The digital satellite data that are used for the digital image processing, mapping and monitoring of vegetation in the study area are thoroughly checked to rectify any geometric and radiometric distortion. Techniques like contrast enhancement, breakpoints etc are used to enhance the imageries so that their histogram distribution becomes almost similar to one another which in turn helps in carrying out the comparative study of the imageries. 6.1 Data calibration and atmospheric correction The satellite data of all the dates were radiometrically corrected to compensate the differences in inter-sensor settings by converting the DN values of respective bands to their radiance using the following standard conversion formula in the model maker module of Erdas Imagine. Lrad = (DN/MaxGray)*(Lmax – Lmin) + Lmin where, Lrad is the Radiance for a given DN value; DN is the Digital Count; MaxGray is the Maximum possible Gray Value and Lmin/Lmax is the Minimum/Maximum radiance value for a given band which are extracted from the respective image header files.

The dark body (pixel) subtraction technique is then applied to all the resultant bands to correct the atmospheric attenuation and to obtain near ground surface reflectance. 6.2 Geo-referencing and study area extraction The 1988 (LANDSAT TM, dated: 05-02-1988) satellite imagery was first geo-referenced using the SOI toposheets using second order polynomial transformation fit with the subpixel root mean square (RMS) error of 0.5 pixels. Consequently, the remaining imageries, i.e. 1998 (IRS-1C L3, dated: 23-12-1998) and 2008 (IRS-P6 L3, dated: 22-01-2008) were then geo-referenced to the 1988 (geo-referenced) imagery to enable further analysis. Before geo-referencing, the 1998 and 2008 imageries were resample to the pixel size of the 1988 LANDSAT TM imageries to achieve higher accuracy in geo-referencing and to enable image compositing. From these geo-referenced imageries the study area is extracted using the subset utility of the Erdas Imagine software and subsequent analysis is carried out on these extracted scenes to reduce space and time consumption. The detailed methodology is represented in the flow diagram (Fig.2).

Figure 2: Flow diagram showing the stages of the methodology 6.3 NDVI Generation Normalized Difference Vegetation Index (NDVI) is one of the most widely used vegetation index that characterizes the vegetation vigour (Rouse et al., 1974) which was introduced by Deering (Deering, 1978) and Tucker (Tucker, 1979). It is a nonlinear function which varies between -1 and +1 but is undefined when RED and NIR (Near Infra Red) are zero. Only the positive

Satellite Imageries [1988, 1998 & 2008]

Data Preparation [Image rectification, enhancement,

data calibration, geo-referencing etc]

NDVI generation [NDVI= (Infrared -Red) / (Infrared + Red)]

NDVI composite [Composite Image=2008+1998+1988]

NDVI classification [Advance RGB clustering leads to 234

optimum classes that were finally merge down to 6 classes]


values correspond with vegetated zones. The negative values, generated by a higher reflectance in the visible region than in the infrared region, are due to clouds, snow, bare soil and rock. The NDVI is represented by the following formula.

NDVI = (Infra Red - Red) / (Infra Red + Red)

NDVI for each of the satellite imageries was produced using Erdas Imagine that results a triplicate set of 8 bit grayscale images representing the amount of vegetation present at each time (1988, 1998 and 2008). In these images, light areas represent regions of high vegetation, and conversely, dark areas show regions of low vegetation. 6.4 Image compositing The resultant grayscale images (NDVI images) were then each assigned to an individual channel of RGB composite image. The 1988 NDVI image was assigned to the blue channel and similarly the 1998 NDVI and 2008 NDVI was assigned to the green and red channel respectively (Fig. 3).

Figure 3: RGB Composite Image of the NDVI Images (Red-2008, Green-1998 & Blue-1988)

6.5 Classification The three date RGB composite image was classified using the Advanced RGB clustering Module of the Erdas Imagine. The Advanced RGB Clustering utility plots the pixels from 3 selected input bands into a 3-dimensional feature space (ERDAS, 1999a). Then a 3D grid is used to partition the space into clusters. Then a minimum threshold is set on the clusters, so that only clusters that are as large as the threshold will become output classes. Pixels which do not fall into any of the remaining clusters are assigned to the cluster with the smallest city-block distance from the

pixel. The city-block distance is calculated as the sum of the distances in the red, green, and blue directions of the 3-dimensional space. As a result 234 classes were classified that are later on merged using the grouping and recode tools of the Erdas Imagine to obtain only six classes (Fig. 4).

Figure 4: Classified Composite Image of the study area 7. Result and Discussion NDVI images have long been used and are widely accepted as an effective means of monitoring vegetation. The majority of change detection techniques like image differencing, image ratioing and Principal Components Analysis (PCA) result in data that are somewhat comparatively difficult to interpret (Anderson, 2002; Gomez, 2009). Unlike these methods, NDVI production followed by image compositing, results in an easily interpretable and extremely quick approach to vegetation monitoring and change detection (Gomez, 2009). One of the major benefits of this method is that it is based on the NDVI because NDVI, being a ratio between reflectance in different bands, it corrects for eventual errors due to topography and shade and compensating for variation in illumination due to terrain (Lillesand and Kiefer, 2002). And further, the abstraction of the multiband images into a single 8 bit greyscale (Erdas, 1999b) of NDVI is the key to success of the technique. By reducing a date to a single NDVI image, the processing time is also considerably reduced. This method also allows for the comparison of three rather than only two dates in a single image that provides good information in analyzing the temporal changes in vegetation.

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The 1988 (blue channel)-1998(green channel) NDVI composite shows a good amount of vegetation in the central part of the study area in 1988 and an apparent change of vegetation towards some part of north, south and near Sungang village at the centre in 1998 (Fig. 5a). The eastern, north-eastern, south-eastern and some part of the southern and western portion of the region remained constantly vegetated throughout the period as indicated by the light tone (high vegetation) as well as cyan indicating presence of vegetation in both years. There is also good amount of vegetation in the central portion in 1988 (blue) which is absent in 1998. In the 1998(green channel)-2008(red channel) NDVI composite, it is clearly seen that most of the vegetation present in 2008 is absent in 1998 as indicated by the reddish tint (2008) to the entire image. During this period also, the constantly vegetated areas in the 1988-1998 composite continues to follow the same trend and is clearly seen in the entire image as light (high vegetation) and yellowish tone (Fig. 5b). The three year composite of NDVI (Fig. 3) shows some increase (reddish) in the amount of vegetation present in the region. The green areas are those which have lost vegetation during 1998-2008 while the blue areas are those which have lost vegetation in 1988-1998. Further, there is a fair amount (white) of vegetation along the eastern, north-eastern, south-eastern and some part of the southern and western portion of the region in all three years (14.08%). The six classes derived from the advanced RGB clustering of the composite NDVI image also clearly shows the presence and absence of vegetation. The classified image illustrates a decreasing amount of vegetation from 1988 to 1998 and apparent increased in overall vegetation during the year 2008 by 28.00% (Table 1). Table 1: Vegetation changes during the study period (1988-2008)

Sl no Class Area

(ha) Percentage

(%) 1 Absent 129.80 17.38 2 Constant 105.19 14.08 3 Gain since 1988 124.60 16.68 4 Gain since 1998 209.15 28.00 5 Loss since 1988 71.80 9.61 6 Missing in 1998 106.32 14.24

Total 746.86 100.00

The present study indicates that there is gain in the area of the vegetation since 1988 and 1998 which are noticeable during the 2008 data is mainly because of the Joint Forest Management (JFM) programme, a programme that involves the participation of villagers in forest management, adopted by the state forest department (Fig. 6). Accordingly, various species like pinus khasya, schima wallichii, alnus nepalensis, litsaea polyantha, cedrela toona, emblica officinalis,

heining spondias magnifera etc were planted from time to time which helps in maintaining vegetation status of the study area. Besides, there is lack of commercial exploitation of timber as the villagers have change their way of livelihood to cash cropping which gives less pressure to the forest areas. This increase in vegetation corresponds to the open and dense forest class of the FSI classification. On the other hand, the loss of vegetation since 1988 and missing in 1998 corresponds to the grassland and the scrub class of FSI classification. The reason for the loss is mainly attributed to the changing life style of the villagers who have stopped timber logging and shifted to cash crop (ginger, arum, passion fruit, pine-apple etc) cultivations (Fig. 6) Firewood collection also contributes some amount of loss.

Figure 5: Composite NDVI images of (a) 1988(Blue) and 1998 (Green) and (b) 1998(Green) & 2008(Red)

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(a)

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(b)


Figure 6: Field Photograph showing the vegetation changes in the study area during 1998 and 2008. The red boundary is the changes in the grassland / scrub areas that has been utilised for pineapple cultivation and the yellow boundary is the area where plantation (mostly pine) has taken up as per JFM programme. 8. Conclusion The use of multi-sensor temporal satellite data like LANDSAT TM, IRS-1C L3 and IRS-P6 L3 imagery to produce NDVI images and subsequent colour composites is an effective method to monitor and describe vegetation changes over time. By creating three year colour composites it has become easier to discriminate change areas. Using this technique the vegetation of Kangchup Chiru Reserve Forest, one of the reserve forest of Manipur, has been successfully studied. Further, it is worth mentioning that in spite of its proximity to city and the dependency of seven villages (about 1000 families) for livelihood and others in and around it, the mixed very dense forest area of the study area still remains less disturbed which is being identified in the present study as the “constant” class. The main reason is the JFM programme and the general awareness of the villagers about the deforestation activities. With their sincere efforts the reserve forest is being maintained. This could be a lesson learnt for other parts of the region as well. References Anderson, J. (2002). A Comparison of Four Change Detection Techniques for Two Urban Areas in the United States, Research Project submitted to the Eberly College of Arts and Sciences, Department of Geology and Geography, West Virginia University, Morgantown, West Virginia. Available: http://wvuscholar.wvu.edu:8881/exlibris/dtl/d3_1/apache_media/L2V4bGlicmlzL2R0bC9kM18xL2FwYWNoZV9tZWRpYS82MTkx.pdf.

Anonymous (2002a). Biodiversity Charaterisation at Landscape level in North-East India using Satellite Remote Sensing and Geographical Information System. Indian Institute of Remote Sensing, Dehradun. Anonymous (2002b). Wielding the stick – Ban on felling becomes an instrument for Northeastern states to gain forest control. Down to Earth, March 15, pp. 26-34. Anonymous (2009). State of Forest Report 2009. Forest Survey of India, Dehradun, India. Behera, M.D., S.P.S. Kushwaha, P.S. Roy, S. Srivastava, T.P. Singh and R.C. Dubey (2002). Comparing structure and composition of coniferous forests in Subansiri district, Arunachal Pradesh. Current Science, 82 (1), 70-76. Byrne, G., P. Crapper and K. Mayo (1980). Monitoring land-cover change by principle components analysis of multitemporal landsat data. Remote Sensing of Environment, 10: pp. 175-184. Cakir, H.I., S. Khorram and S.A.C. Nelson (2006). Correspondence analysis for detecting land cover change. Remote Sensing of Environment, 102: pp. 306-317. Champion, H.G. and S.K. Seth (1968). A Revised Survey of the Forest Types in India. Govt. of India Press, Nasik. Deering, D.W. (1978). Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. Diss. Texas A&M Univ., College Station, 338p. Du Plessis, W.P. (1999). Linear regression relationships between NDVI, vegetation and rainfall in Etosha National park, Namibia. Journal of Arid Environments, 42, 235-260. ERDAS (1999a). Field Guide, fifth edition, ERDAS, Inc. Atlanta, Georgia. ERDAS (1999b). Tour Guide, fifth edition, ERDAS, Inc. Atlanta, Georgia, pp. 247-248. Fung, T. (1990). An Assessment of TM Imagery for Land-Cover Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 28: pp. 681-684. Fung, T. and E. LeDrew (1987). Application of Principle Component Analysis to Change Detection. Photogrammetric Engineering & Remote Sensing, 53: pp. 1649-1658. Globcover (2008). Products Description and Validation Report 12.1 version: 1-47 (www.postel.medias france. org)


Gomez, O. F. (2009). Change Detection of Vegetation Using LANDSAT Imagery. Available: http://www.crwr.utexas.edu/gis/gishydro99/class/gomez/termproj.htm Gupta S., Sarnam Singh, Shefali Agarwal and P.S. Roy (2006). Degradation of Tropical Evergreen Forests in Mokokchung, Nagaland Area, India. International Journal of Ecology and Environment Sciences, 32 (4), 345-356. Handique, R. (2004). British Forest Policy in Assam. Concept Publishing Company, New Delhi Hegde, S.N. (2000). Conservation of North East Flora. Arunachal Forest News, Vol 18 (1&2). Hoffer, R.M. (1978). Biological and physical considerations in applying computer-aided analysis techniques to remote sensor data. In Swam PH, Davies SM, eds. i. New York. McGraw- Hill, pp. 242-171. Huete, A. R. and C.J. Tucker (1991). Investigation of soil influences in AVHRR red and near-infrared vegetation index imagery. International Journal of Remote Sensing, 12, 1223-1242. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao and L.G. Ferreira (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83: pp. 195-213. Jensen, J.R. (1996). Introductory Digital Image Processing- A Remote Sensing Perspective. New Jersey: Prentice-Hall, pp. 1-187. Johnson, R.D. and E.S. Kasischke (1998). Change Vector Analysis: A Technique for Multispectral Monitoring of Land Cover and Condition. International Journal of Remote Sensing, 19(3), 411-426. Lambin, E. and A. Strahler (1994). Change Vector Analysis in Multitemporal Space: A Tool to Detect and Categorize Land-Cover Change Processes Using High Temporal Resolution Satellite Data. Remote Sensing of Environment, 48: pp. 231-244. Lillesand, T.M. and R.W. Kiefer (2002). Remote Sensing and Image Interpretation. Fourth Ed. John Wiley and Sons. USA. Lunetta, R.S., D.M. Johnson, J.G. Lyon and J. Crotwell (2004). Impacts of imagery temporal frequency on land-cover change detection monitoring. Remote Sensing of Environment, 89: pp. 444-454. Mas, J.F. (1999). Monitoring Land-Cover Changes: A Comparison of Change Detection Techniques. International Journal of Remote Sensing, 20(1), 139- 152.

Mutanga, O.and A.K. Skidmore (2004). Narrow band vegetation indices overcome the saturation problem in biomass estimation. International Journal of Remote Sensing, 25,3999-4014. Myers, N., R.A. Mittermeier, C.G. Mittermeier, G.A.B. de Fonseca and J. Kent (2000). Biodiversity hotspots for conservation priorities. Nature 403, 853858. DOI 10.1038/35002501. Myneni, R. B., C. J. Tucker, G. Asrar and C.D. Keeling (1998). Inter annual variations in satellite-sensed vegetation index data from 1981 to 1991. Journal of Geophysical Research, 103, 6145-6160. Nelson, R.F. (1983). Detecting Forest Canopy Change Due to Insect Activity Using Landsat MSS. Photogrammetric Engineering & Remote Sensing, 49: pp. 1303-1314. Pilon, P. G., P. J. Howath and R. A. Bullock (1988). An Enhanced Classification Approach to Change Detection in Semi-Arid Environments. Photogrammetric Engineering & Remote Sensing, 54: 1709-17 16. Proctor, J., K. Haridasan and G.W. Smith (1998). How far does lowland tropical rainforests go? Global. Ecol.Biogeogr, Vol 7: pp. 141-146 Ramakanta, V, A.K. Gupta and Ajith Kumar (2012). Biodiversity of Northeast India An Overview. Available: http://oldwww.wii.gov.in/envis/rain_forest/chapter1.htm Riordian, C.J. (1980). Non-urban to Urban Land Cover Change Detection Using Landsat Data. Summary Report of the Colorado Agricultural Research Experiment Station, Fort Collins, Colorado: NASA Contract NAS5-25696, 40 pages Rouse Jr., J.W., R.H. Haas, D.W. Deering, J.A. Schell and J.C. Harlan (1974). Monitoring the vernal advancement and retrogradation (greenwave effect) of natural vegetation. NASA/GSFC Type III Final Rpt. Greenbelt, MD, USA: Goddard Space Flight Centre. Roy, P.S. and Sanjay Tomar (2001). Landscape cover dynamics pattern in Meghalaya. International Journal of Remote of Remote Sensing, 22 (18), 3813-3825. Roy, P.S., K.K. Das and K.S.M. Naidu (1991). Forest cover and Landuse mapping in Karbi Anglong and North Cachar Hill districts of Assam using Landsat MSS data. Journal of the Indian Society of Remote Sensing (Photonirvachak), 19 (2), 113-123.

Roy, P.S. and P.K. Joshi (2002). Tropical Forest Cover Assessment in North East India- Potentials of temporal Wide Swath Satellite Data (IRS 1C- WiFS). International Journal of Remote Sensing, 23 (22), 4881-4896.


Roy, P.S. and Sameer Saran (2004). Biodiversity Information System for North East India. Geocarto International, Vo. 19 No. 3, pp. 3-8. Roy, P.S., Sarnam Singh, Shefali Agarwal, Stutee Gupta, P.K. Joshi, Sein Aung, I.J. Singh and Yogital Shukla (2002). TREES II-Tropical Forest Assessment in India and Northern Myanmar. Paper Presented at ‘International Workshop on Tropical Cover Assessment and Conservation Issues in South East Asia’, 12-14 February 2002, IIRS, Dehradun, India. SBSAP (2005). State Biodiversity Strategy and Action Plan Reports for Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura and Sikkim. Srivastava, Shalini, T.P. Singh, Harnam Singh, S.P.S. Kushwaha and P.S. Roy (2002). Assessment of large-scale deforestation in Sonitpur district of Assam. Current Science, 82 (12), 1479-1484. Singh, A. (1989). Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing, 10, 989-1003.

Singh, T.P., Sarnam Singh and P.S. Roy(2002). Assessing Jhum-Induced Forest Loss in DibangValley, Arunachal Himalayas-A Remote Sensing Perspective. Journal of the Indian Society of Remote Sensing, 31 (1), 3-9. Sohl, T.L. (1999). Change Analysis in the United Arab Emirates: An Investigation of Techniques. Photogrammetric Engineering and Remote Sensing, 65(4): pp. 475 – 484. Gupta S., Sarnam Singh, Shefali Agarwal and P.S. Roy (2006). Degradation of Tropical Evergreen Forests in Mokokchung, Nagaland Area, India. International Journal of Ecology and Environment Sciences, 32 (4), 345-356. Sunar, F. (1998). An Analysis of Change in a Multi-date Data Set: A Case study in The Ikitelli Area, Istanbul, Turkey. International Journal of Remote Sensing, 19, 225-235. Tucker, C.J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8(2), 127-150.

Journal of Geomatics �� Vol.6 No.2 October 2012

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Modeling of spatio-temporal dynamics of land use land cover - a review and assessment

M. Surabuddin Mondal¹, Nayan Sharma², Martin Kappas1 and P. K. Garg3 ¹Dept. of Cartography, GIS & Remote Sensing, Institute of Geography, Georg-August University of Göttingen, Germany

²Dept. of W R D & M, Indian Institute of Technology, Roorkee - 247667, India 3Dept. of Civil (Geomatics) Engineering, Indian Institute of Technology, Roorkee - 247667, India

Email: [email protected] ; [email protected]

(Received: August 11, 2011; in final form May 21, 2012) Abstract: An attempt is made in this paper to identify and review remote sensing and GIS based LUCC (land use land cover change) models. Attempts are also made for critical assessment and comparative analysis of the identified reviewed models. Background of remote sensing and GIS based LUCC modeling is described in this study. Literature and web searches as well as consultations with experts were undertaken for listing / identifying the models. About 29 models were short listed on the basis of their importance (i.e. worldwide mostly used). We could not find the presence of one single model which will fulfill all the needs of LULC (land use land cover) change analyst’s community. Each and every model has its own merits and demerits, some technical limitations, considered limited human decision making, socio-economic or biophysical factors. We found that one single model cannot be acceptable worldwide for LUCC analysis, due to large regional variation in the dynamics across physical and social settings. We also found that LUCC are poorly understood. Much work remains to be done to understand and model LUCC. LUCC modeling should be continued for specific regions with consideration of different specific regional factors. Key�words:��

� 1. Introduction The land-use and land-cover change (LUCC) plays an important role in global environment change. It contributes significantly to earth-atmosphere interactions and biodiversity loss, and is a major factor in sustainable development and human responses to global change. Inventory and monitoring of land-use/land-cover changes are indispensable aspects of further understanding of change mechanism and modeling the impact of change on environment and associated eco-systems at different scales (Turner et al., 1995; William et al., 1994; Meyer and Turner, 1994). By recognizing the importance of land-use and land-cover change, there is a need for an interdisciplinary approach to explore the possibility of a co-operative research with the general goal of improving understanding of (i) the driving forces (exogenous variables) of land use as they operate through the land manager; (ii) the land-cover implications of land use; (iii) the spatial and temporal variability in land-use/cover dynamics; and (iv) regional and global models and projections of land-use/cover change. The LUCC research activities will ultimately contribute to (i) methodological advancement in the design and implementation of LUCC case studies and case study protocols, the means to interpolate and extrapolate from LUCC sample data across space and time scales, and the structure and functioning of integrated LUCC models (ii) analytical advancement in a suite of integrated LUCC models ranging from the household and farm to the globe (or local to global) (iii) cooperation with other projects and programmes, LUCC data development and format design, and (iv)

empirically-derived inventories of geographically specific land-use/cover changes and analytically-derived projections thereof across specific time scales. The understanding gained from the results of a LUCC project/programme will be of use to a wide range of researchers, policy planners and other decision makers requiring improved means of projecting land-use/cover change in terms of its implications for (i) global environmental change, (ii) local-to-regional sustainability issues and (iii) the assessment of responses to local and environmental change. Land-use and land-cover change has the potential to integrate research on the natural and human dimensions of global environmental change and the understanding gained from this integration contributes to other research and policy initiatives such as those of the World Climate Research Programme (WCRP, 1990) and the Intergovernmental Panel on Climate Change (IPCC, 1990). 2. Significance of Land Use Land Cover Change (LUCC) Land-use change is a locally pervasive and globally significant ecological trend (Agarwal et al., 2001). Vitousek (1994) notes that “three of the well-documented global changes are increasing concentrations of carbon dioxide in the atmosphere; alterations in the biochemistry of the global nitrogen cycle; and on- going land-use/land-cover change.” In the case of the United States for example, 121,000 km² of non- federal lands were converted to urban developments over a 15-year interval between 1982 and 1997 (NRCS/USDA 1999). On a global scale and over a longer time period, nearly 1.2 million km² of forest and woodland and 5.6 million km² of grassland



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and pasture have been converted to other uses during the last three centuries, according to Ramankutty and Foley (1999). During this same time period, cropland has increased by 12 million km². Currently, humans have transformed significant portions of the Earth’s land surface: 10 to 15 percent is dominated by agricultural row crop or urban- industrial areas, and 6 to 8 percent is pasture (Vitousek et al., 1997). 3. Land Use Land Cover (LULC) Change and Modeling Aspects Models on land use and land cover change are powerful tools that can be used to understand and analyze the important linkage between socio-economic processes associated with land development, agricultural activities and natural resource management strategies and the ways that these changes affect the structure and function of ecosystems (Roy and Tomar 2001) (Fig. 1). Long term understanding on land use land cover (LULC) needs to propose a more dynamic framework that explicitly links what is often divided into separate natural and human systems into a more integrated model. In developing countries like India, scenario is likely that land use and land cover are often semantically equivalent i.e. land use activities associated with logging leads to a deforested land cover (Lambin, 1997). Therefore, satellite images can often be used to detect land use change through observations of the biophysical characteristics of the lands. Contrastingly developed countries like USA, Europe LULC are less likely to be equivalent. Although, forestry can be modeled as a land use activity that responds to economic, social and demographic drivers, such drivers do not provide the direct predictors for understanding and modeling the amount and locations of forests and tree cover in all parts of a landscape (Mauldin et al., 1999 ; Geist and Lambin, 2002).

Sources: Agarwal et al. 2001

Figure 1: Conceptual Framework for Investigating Human Ecosystems 4. Background of Remote Sensing GIS Based Land Use Land Cover Change (LUCC) Modeling With the availability of aerial photographs and development of aerial photo-interpretation techniques in the 1920's, its use for land use land cover mapping

began in the mid-1930s. Several studies on land use and land cover mapping, and change detection have been subsequently carried out using aerial photographs (Avery, 1970; Sahai et al., 1977; Quirk and Scarpace, 1982). Though the Gemini and Apollo space photographs were used for mapping land use/land cover in the late 1960’s and early seventies (McPhail and Campbell, 1970), the operational use of space borne multispectral data began only after the launch of the Earth Resources Technology Satellite (ERTS-I), later renamed as Landsat-1 in July 1972. Synoptic view of a fairly large area at regular intervals provided by Landsat-Multispectral Scanner (MSS) was exploited for mapping and monitoring land use land cover following the United States Geological Survey (Anderson et al., 1971) land use land cover classification system. There is a growing trend in the development of change detection techniques using remote sensing data. The change detection techniques, thus developed, could be group into two general categories: (i) those based on spectral classification of input data such as post-classification comparison (Mas, 1999) and direct two-date classification (Li and Yeh, 1997), and (ii) those based on radiometric change between acquisition dates, including (a) image algebra method such as band differencing (Weismiller et al., 1977), ratioing (Howarth and Wickware, 1987) and vegetation indices (Nelson, 1983); (b) regression analysis (Singh, 1986); (c) principal component analysis (Byrne et al., 1980; Gong, 1993), and (d) change-vector analysis (CVA) (Malila, 1980). In addition, hybrid approaches involving a mixture of categorical and radiometric change information are also proposed and evaluated (Colwell and Weber, 1981). Post-classification comparison examines changes over time between various thematic land-cover categories (e.g. forest, grassland, agriculture); it is advantageous when using different sensors, with different spatial and spectral resolutions, between image dates (Singh, 1989). Further, post-classification comparison permits the use of information on the types of land-cover transformations that ever, this approach has significant limitations, because the comparison of classifications for different dates does not allow the detection of subtle, low-magnitude modifications within land-cover categories (Stow et al., 1980). Further, the propagation of error through post-classification comparison approaches is documented (Stow et al., 2002; Macleod and Congalton, 1998). Pre-classification enhancement approaches to land-cover change involve enhancing alterations in the concentration of some landscape attribute that can be continuously measured (e.g. spectral vegetation index) (Coppin et al., 2004). Various methods are developed to compare multi-temporal signatures are reviewed (Singh, 1989; Jensen, 1996). Pre-classification enhancement, therefore, appears more suitable than post-classification comparison for land-cover


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monitoring programs that require detailed regional estimates of forest-cover change and the associated causes of that change. Some attempts are made to evaluate the reliability of various change detection techniques in order to suggest a particular technique for land-land-cover change detection. For instance, while evaluating the automated methods for change detection for identifying an optimum algorithm for forest change detection Singh (1986) observed that the regression method using Landsat MSS band 2 produced the highest change detection accuracy followed by image rationing and image differencing.

5. Remote Sensing GIS Based Modelling of Land Use Land Cover Changes Projections of future land-use and land-cover patterns are needed to emulate the implications of human action for the future of ecosystem (Turner et al., 1995). Models of land-use and land-cover change have been developed to address which, where and why land-use changes occurs (Riebsame et al., 1994; Lambin, 1997; Theobald and Hobbs, 1998). These models are also very useful tools that can be used to understand and analyze the important linkage between socio-economic processes associated with land development, agricultural activities, and natural resources management strategies and the ways that these changes affect the structure and function of ecosystems (Turner and Meyer, 1991). They usually involve empirically fitting the models to some historical pattern of change, then extending those patterns into the future for prediction. These models present a range of outcomes that reflect the current and recent trends that can serve as useful benchmarks against which more process-oriented models can be compared. To be useful, predictive models need to represent with reference to current and recent trends, the (i) amount of land cover changes, (ii) location of future changes, and (iii) spatial patterns of those changes. Although several models exist to address the first two of these conditions (Veldkamp, and Fresco, 1996; Landis and Zhang, 1998), few models exists that specifically aim to reproduce the spatial patterns of land-cover changes. Several empirical models are developed to address the land-cover conversion process. Transition probability models are extensively used for analysis and stochastic modelling of land-use and land-cover change (Bell, 1974; Turner, 1987; Muller and Middleton, 1994). Markov chain models represent a suite of such models. The central mechanism of a Markov chain is a probability function which refers to the likelihood of transition from one cover to another cover. The probability function can be static over time or can be adjusted at specific intervals to account for changes in the stationary of the processes controlling the transition sequences. The probability function and transition sequences can be derived from direct observations using satellite data. The primary limitations of Markov transition probability-based models for land-use and

land-cover change analysis are: (1) the assumption of stationary in the transition matrix i.e. that it is constant in time and space; (2) the assumption spatial independence of transitions; and (3) the difficulty of ascribing causality within the model, i.e. the transition probabilities are often derived empirically from multi-temporal maps with no description of the process (Baker, 1989). The third limitation assumes greater significance in the context of land-cover change studies from remotely-sensed images, and when those changes are driven by economic and social processes. To address the limitations 1 and 3, Baker (1989) suggested setting state transition probabilities as a function of exogenous or endogenous variables, which vary in space and time. Another appropriate model framework is the suite of logistic function models. These models are used in various case studies to account for changes in the rate of land-cover conversion under constraints. Whereas Markov transition probabilities provide a convenient analytical framework for simulating land-cover change using observed transitions, e.g. from remote sensing, alternate approaches are used to for modeling the influence of social and economic drivers on land-cover change. The alternative model structures are designed to introduce a better representation of causative factors into the models by relating change to either exogenous driving variables, spatial interaction process or both. Two primary types of land-use land-cover change models, namely regression type models and spatial transition-based models are used for land-cover change analysis (Theobald and Hobbs, 1998). The first type of models establishes functional relationships between a set of spatial predictor variables that are used to predict the location of change on the landscape. The regression models utilize a system of observation in conjunction with ancillary variables, such as socio-economic data, to identify explicitly the causes of land-use change. These types of models attempt to relate rates of cover-conversion to data expressing the various hypothesized driving forces or proximate causes of land-cover change. Regression analyses can be conducted in two ways: by cross-sectional analysis (i.e., at one point in time across a large number of specific locations), or by panel analysis (by relating change in cover during an interval of time to changes in other variables during the same interval across a large number of specific locations). Included in this category are logistic regression models (Landis, 1994), hedonic price models (Alig, 1986; Geoghegan et al., 1997) and artificial neural networks (Pijanowsky et al., 2000). The spatial-transition-based models are exemplified by a spatial-temporal expansion of the Markov transition models referred to as cellular automata (Deadman et al., 1993; Clarke et al., 1997). These models use spatially variable transition probabilities to account for the effects of exogenous variables on the transition process (Baker, 1989; Brown et al., 2000).To estimate probabilities of land-use transition, land-use change is


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typically modeled as a function of variables describing biophysical land quality (i.e. soils and terrain) and location relative to, for example, jobs, markets and amenities (Landis and Zhang, 1996). These models are usually calibrated using maps of observed change. These models are developed in recent years as a response to the availability of remote sensing, geographic information systems and multivariate-multitemporal mathematical models. This approach consists of analyzing land-cover conversion in relation to geographically referenced data on natural and cultural landscape variables. Both types of models, namely regression and spatial transition-based models could be used to include geographic site and situation variables in modeling change. In essence, these classes of models form a constellation of approaches which, when taken together, can be used to analyze when (Markov and logistic), why (regression) and where (spatial statistical) land-cover conversion (or modification) processes operate. The suite of empirical models can serve as a foundation upon which mechanistic and systems dynamics models can be built, the essential feature being the use of direct observations of spatial phenomena. Modellers use linear statistical models, such as logistic regression (Wear and Bolstad, 1998; Schneider and Pontius, 2001), and non-linear approaches, like artificial neural networks, because the relationships between the predictor variables and land-use change are not always linear. Generalized Additive Model (GAM) offers a non-linear statistical alternative to logistic regression (Hasite and Tibshirani, 1990). For the estimation of land-cover patters in Glacier National Park, Brown et al., (2000) implemented GAMs and found significant non-linear relationships with topographic and disturbance variables. Besides, theory of evidence (Dempster-Shafer theory) is also used for modeling land-use and land-cover changes (Hubert-Moy et al., 2001). The Dempster-Shafer theory introduces uncertainty in modeling and allows the expression of ignorance in the body of knowledge and states that belief in a hypothesis is not necessarily the compliment of its negation. The theory is based on the use of two types of information. 6. Previous Models Summarizing a large number of case studies, Agarwal et al. (2001) find that land-use change is driven by a combination of the following fundamental high level causes: -resource scarcity leading to an increase in the pressure of production on resources, changing opportunities created by markets, outside policy intervention, loss of adaptive capacity and increased vulnerability and changes in social organization, in resource access and in attitudes. Agarwal et al. (2001) review highlights as many as 19 land use change models for their spatial, temporal and human decision-making characteristics for comparing and reviewing land use change models (Fig. 2).

1. General Ecosystem Model (GEM) (Fitz et al., 1996) 2. Patuxent Landscape Model (PLM) (Voinov et al., 1999) 3. CLUE Model (Conversion of Land Use and its Effects) (Veldkamp and Fresco, 1996a) 4. CLUE-CR (Conversion of Land Use and its Effects – Costa Rica) (Veldkamp and Fresco, 1996b) 5. Area base model (Hardie and Parks, 1997) 6. Univariate spatial models (Mertens and Lambin, 1997) 7. Econometric (multinomial logit) model (Chomitz and Gray, 1996) 8. Spatial dynamic model (Gilruth et al., 1995) 9. Spatial Markov model (Wood et al., 1997) 10. CUF (California Urban Futures) (Landis, 1995) 11. LUCAS (Land Use Change Analysis System) (Berry et al., 1996) 12. Simple log weights (Wear and Bolstad, 1998) 13. Logit model (Wear et al., 1999) 14. Dynamic model (Swallow et al., 1997) 15. NELUP (Natural Environment Research Council (NERC)–Economic and Social Research Council (ESRC): NERC/ESRC Land Use Programme (NELUP) (O’Callaghan, 1995) 16. NELUP - Extension, (Oglethorpe and O’Callaghan, 1995) 17. FASOM (Forest and Agriculture Sector Optimization Model) (Adams et al., 1996) 18. CURBA (California Urban and Biodiversity Analysis Model) (Landis et al., 1998) 19. Cellular automata model (Clarke et al., 1997 Kirtland et al., 2000) Agarwal et al., 2001 describe clearly the overviews of the above mention models.

Figure 2: A Three-Dimensional Framework for Reviewing and Assessing Land Use Change Models Figure 3 is an example of the framework with the three dimensions represented together with a few general models, including some types that were reviewed by Agarwal et al. 2001. Various modeling approaches would vary in their placement along these three dimensions of complexity since the location of a land-use change model reflects its technical structure as well as its sophistication and application (Agarwal et al., 2001). The analysis that follows attempts to characterize existing land-use models on each modeling dimension. Models are assigned a level in the human decision-making dimension and their ability in the spatial and temporal dimensions are estimated as well. In addition, document and compare models across several other factors including: the model type,


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dependent or explanatory variables if any, modules, and independent variables (Agarwal et al., 2001).

Sources: Agarwal et al. 2001

Figure 3: Model Complexity and a Three-Dimensional Framework for Reviewing and Assessing Land-Use Change Models 7. Recently Developed Model The models surveyed are towards this, a series of recent papers, reports and workshops have been carried out by members of LUCC and other research community (Turner et al., 1995; Moran, 2000; Pijanowsky et al., 2002; Laurance et al., 2001; McConnell and Moran, 2001; Nepstad et al., 2001; Pontius et al., 2001, Pontius et al., 2004; Veldkamp and Lambin, 2001; Alves, 2002; Geist and Lambin, 2002; Leemans et al., 2003; Pontius and Batchu, 2003; de Nijs et al., 2004; Engelen et al., 2003; Verburg et al., 2004; Verburg and Veldkamp, 2004; McConnellet al., 2004; Pontius Jr., 2002, Pontius Jr. et al., 2004; Pontius and Malanson, 2005; Pontius and Spencer, 2005; Batty and Torrens, 2005; Brown et al., 2005; Castella et al., 2005 a and b; Pijanowski et al., 2005; Koomen et al., 2005; Pontius and Cheuk, 2006; Pontius and Lippitt, 2006; Idrisi (Clark Labs) Focus Paper 2008; Konstantinos et al., 2009). We can summarize as: 1. GEOMOD & GEOMOD 2 (Pontius Jr. et al., 2001) 2. LTM (Land Transformation Model) (Pijanowski et al., 2002) 3. SELUTH (Slope, Land use, Exclusion, Urban extent, Transportation, Hill shade) (Clarke et al.,

2003) 4. Environnent Explorer (de Nijset al., 2004) 5. CLUE-S (2005) (Verburg and Veldkamp, 2005) 5. Land Use Scanner (Koomen et al., 2005) 6. SAMBA (Castella et al., 2005 a and b) 7. Land Change Modeler – for Ecological Sustainability (Clark Labs. 2006) 8. Earth Trends Modeler (Clark Labs., 2008) 9. Multi Agent-Based Economic Landscape

(MABEL) Model (Konstantinos et al., 2009)

Pontius Jr. et al. (2001) modeled the spatial pattern of land-use change for Costa Rica using GEOMOD 2 model. Geomod is a LUCC model designed to simulate a one-way transition from one category to one other category (Pontius et al., 2001; Pontius and Malanson 2005; Pontius and Spencer 2005). The model quantifies the factors associated with land-use, and simulates the spatial pattern of land-use forward and backward in time. Schneider and Pontius Jr. (2001) modeled the land-use change in the Ipswich watershed, Massachusetts, U. S. A. using logistic regression, multi-criteria analysis and spatial filters. For visualizing alternate future scenario of the Washington, D. C. – Baltimore region Mid-Atlantic RESAC (2003) used SLEUTH (Slope, Land use, Exclusion, Urban extent, Transportation, Hill shade) – one of the cellular automata class of models. SLEUTH is a shareware cellular automata model of urban growth and land use change. The SLEUTH model is calibrated using four different methods: the traditional brute force method (Silva and Clarke, 2002), a full resolution brute force method (Dietzel and Clarke, 2004), a genetic algorithm (Goldstein, 2004), and a randomized parameter search. Land Transformation Model (2005) uses artificial neural networks to simulate land change (Pijanowski et al., 2000; 2002; and 2005). The neural net trains on an input-output relationship until it obtain a satisfactory fit between the data concerning urban growth and the independent variables. The LTM obtains a relationship between the independent variables and urban growth. Land Use Scanner (2005) is a GIS-based model that uses a logit model and expert opinion to simulate future land use patterns (Koomen et al., 2005; Hilferink and Rietveld 1999; Schotten et al., 2001). The expected quantities of changes are based on a linear extrapolation of the national trend in land use statistics from two time data. The regional demand for each land use is allocated to individual pixels based on suitability. Suitability maps are generated for all different land uses based on physical properties, operative policies, relations to nearby land-use functions, and expert judgment. The model uses data in which each pixel possesses a specific proportion of 36 possible categories. Environment Explorer (2005) is a dynamic cellular automata model, which consists of three spatial levels (de Nijs et al., 2004; Engelen et al., 2003; Verburg et al., 2004). At the national level, the model combines countrywide economic and demographic scenarios, and distributes them at the regional level. The regional level uses a dynamic spatial interaction model to calculate the number of inhabitants and number of jobs over forty regions, and then proceeds to model the land-use demands. Allocation of the land-use demands on the 500 meter grid is determined by a weighted sum of the maps of zoning, suitability, accessibility, and neighborhood potential.

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SAMBA (2005) is an agent-based modeling framework. The SAMBA team developed a number of scenarios that were discussed by scientists and local stakeholders as part of a negotiation platform on natural resources management through a participatory process combining role-play gaming and agent-based modeling (Boissau and Castella, 2003; Castella et al., 2005 a and b). The model is parameterized according to local specificities, e.g. soil, climate, livestock, population, ethnicity, and gender. CLUE-S (2005) is a spatially-explicit, multi-scale model that projects land-use change (Kok and Veldkamp, 2001; Veldkamp and Fresco, 1996; Verburg et al., 2004). CLUE (1996) is the predecessor of CLUE-S, so the two models share many philosophical approaches and computational features. CLUE-S (2005) is a fundamentally revised version of the model called Conversion of Land Use and its Effects (CLUE 1996). The CLUE (1996) model structure is based on systems theory to allow the integrated analysis of land-use change in relation to socio-economic and biophysical driving factors. Verburg et al. (2005) developed a dynamic, spatially explicit land-use change model – CLUE (Conversion of Land Use and its Effects) for the regional scale. CLUE-S is designed to work with fine resolution data where each pixel represents a single dominant land use, rather than a heterogeneous mix of various categories as in the original CLUE model (Verburg et al., 2005; Verburg and Veldkamp, 2004). CLUE-S consists of two main components. The first component supports a multi-scale spatially-explicit methodology to quantify empirical relationships between land-use patterns and their driving forces. The second component uses the results from the first component in a dynamic simulation technique to explore changes in land use under various scenarios. A combination of expert knowledge and empirical analysis usually serves for calibration. A user of CLUE-S can specify any quantity of land change based on various sectoral models. The Land Change Modeler (LCM) (Clarke lab, 2006) for ecological sustainability is a software solution designed to address the pressing problem of accelerated land conversion and the very specific analytical needs of biodiversity conservation. Land Change Modeler provides tools for the assessment and projection of land cover change, and the implications for species habitat and biodiversity. Earth Trends Modeler (Clark Labs, 2007) is a new vertical application focused on the analysis of trends and the dynamic characteristics of these phenomena as evident in image time series. Earth trends modeller allows you to view animations of series in a space-time cube format, analyze variability across varying temporal scales, extract profiles of values over time, and analyze long-term trends with a variety of techniques for trend analysis. Tools are included to examine trends in seasonality, such as phenological

change in plant species, with a newly developed procedure for seasonal trend analysis, utilize principal components analysis for the decomposition of a series into its underlying constituents, uncover characteristic patterns of variability over space-time with the empirical orthogonal teleconnection (EOT) method, explore for the presence of cycles in the series utilizing Fourier-PCA, and examine relationships between series using a linear modeling (multiple regression) tool. MABEL (2008) model is the use of sequential decision-making process simulations for base agents in our multi-agent based economic landscape. The sequential decision-making process described here is a data-driven Markov-Decision Problem (MDP) integrated with stochastic properties. Utility acquisition attributes in our model are generated for each time step of the simulation. We illustrate the basic components of such a process in MABEL, with respect to land-use change. We also show how Geographic Information Systems (GIS), socioeconomic data, a Knowledge-Base, and a market-model are integrated into MABEL. A Rule-based Maximum Expected Utility acquisition is used as a constraint optimization problem. The optimal policy of base-agents’ decision making in MABEL is one that maximizes the differences between expected utility and average expected rewards of agent actions. Finally, we present a procedural representation of extracting optimal agent policies from socio-economic data using Belief Networks (BN’s) (Konstantinos et al., 2009). 8. Conclusion Initial knowledge on extrinsic and intrinsic factors operating at different spatial and temporal scales is needful for quantifying LULC changes. In the past few decades, there is change in land use, because of the expansion of mining areas, increment in construction of dams, industrialization, urbanization etc. ,to name a few, which affect the areas as external factors. Internal changes includes shifting cultivation areas, selective logging due to human pressure on forest resources and habitat loss of wildlife due to reduction in the forests. Land-use and land-cover change is, however, poorly understood. The long-term global character, extent, and rates of changes in land cover and some land uses are known in rough outline. Uncertainty and error remain relatively high (Meyer and Turner, 1994), yet the advent of more precise and geographically referenced data on cover and use has created opportunities for improved analysis. Modeling the dynamics of land-use and land-cover change, however, has been hindered by the large variation in those dynamics across physical and social settings. Global aggregate assessments based on simple assumptions miss the target for large sections of the world, while local and regional assessments are too specific to be extrapolated to wider scales. Much work remains to be done to fill these increasingly critical gaps in understanding.


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Within reviewed models, we could not find the presence of one single model, which will fulfill all needs of LULC (land use land cover) change analyst community. Each and every model has some merits and demerits. Some models have some technical limitations (i.e., spatial interaction, temporal complexity etc.), some models were considered to have limited scope for human decision making or limited socio-economic factors, some model considered to have limited biophysical factors. We can also say that one single model cannot be sufficient for LUCC modeling that is suitable for worldwide scenario, due to regional variation of human dimension and biophysical factors. Much modeling work remains to be done to understand land use land cover changes. LUCC modeling for specific region considering regional factorial specification also needs should be continued. References Adams, D. M., R. J. Alig, J. M. Callaway, B. A. McCarl and S. M. Winnett (1996). The forest and agricultural sector optimization model (FASOM), Model structure and policy applications, USDA Forest Service Pacific Northwest Research Station Research Paper. Agarwal, C., G. M. Green, J. M. Grove, T. P. Evans and G. M. Schweik (2001). A Review and Assessment of Land-Use Change Models. Dynamics of Space, Time and Human Choice. Bloomington and South Burlington, Center for the Study of Institutions, Population and Environmental Change, Indiana University and USDA Forest Service, CIPEC Collaborative Report Series 1. Alig, R. J. (1986). Econometric Analysis of the Factors Influencing Forest Acreage Trends in the Southeast. Forest Science, 32(1), pp. 119-134. Alves, D. S. (2002). Space-time dynamics of deforestation in Brazilian Amazonia, International Journal of Remote Sensing, 4, 903-908. Anderson, J. R. (1971). Land use classification schemes used in selected recent geographic applications of remote sensing: Photogrammetric Engineering and Remote Sensing, 37(4), pp. 379-387. Avery, T. (1968). Interpretation of aerial photographs [2nd eds] Minneapolis, Burgess Pub. Co., pp. 324. Baker, W. L. (1989). A review of models of landscape change. Landscape Ecology, 2, pp. 111-133. Batty, M. and T. M. Paul (2005). Modeling and prediction in a complex world, Futures, 37(7), pp. 745-766. Bell, E. J. (1974). Markov analysis of land use change-Application of stochastic processes to remotely

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Quantitative morphometric analysis of Bilrai watershed, Shivpuri district, Madhya Pradesh using remote sensing and GIS

D.D.Sinha1, S.N.Mohapatra1 and Padmini Pani2

1Centre of Remote Sensing & GIS, SOS in Earth Science, Jiwaji University, Gwalior 2Centre for the Study of Regional Development, JNU, New Delhi, India

Email: [email protected], [email protected] and [email protected]

(Received: November 16, 2011; in final form July 18, 2012)

Abstract: Each and every River basin and watershed normally comprises a distinct morphologic region and have special relevance to drainage pattern and geomorphology. Morphometric analysis is based on certain quantities that are considered to characterize stream networks and drainage basin systems. These quantities are stream length, number of streams, bifurcation ratio, density of streams per unit drainage area, elevation difference, slope, and perimeter and area of drainage basins, etc. The surface stream patterns are usually influenced by the underlying lithology, geological structures, the topography and various hydrological factors. In the present work, IRS 1D LISS III satellite digital data and Geographic Information Systems (GIS) are used in order to calculate and accurately delineate the morphometric characteristics of Bilrai watershed with respect to the linear, areal and relief aspects. The conventional quantities mentioned above were extracted for the study area. The watershed was delineated and Strahler’s technique is used to map the Bilrai watershed and detailed morphometric analysis. The extracted values were mapped and analyzed using statistical approaches and in a GIS environment to characterize the stream networks and drainage basin systems. The morphometric analysis reveals that the basin is of fifth order and drainage pattern developed in the area are mostly dendritic in nature. The results that are obtained on the basis of stream and drainage basin analysis provide valuable information for an improved understanding of hydrological characteristics in the study area. Key Words: Morphometric, Bifurcation ratio, lithology, geomorphology, GIS 1. Introduction Morphometric studies may be purely descriptive or genetic. Attempts have been made to use the spatial analysis of geomorphic variables to analyze and interpret the landform. To study detailed morphometric characteristics of any river basin area, thorough knowledge of the nature and behavior of the surface streams in terms of quantity is a very important requisite. Basin morphology controls the basin hydrology and hence it is necessary to understand the development and evolution of the surface streams. One of the most important added advantages of quantitative analysis is that many of the basin variables derived are in the form of ratios or dimensionless numbers, thus providing an effective comparison regardless of scale. Morphometric analysis of surface-stream networks has been used to quantitatively describe stream basins with the goal of understanding their processes and evolution (Horton, 1945; Strahler, 1952, 1957, 1958, 1964; Shreve, 1967; Patton and Baker, 1976; Rodrique-Iturbe and Valdes, 1979; Abrahams, 1984; Chutha and Doodge, 1990; Wilgoose et al., 1991). Morphometric analysis using remote sensing techniques has been successfully attempted earlier by various authors (Nag and Chakraborty, 2003; Agrawal., 1998; Vittala et al., 2004; Gowd, 2004). Morphometric analysis plays an important role to understand and assess the potential sites for groundwater as well as artificial recharge zonation mapping (Sinha et al., 2012). In the prioritization of Sub-watersheds morphometric analysis has a great

significance. The morphometric parameters i.e. drainage density, stream frequency, mean bifurcation ratio, drainage texture, length of overland flow, form factor, circularity ratio, elongation ratio, basin shape and compactness coefficient are also termed as erosion risk assessment parameters and have been used for prioritizing sub-watersheds (Biswas et al., 1999). The linear parameters like drainage density, stream frequency, mean bifurcation ratio, drainage texture, length of overland flow have a direct relationship with erodibility whereas shape parameters such as elongation ratio, circularity ratio, form factor, basin shape and compactness coefficient have an inverse relationship with erodibility (Nooka Ratnam et al., 2005). 1.1 Study Area The study area Bilrai Watershed is geographically situated between 25015’54”N to 25034’54”N latitude and 78009’31” to 78018’00” E longitude covering an area of 294.167 sq. km. (Fig. 1). The area forms a part of three blocks namely Karera, Narwar and Pichore blocks of Shivpuri district of M.P. The area is drained by Bilrai River which is tributary of the Mahaur River, which ultimately joins Sind River. The entire watershed area is rural comprising of 126 thinly populated revenue villages spread over most of the area. The average annual rainfall is about 812 mm. The Bundelkhand gneissic complex constitutes the major part of area and chiefly represented by granites and gneisses. Mohapatra et al. (2009) carried out waste land inventory of the area for watershed management.



Figure 1: Location Map of Bilrai Watershed

2. Material and methods The morphometric parameters used for the analysis are calculated using ERDAS and ARC GIS software. For the morphometric analysis of the study area Survey of India topographic sheets bearing numbers 54K/2, 3 & 7 at 1:50,000 scale have been used. The interpretation of IRS 1D LISS III sensor data has been carried out

taking into consideration various image and terrain elements along with drainage pattern, stream orders and classification system. In present study area the drainages which forms Bilrai watershed are taken into consideration. Quantifiable sets of geometric properties were defined, that described the linear, areal, and relief characteristics of the watershed. The various morphometric parameters computed include stream order (U), Bifurcation ratio (Rb), Stream length (Lu), Form factor (Rf), Drainage density (D), Weighted mean bifurcation ratio( Rbw), Stream frequency (F), Length of overland flow (Lg), Constant of channel maintenance (C), Basin circulation (Rc), Basin elongation (E), Lemniscate (K), Basin relief (H), Relief ratio (Rh), Ruggedness index (HD), Dissection index (DI), and Basin area(A). The methodology for calculation of morphometric parameters adopted for the present study is tabulated in Table 1.

Table 1: Methodology for calculation of morphometric parameters Sl. Parameters Methodology

1 Stream Order Hierarchical order

Where : Lu = total stream

length of order ‘u’

Nu = total no. of stream segments of order ‘u’

Nu+1=number of segment of next

2 Stream Length(Lu) Length of Stream

3 Mean Stream Length (Lsm)

Lsm=Lu/Nu

4 Stream Length Ratio RL =Lu/Lu-1 5 Bifurcation Ratio

(Rb) Rb=Nu/Nu+1

6 Drainage density (D)

Dd=L/A

7 Drainage Textute T = Dd x Fs higher order.

L = total length of streams.

A = area of basin.

N = total number of streams.

P = perimeter of basin.

Lb = basin length.

Pi = ‘p’ value (3.14)

H1 = maxi-mum

elevation H2 = mini-mum

elevation

8 Stream frequency (Fs)

Fs=N/A

9 Length of Overland Flow

Lg = A / 2L

10 Constant channel maintenance (C)

C=A/L

11 Form factor (Rf) Rf=A/Lb2

12 Basin circulation ratio (Rc)

Rc=4�A/P2

13 Basin elongation ratio (E)

E=2�A/�/Lb

14 Lemniscate (K) K=Lb2/4A 15 Basin relief (H) H=H1 -H 2

16 Relief ratio (Rh) Rh=H/Lb 17 Ruggedness index

(HD) HD=H×Dd

18 Dissection index (DI)

H1 -H 2 / H1

3. Morphometric Analysis

The morphometric analysis of a drainage basin and its stream channel system can be better achieved through the measurement of linear aspects, aerial aspects and relief aspects of channel networks and contributing ground slopes. Each set of relationships provided useful tools with which to study the nature and behavior of stream networks. In the present area the general flow direction of river is from south to north. The results of various morphometric parameters computed are summarized in Table 2. The linear aspects were studied by using the methodology suggested by Horton (1945), Strahler (1953), Chorley et al. (1957) and Scheideggar (1965). The study of linear aspects includes, stream order, stream length, stream length ratio and bifurcation ratio. The total number of streams in the study area, digitized from SOI toposheets, are 615 and these streams are distributed as 445 in the first order, 132 in the second order, 34 in the third order, 03 in the fourth order and


one in the fifth order. However, when it is digitized from satellite data (IRS 1D LISS III), the total number of streams are 490 and the stream are distributed as 342 in the first order, 112 in the second order, 32 in the third order, 03 in the fourth order and one in the fifth order (Table 3). The major difference in first and second stream order is due to low resolution of satellite data. The stream orders of the area have been ranked according to the (Strahler, 1964) steam ordering system. The number of stream decrease as the stream order increases. The relation of number of streams against stream order of the basin shows that the number of streams of a given order forms an inverse geometric sequence by decreasing systematically with increasing order and conforms to the Horton’s (1945) law of stream numbers. In the study area the basin has negative correlation between stream number and stream order. The correlation coefficient (r) value is -0.9918 and corresponding value for goodness of a fit (r2) is 0.9837. The stream length (L) of various stream segments have been measured digitally to establish Horton’s second law of drainage. The total length of each order of stream and its mean length were also computed. The total stream length in the study area is 503.644 km and is distributed to the length of the first order 276.99 km, second order 122.45 km, third order 61.20 km, fourth order 16.23 km, fifth order 26.77 km. From the mean length, the length ratio for succeeding order of streams is calculated. The trend line plot between the stream order on X-axis and log of mean stream length on Y-axis comes out to be a linear line satisfying the Horton’s second law of stream length. The trend line plot between stream order and log of stream length is a linear line satisfying the revised law of stream length (Chorley et al., 1957), which states that “the total length of stream of each of the different orders in a drainage basin tends closely to approximate an inverse log arithmetic series”. A graphical plot of the log of the mean lengths of the stream to the order of streams shows a direct relationship up to the fifth order and the equation of the line is:

Log Y = 0.4031 X - 0.7739

where, Y is the mean length of stream and X is the order of the stream.

The values of correlation coefficient (r), and goodness of fit (r2) for mean stream length and stream order for the basin are 0.9679 and 0.937 respectively. Stream length ratio (RL) may be defined as the ratio of the mean length of the one order to next lower order of stream segment (Horton, 1945). The ratio of average of the length of stream orders to their number was noticed that increasing the number of stream order, in the first order, to second stream orders are 1.49, km, second stream orders to third stream orders 1.94 km, third stream orders to forth stream orders 3.0, fourth stream orders to fifth stream orders 4.94 km. The bifurcation ratio is indicative of shape of the basin also. An elongated basin is likely to have a high Rb, whereas a circular basin is likely to have low Rb. The mean bifurcation ratio in the area is 5.396. The weighted

mean bifurcation ratio is calculated by taking the ratios of sum of products of bifurcation ratio and total number of streams involved for each bifurcation ratio. The calculated bifurcation ratio 4.083 indicates that the basin has suffered less structural disturbances.

Table 2: Result of morphometric analysis of the area Sl. Parameters Values 1 Total no. of stream 615

2 Total length of stream (L) 503.644 km

3 Length of the basin (Lb) 33.86 km

4 Total basin area (A) 294.187 Sq.km

5 Width of the basin (W) 11.62 km

6 Perimeter of basin (P) 89.56 km

7 Drainage density (D) 1.1712 km/km2

8 Drainage texture (T) 1.1386 /km3

9 Stream frequency (Fs) 1.184 / km2

10 Length of overland flow (Lg) 0.41 km

11 Constant channel maintenance (C) 0.584 km

12 Form factor (Rf) 0.2565

13 Basin circulation ratio (Rc) 0.46

14 Basin elongation ratio (E) 0.572

15 Lemniscate (K) 0.974

16 Basin relief (H) 134 m

17 Relief ratio (Rh) 0.003

18 Ruggedness index (HD) 0.229

19 Dissection index (DI) 0.3462

Table 3: Comparison of Drainages derived from Satellite data and SOI Toposheets

Areal relationships provide useful data on the characteristics of streams as regards the basin, including the collection of rainfall and concentration of runoff, the interaction of climate and geology, and the area necessary to maintain measured units of channel length. Basin length has been given different meanings by different workers (Schumm, 1956), the basin length is the longest length of the basin and end being it’s mouth. The length of Bilrai watershed is 33.86 km. The measurement of the width is difficult because of difference the shape and boundary of the valley basin, so it is useful to take the average width which is 11.62 km. All stream flows originating in the area are being discharged through a single outlet, this constitutes the catchment area. Thus, the catchment area can be measured by calculating area enclosed by the surface water divide. The area of the basins was

Stream Order

Drainage No.

(Satellite data)

% Drainage No.

(SOI Toposheets)

%

1 342 69 445 72 2 112 22 132 25 3 32 0.065 34 0.05 4 3 0.004 3 0.004 5 1 0.001 1 0.001

Total 490 615


measured with the help of software. The Bilrai watershed area of the basin calculated as 294.167 sq. km. and perimeter as 89.56 km. Areal parameters analysed are form factor, drainage density, drainage texture, stream frequency, length of overland flow, constant channel maintenance, basin elongation ratio, basin circulation ratio and lemniscate. The form factor of the watershed is calculated as 0.256. Amount of vegetation and rainfall absorption capacity of soils, which influences the rate of surface run-off, affects the drainage texture of an area. The Semi-arid regions gave finer drainage density texture than humid region. Low drainage density result in the areas of highly resistant or permeable subsoil material, dense vegetation and low relief (Nag and Chakraborty, 2003). High drainage density is the result of weak or impermeable subsurface material, sparse vegetation and mountainous relief. Low drainage density leads to coarse drainage texture while high drainage density leads to fine drainage texture. The drainage density is calculated as 1.712 km/km2 indicating low drainage density. Drainage texture (Rt) is one of the important concepts of geomorphology which refers to the relative spacing of drainage lines. Drainage lines are numerous over impermeable areas than permeable areas. Smith (1935) has classified drainage density into five different textures. The drainage density less than 2 indicates very coarse, between 2 and 4 is related to coarse, between 4 and 6 is moderate, between 6 and 8 is fine and greater than 8 is very fine drainage texture. In the present study, the drainage density is of very coarse to coarse drainage texture. The value of stream frequency in the present study area is 1.184/km2. It depends upon distribution of rainfall pattern, rock and soil types and relief. The value of stream frequency indicates that the basin possesses a low relief area. According to Horton (1945), the length of overland flow (Lg) is one of the most important independent variables affecting both the hydrologic and physiographic development of drainage basins. In the area under investigation the length of overland flow value is 0.41 km. The value shows that the river basin is of flat surface and of gentle slope and the rain water has to run over this distance before getting concentrated in respective stream channel. The constant (C) is expressed as km2/km, and depends upon not only the rock type and permeability, climatic regime, vegetation cover and relief, but also the duration of erosion and climatic history. The constant is low in area of close dissection. The value of constant channel maintenance is 0.584 km. A circular basin is more efficient in the discharge of run-off than an elongated basin, the values of basin elongation generally vary from 0.6 to 1.0 over a wide variety of climatic and geologic types. Values close to 1.0 are typical of regions of very low relief, whereas values in the range 0.6–0.8 are usually associated with high relief and steep ground slope (Strahler, 1964). The varying shapes of drainage basins can be classified with the help of the index of elongation ratio, i.e. circular (0.9–1.00), oval (0.8–0.9), less elongated (0.5–0.7), elongated (0.5–0.7) and more elongated (0.5).

In the study area the basin elongation ratio is 0.572 which indicates an elongated basin. Miller (1953) has given the values of circulatory ratio as 0.6 to 0.7 in the homogeneous geologic material to preserve geometrical symmetry. The basin circulation of the Bilrai watershed is 0.46 which indicates the elongated nature of the basin. The area is characterised by high to moderate relief and the drainage system, is structurally controlled. Chorley et al. (1957) have used the term lemniscate or pear shape, which defines precisely the shape of the basin. The calculated average lemniscate value for the basin is 0.974.

Relief relationships provide information regarding differential elevations within the basin and corresponding elevational organization of various stream segments. Though relief calculations may be extremely involved due to their complicated three-dimensional aspect, they have been effective in quantitatively describing successive phases of landscape evolution (Rodrique-Itrube and Valdes, 1979). The relief parameters analysed includes basin relief, relief ratio, ruggedness index, dissection index. Basin relief is calculated on the basis of the highest and lowest elevations and the data of relative relief so derived is 134m indicating that it is moderately high relative relief in elevation. There is direct relationship between the relief and channel gradient. There is also a correlation between geomorphological characteristics and the relief ratio of a drainage basin. The relief ratio normally increases with decreasing drainage area and size of the given drainage basin (Schumm 1956). The values of relief ratio is 0.004. The lower values may indicate the presence of basement rocks that are exposed in the form of small series and mounds with lower degree of slope. Ruggedness index (HD) is a dimension less quantity unit to combine the qualities of slope, steepness and length. Generally the ruggedness value range from as low as 0.06 in subdued low relief (plain area) to over 1.0 in mountain ranges or in badlands or on weak clays. The ruggedness index calculated for the basin is 0.229. Dissection index is also used as morphometric determinant of the stage of cycle of erosion where in old, mature and young stages are related to dissection indices of less than 0.1 (less than 10 per cent), 0.1–0.3 ( 10%–30%) and more than 0.3 (above 30%) respectively. The dissection index is observed that 0.346, belongs to dissection index category of above 30% and is indicative of the stage of cycle of erosion.

4. Conclusion

Morphometric analysis of Bilrai watershed with shape, relief and the characteristics of river draining network has been studied and classified. The elongated shape of the basin, which is extending from the south to north reduces the risks of floods. Lengths of the stream orders are increased due to different geomorphological changes with geological and structural formation. The increase in the river drainage network in the first stream orders of the basin and the short distances of these stream orders help the increase in water


denudation and erosion in the basin. The dendritic type of river drainage represents the most parts of the basin because of the rocky granite gneiss formations. The drainage morphometric parameters estimated for the study are is in coherence with the nature of the terrain encountered in the study area. The primary use of the estimation of these drainage morphometric parameters is to have clear idea about the terrain feature of the area under consideration. Remote Sensing coupled with GIS techniques have proved to be an efficient tool in drainage delineation and their updation. In spite of low relief, low drainage density values indicate that the area is classified as coarse and gentle slope. Circulatory and elongation ratios show that the area has elongation in shape. The morphometric parameters like linear, aerial and relief using GIS is found to be of immense utility for the present study.

References

Abrahams, A.D. (1984). Channel networks: A geomorphological perspective, Water Resources Research, Vol. 20, pp. 161-168.

Agrawal, C.S. (1998). Study of drainage pattern through aerial data in Naugarh area of Varanasi district. U.P. Jour. Indian Soc. Remote Sensing, 26 (4), 69 -175.

Biswas, S., S. Sudhakar and V.R. Desai (1999). Prioritization of sub-watersheds based on morphometric analysis of drainage basin, district Midnapore, West Bengal. Indian Soc. Remote Sensing, Vol. 27(3), pp.155-166.

Chorley, R.J., E.G. Malm Donald and H.A. Pogorzelski (1957). A new standard for estimating drainage basin shape, Am. J. Sci., Vol. 255, pp.138-141.

Chutha, P. and J.C.I. Doodge (1990). The shape parameters of the geomorphic unit hydrograph, Journal of Hydrology, 117, 81-97.

Gowd, S.S. (2004). Morphometric analysis of Peddavanka basin, Anantapur district, Andhra Pradesh, India, Journal of Applied Hydrology, xv11(1), 1-8. Horton, R.E. (1945). Erosional development of streams and their drainage basin, Hydrophysical approach to quantitative morphology, Bull. Geo. Soc. Am. Vol. 56. pp. 270-375.

Nag, S.K. and S. Chakraborty (2003). Influence of rock types and structures in the development of drainage network in hard rock area, Indian Soc Remote Sensing, 31(1), 25-35. Nooka Ratlam, K., Y.K. Srivastava, V. Venkateshara Rao, E. Amminedu and K.S.R. Murthy (2005). Check dam positioning by prioritization of micro-watersheds using SYI model and morphometric analysis - Remote Sensing and GIS perspective, Indian Soc. Remote Sensing, Vol. 33(1), pp.25-38.

Mohapatra, S.N., D.D Sinha and P. Padmini (2009). Wasteland inventory and watershed area management using remote sensing and GIS in a hard rock terrain, In New Technologies For Rural Development having Potential of Commercialization, edited by J.P. Shukla, Allied Publishers Pvt. Ltd. ISBN : 978-81-8424-442-7, pp. 16-21.

Patton, P.C. and V.R. Baker (1976). Morphometry and floods in small drainage basins subject to diverse hydrogeologic controls, Water Resources, Vol.12, pp. 941-52.

Rodrique-Itrube, I. and J.B. Valdes (1979). The geomorphologic structure of hydrologic response, Water Resources, Vol. 15, pp. 1409-20.

Schumm, S.A. (1956). Evolution of drainage system and slope in badlands at Perth amboy, New Jersey. Natl. Geol. Soc. America. Bull. Vol. 67, pp.597 – 646.

Shreve, R.L. (1967). Statistical law of stream numbers. J. Geol, 74 (1): 17-37.

Sinha, D.D., S.N Mohapatra and P. Padmini (2012). Mapping and Assessment of Groundwater Potential in Bilrai Watershed (Shivpuri District, M.P.) – A Geomatics Approach, Indian Society of Remote Sensing, Springer (http://dx.doi.org/10.1007/s12524-011-0175-2 / DOI 10.1007/s12524-011-0175-2, published online.

Smith, K.J. (1935). Relative relief of Ohil. Geographical Review. Vol. 35, pp. 272.

Strahler, A.N. (1952). Hypsometric, (area attitude) Analysis of erosional Topography. Bull, Geol. Soc. Am. Vol. 63, pp. 1114 -1142. Strahler, A.N. (1957). Quantitative analysis of watershed geomorphology. Amer. Geophys. Union Trans, Vol.38, No .6, pp. 913-920. In: Schumm, H.S. (Ed). Drainage basin morphometry. Benchmark papers in Geology. Vol. 41. Strahler, A.N. (1958). Dimensional analysis applied to fluvially eroded landforms, Geological Society of America Bulletin. Vol. 69, pp. 279-299. Strahler, A.N. (1964). Quantitative geomorphology of drainage basin and channel networks, In: V. T. Chow (Editor) Handbook of applied Hydrology, Mc Graw Hill Book Co. New York, pp. 4-76. Vittala, S., G.S. Srinavasa and Honnegowda (2004). Morphometric analysis of sub-watersheds in the Pavagada area Tumkur district, south India using remote and GIS techniques Journal of the Indian society. Remote Sensing. 32, 235 - 251. Wilgoose, G., R.L. Bras and I. Rodriguez-Iturbe (1991). A coupled channel network growth and hillslope evolution model: 2. Nondimensionalization and applications, Water Resources, Vol. 27, pp. 1685-1696.

Journal of Geomatics 109 ��Vol.6 No.2 October 2012

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Web-GIS based monitoring of vegetation using NDVI profiles

Shashikant A. Sharma and Shweta Mishra Space Applications Centre (ISRO), Ahmedabad


(Received: April 20, 2012; in final form August 24, 2012)

Abstract: The NDVI has found a wide application in vegetative studies as it has been used to estimate crop yields, crop rotation, drought monitoring and forest biomass among others. It is often directly related to other ground parameters such as percent of ground cover, photosynthetic activity of the plant, surface water, leaf area index and the amount of biomass. NDVI has much usage due to its simplicity and widespread familiarity. Global MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Comparing current year’s NDVI data with the previous-years average reveals whether the productivity in a given region is typical, or whether the plant growth is significantly more or less productive. In order to assess, visualize and analyze NDVI images, a web-GIS based software has been developed using open source like Java, MapGuide and PostgreSQL. It is possible to know that in which district/taluka/grid and which duration the crop is deviating from the average. The software helps the user to get not only an overall picture of the country with regard to condition of vegetation over the cropping season but also warns against drought and other severe conditions. Key Words: NDVI, Vegetation Index, Crop Condition, MODIS, Web GIS, Open Source Software 1. Introduction The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that uses the visible and near- infrared (NIR) bands of the electromagnetic (EM) spectrum, and is adopted to analyse remote sensing measurements and assess whether the target being observed contains live green vegetation or not. NDVI has found a wide application in vegetative studies as it has been used to estimate crop yields, drought monitoring and forest biomass among others. It is often directly related to other ground parameters such as percent of ground cover, photosynthetic activity of the plant, surface water, leaf area index and the amount of biomass. Study of NDVI profile gives a dynamic assessment of crop vigor and crop condition. 2. Normalized Difference Vegetation Index (NDVI) When sunlight strikes objects, certain wavelengths of electromagnetic spectrum are absorbed and some of the wavelengths are reflected. Chlorophyll, the pigment in plant leaf, strongly absorbs visible light (from 0.4 to 0.7 μm) for use in photosynthesis. The cell structure of the leaves, on the other hand, strongly reflects near-infrared light (from 0.7 to 1.1 μm). The more leaves a plant has, the more these wavelengths of light are affected. The satellite data acquired in visible and NIR, is natural to exploit the strong differences in plant reflectance to determine their spatial distribution in these satellite data. Subsequent work has shown that the NDVI is directly related to the photosynthetic capacity and hence energy absorption of plant canopies. Generally, healthy vegetation will absorb most of the visible light that falls on it and reflects a large portion of the NIR. Unhealthy or sparse vegetation reflects relatively more visible light and less NIR light. Bare

soils on the other hand reflect moderately in both the red and infrared portion of the EM spectrum. Since we know the behavior of plants across the EM spectrum, we can derive NDVI information by focusing on the satellite bands that are most sensitive to vegetation information (NIR and red). The bigger the difference therefore between the NIR and the red reflectance, the more vegetation there has to be. The NDVI is defined as: NDVI = (NIR-Red) / (NIR+Red) It is a traditional vegetation index used by researchers for extracting vegetation abundance from remotely sensed data. It divides the difference between reflectance values in the visible red and near-infrared wavelengths by the overall reflectance in those wavelengths to give an indicator of green vegetation abundance (Tucker, 1979). NDVI values are represented as a ratio ranging from-1.0 to 1.0 but in practice extreme negative values represent water, values around zero represent bare soil and values above 0.3 represent green vegetation. It has much usage due to its simplicity and widespread familiarity. Some of the more common applications may include global agricultural monitoring and forecasting, forest management, land-use planning, land cover characterization and change detection. Multi-temporal NDVI obtained from sensors onboard IRS satellites has been used for crop yield estimation (Rajak et al., 2005). 3. MODIS NDVI Data Moderate Resolution Imaging Spectro-radiometer (MODIS) vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Red (0.645 μm) and NIR (0.858 μm) reflectances are used to determine the MODIS



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daily NDVI. These are computed from atmospherically corrected bi-directional surface reflectances that have been masked for water, clouds, heavy aerosols and cloud shadows. Global MOD13A1 data are provided every 16 days at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection (ftp://e4ftl01.cr.usgs.gov/MOLT/MOD13A1.005/). Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. The accuracy of this product has been assessed over a widely distributed set of locations and time periods via several ground-truth and validation efforts. Images from MODIS also offer potential to precisely quantify crop health through an improved characterization of plant chlorophyll, photosynthesis, and biomass amount. The improved spectral resolution of MODIS serves for creation of more precise vegetation indices, which have been widely used to measure and predict crop yields (Oza et al., 2010, Sakamoto et al., 2005). The specified spectral ranges are in accordance with well known remote sensing algorithms applied to estimate vegetation properties (Gitelson, 2004).

4. Rationale

Satellite remote sensors can quantify what fraction of the photosynthetically active radiation (PAR) is absorbed by vegetation. It is proved that net photosynthesis is directly related to the amount of PAR that plants absorb. In short, the more a plant is absorbing visible sunlight (during the growing season), the more it is photosynthesizing and the more it is being productive. Conversely, the less sunlight the plant absorbs, the less it is photosynthesizing, and the less it is being productive. Either scenario results in an NDVI value that, over time, can be averaged to establish the "normal" growing conditions for the vegetation in a given region for a given time of the year. In short, a region’s absorption and reflection of PAR over a given period of time can be used to characterize the health of the vegetation there, relative to the normal. Comparing current year’s NDVI data with the previous-years average reveals whether the productivity in a given region is typical, or whether the plant growth is significantly more or less productive. NDVI based customised application are developed for drought monitoring (Rao et al., 2012) and web based crop condition related information is also available from various websites of FAO and NRSC (www.fao.org, www.dsc.nrsc.gov.in). However, there is no web based tool available to view multi-temporal NDVI profiles and maps at district/taluka/grid level.

FASAL is a national level project for forecasting crop production for major crops and monitor crop condition (Oza and Parihar, 2006). In order to provide this information to a large number of users who do not have data, can analyse crop condition using this website. Here, one can easily visualize and anlayse which area and during which duration the crop is deviating from the average. 5. Methodology MODIS NDVI data of 9 tiles (each of size 4°X4°) covering India, from January 1, 2006 to till date was acquired and mosaics were prepared. Per year, 23 images of 16-day-NDVI-composites were layer stacked. The mosaic in sinusoidal projection was converted into LCC projection as per NNRMS standards. A 5X5 km grid cover for India (~ 0.13 million grids), generated for FASAL project was used for overlay and analysis. District / taluka / grid boundaries were overlaid and average NDVI beneath these regions were calculated. These average values of NDVI of all pixels falling within administrative boundaries or grid was then added as attributes in the vector layer using IGiS software (www. scanpointgeomatics.com). Also, agriculture mask prepared from landuse/landcover map of National Resources Census at 1:250000 scale was overlaid to get the NDVI values of only agriculture pixels. The overall methodology is described in figure 1. The processing and updation of the latest NDVI profiles, maps and images on this web site are made available within an hour. 6. Database and software Database was designed for efficient handling of raster, vector and tabular data. For this application, PostgreSQL database is used for storing NDVI information. For each year, district, taluka and grid NDVI information, separate tables are created and designed in third normalized form (3NF). Database normalization is the process of removing redundant data from the tables in order to improve storage efficiency, data integrity and scalability. Here database is designed in 3NF so there is no transitive dependency and all non–primary key attributes are mutually independent and it meets all the requirements of the first and second normal form. Also new data can be easily updated without affecting entire database. Currently, NDVI data from 2006 to till date is available for MODIS Sensor. Entity Relationship Diagram (ERD) of the database is shown in figure 2. ERD is a visual representation of different data using conventions that describe how these data are related to each other. Data can be easily retrieved from database on user demand on the basis of district / taluka / grid code as primary key and joining different tables required.


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Figure 1: Methodology adopted for vegetation monitoring using NDVI profiles Kava chart is used for showing NDVI profile in interactive manner. Kava Chart tools provide robust well tested components that easily translate numbers to graphics with minimal effort. It is implemented in Java and the charting tools can be used from within HTML pages and Java applications.

Figure 2: ER diagram showing the entity relationship

of the database

Apart from this, a web-GIS based tool was developed for visualization of latest NDVI profile using Web Mapping Service (WMS). WMS provides a simple HTTP interface for requesting geo-registered map images from one or more distributed geospatial databases. A WMS request defines the geographic layer(s) and the response to the request is one or more geo-registered map images (returned as PNG) that can be displayed in a browser application. Here AutoDesk MapGuide is used as a map server which is an open source software that provides WMS capability. As a result, dynamic web layout is generated for displaying maps containing Web-GIS enable district, taluka and grid level NDVI maps, and ‘deviation of NDVI from average’ map in Web browser using Web Server Extensions API. Latest NDVI raster image is also served in the background. It has basic web-GIS functionalities of pan, zoom, identify, select, layers on/off etc. 7. Results and discussion For visualization and analysis of NDVI images, 16-day-NDVI-composites of MODIS data from 2006 to till date were layer stacked and overlaid state/district/taluka boundaries and average NDVI of these regions was calculated. Later, agriculture mask was overlaid to get the NDVI values of only agriculture pixels falling within the administrative boundaries. One can then visualize current period district/taluka level NDVI profiles together with previous years profiles and gives a qualitative comparison with respect to previous years and their on-the-fly calculated average profile. An illustrative case of a 5X5 km grid of Bathinda district (Punjab) is shown in figure 3. It also allows viewing spatial distribution at district and taluka level data for the country showing current NDVI, cumulative NDVI and ‘deviation of NDVI from average’.

Figure 3: NDVI profile of a 5X5 km grid of Bathinda

district (Punjab) and NDVI map of North India overlaid with district boundaries

A provision has also been made to superimpose various vector layers such as transportation (road, rail

Generation of District / Taluka / Grid level

Database Management

(Updation/ Backup / Restore)

Select State /District/Taluk

NDVI Dat

NDVI Map

Download MODIS Data (2005 to till date)

Reprojection and Layer Stacking

Date Range

View Map

Visualization and Analysis (Web GIS)

User Input

Agriculture Mask

Select Grid

View Profile


�

�

etc.), water bodies viz. rivers, reservoirs, lakes, ponds, canals etc., and settlements as per user’s choice. Web-based software developed using open source software is to visualize district/taluka/grid level NDVI profiles together with one or more previous years NDVI profiles and their average profile. NDVI and ‘deviation of NDVI from average’ can be visualised for the country. It gives a qualitative comparison of crop / vegetation as compared to previous years and average profile.

Figure 4: NDVI profile and ‘NDVI-deviation-from-average’ map for Rabi season 2010-2011 of India

The software is useful to study the patterns of crop condition over a growing period. It is also useful to visualize cumulative NDVI profile for current season with respect to previous year cumulative NDVI profile for comparison. It helps the user to get not only an overall picture of the country with regard to condition of vegetation over the cropping season but also warns against drought and other severe conditions. As shown in figure 4, the current season’s profile of 2010-11 (shown in thick green colour) of Sawai Madhopur district (Rajasthan) is higher than the previous years profile and their average profile (shown in black colour). The crop condition in Rajasthan was better as compared to previous years. 8. Conclusion NDVI is a good indicator of crop growth and vigor. NDVI profile using MODIS multitemporal data is very useful to know the crop condition during its growth period. This web-based software, which can be accessed by a large number of users, gives current season’s NDVI profile, together with previous year’s profiles and their average profile. It can be conveniently used for assessing crop growth and its condition relative to previous years. It is a very useful tool for detecting onset of drought and also gives a spatial distribution of relative crop condition in the country.

Acknowledgements

Authors acknowledge the guidance, encouragement and support received from Dr J.S. Parihar, Deputy Director, EPSA, SAC, Dr. Sushma Panigrahy, Group Director ABHG, SAC and Dr. Ajai, Group Director MPSG, SAC for providing all support to carry out this study.

References

Gitelson, A. (2004). Wide dynamic range vegetation index for remote quantification of geophysical characteristics of vegetation. J. Plant Physiol. 161, 165-173. Oza, M.P., H.P.Bhatt, S.P.Vyas and N.K.Patel (2010). Spectral profile approach for wheat yield modeling using MODIS data. Journal of Geomatics, 4(1), 37-42. Parihar, J.S. and M.P. Oza (2006). FASAL: An integrated approach for crop assessment and production forecasting. Proceeding SPIE 6411 Agriculture and Hydrology Applications of Remote Sensing, 641101 (December 11, 2006); doi:10.1117/12.713157. Rajak, D.R., M.P. Oza, N. Bhagia and V.K. Dadhwal (2005). Spectral wheat growth profile in Punjab using IRS WiFS data. J. Indian Soc. Remote Sensing, 33(2), 345-352. Rao, Ram Mohan, K. Kant, S.K. Saha, I. Dodikhudoev and P.S. Roy (2012). A customized GIS application to create and analyze drought characteristics using Geoinformation techniques. Int. J. of Geoinformatics, 8(2), 13-24. Sakamoto, T., M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka and H. Ohno (2005). A crop phenology detection method using time-series MODIS data. Remote Sensing of Environment, Vol. 96, No. 3-4, 366-374. Tucker, C. J. (1979). Red and Photographic Infrared Linear Combinations for Monitoring Vegetation, Remote Sensing of Environment, 8:127-150. www.fao.org/giews/english/index.html

www.fao.org/geonetwork/srv/en/main.home

www.dsc.nrsc.gov.in/DSC/index.jsp

www.scanpointgeomatics.com/igis.html

ftp://e4ftl01.cr.usgs.gov/MOLT/MOD13A1.005/


Geo-spatial technology based landslide vulnerability assessment and zonation in Sikkim Himalayas in India

L.P.Sharma1, Nilanchal Patel2, M.K.Ghose3 and P. Debnath4

1National Informatics Centre, Sikkim, India 2Department of Remote Sensing, Birla Institute of Technology Mesra, Ranchi, India

3Department of Computer Science, Sikkim Manipal Institute of Technology, Mazitar, Sikkim, India 4College of Agriculture Engineering and Post Harvest Technology, Ranipool, Sikkim, India


(Received: December 20, 2011; in final form July 22, 2012)

Abstract: The research presented in this paper employs a simple yet straightforward deterministic technique that is based on the rationale that the landslide vulnerability of an area would be determined by the aggregation of the landslide densities of the various causative factors present therein. The landslide density of a causative factor is computed as the ratio of the frequency of the landslides to the area covered by the causative factor. In the present study, the thematic layers for fourteen important causative factors that were further sub-divided into a total of 39 sub-categories were collected and intersected in the GIS environment that resulted in the division of study area into a number of spatial units. The landslide inventory layer prepared through the satellite map and the field survey was also intersected in the spatial data set. Landslide density was then computed for each of the sub-categories of the landslide causative parameters. Landslide Information Value (LSIV) for each individual spatial unit was determined by summing up of the landslide densities of the different causative factors present in individual spatial unit. The LSIVs determined for the various spatial units of the entire study area were grouped into five zones of landslide vulnerability based on natural breaks (Jenks) technique. The research was carried out in the Rumtek-Samdung Study area in the Central Himalayas located in the Sikkim state in India. The zonation map prepared through this technique showed significant amount of agreement with the field occurrences of landslides that is further ascertained by the prevalence of a Vulnerability Assessment Accuracy of 80%. Keywords: Landslides, Vulnerability, Zonation, GIS, Causative Parameters, Landslide Density. 1. Introduction

Landslide is a major cause of disaster in the hilly region. In the state of Sikkim 3300 lives are recorded to have been lost causing a widespread state of terror in the mindset of the people when a prolonged monsoon triggered landslides in October 1968. Since then sporadic landslides in all parts of the state have disturbed and damaged lives and properties heavily. Mud slides and debris flows are usually the two types of landslides that have occurred due to heavy and prolonged downpour during the monsoon season. Whenever such landslides have occurred, the manmade structures and natural resources stand no where against the fragile condition of the Himalayan ecosystem, ubiquity of weak geology and slope instability with average monsoon rainfall of 350 mm per day rising up to 500 mm during cloud burst. There has been little effort put till date within and around the study area to scientifically assess the vulnerability with reference to the existing geo-technical information to predict the occurrence of such hazard that could help and support the disaster management authority to work towards disaster reduction strategies like early warning system, vacating of most vulnerable areas, stoppage of civil construction in the vulnerable areas, retrofitting of the structures lying in such areas and so on, and at the same time identifying safe zones for continuation of various sustainable development processes like industrialization and further urbanization (Sharma et

al., 2009). The present study employs Geographical Information System based analysis to delineate the study area into different categories in terms of its vulnerability to landslides. GIS based landslide vulnerability study have been attempted by many researchers and scientists in the past all across the globe (Shivakumar and Mukesh, 2002; Varnes, 1984; Wadge, 1988; Van Westen et al., 1997; Uromeihy and Mahdavifar, 2000; Patanakanog, 2001; Pachauri et al., 1998; Dhakal and Siddle, 2002; George et al., 2007; Carro et al., 2003; Lee, 2007; Gupta et al., 2009; Wang and Unwin, 1992; Pachauri and Pant, 1992; Sakellario and Ferentinou, 2001). Several statistical models have also been developed and employed for performing such vulnerability studies. Logistic Regression Model was used for landslide mapping by Atkinson and Massari (1998), Lee (2005) and Ramakrishan et al. (2005) along with Frequency Ratio method (Lee and Sambath 2006) and Information Value method (Ramakrishna et al., 2005). Fuzzy Algebraic Function was used for landslide susceptibility modeling by Lee (2007), Ercanoglu and Gokceoglu (2004) and Pistocchi et al. (2002). Artificial Neural Network was used for landslide susceptibility study and modeling by Lee et al. (2003a, 2003b, 2004), Pradhan and Lee., (2009) and many others. Knowledge driven raster analysis for landslide study has been conducted by Gupta et al. (2009). Landslide vulnerability assessment and zonation in Sikkim Himalayas was conducted employing the concept of Shannon’s entropy by



Sharma et al. (2010a). In the same study area, landslide vulnerability assessment and zonation was carried out based on the soil characteristics as well (Sharma et al., 2010b). In the present study, we have determined the landslide vulnerability of the various causative factors based on their landslide density which is computed as the ratio of the number of polygons with landslides occurring within the respective causative factors to the area covered by the latter. The Landslide Information Value (LSIV) for the individual polygon was calculated by summing up the landslide densities of the various causative factors present within a polygon. The LSIVs determined for the different polygons present in the study area were categorised into five different classes of vulnerability.

Figure 1: Landslide at Chisopani 2. The Study Area

The present study area Rumtek Samdung is characterized by worst landslide occurrences (Fig 1) .The area falls under the East Sikkim of India and covers a sloppy stretch of hills (Fig 2) Lingdum village in the eastern part to Chanday village in the western side. The elevation range in the area varies from 400 meters at Singtam to 4000 meters around Sang village. The area is generally characterized by steep and moderate steep slopes with coarse loamy soil associated with high hydraulic conductivity and severe erosion characteristics. Landslides are common features during the monsoons almost in every part of the study area. The stretch of National Highway 31A from Singtam to 9th mile near Ranipool lies within the study area. This stretch of national highway is dangerous for landslides and road blockades during the monsoon During the monsoons the roads are blocked

sometimes even for twenty four hours and the travelers need to manage either by transshipment or through the alternate routes. The road from Singtam to Sirwani that connects the eastern Sikkim to southern Sikkim is also highly vulnerable to landslides and gets blocked every monsoon. The rural landmass in the villages around Sirwani, Tumen, Chalamthang and Chispoani have witnessed various types of major and minor slides during monsoon that have disturbed the people living in the areas with some of the houses being washed away at frequent intervals.

Figure 2: The Study Area 3. Landslide Inventorization

A landslide inventory map was generated for the study area with the help of satellite images (Quick Bird and Cartosat) comprising a total of 69 big and small landslides that occurred in the study area during 2005 - 2007 that were digitized as polygons using ArcGIS (Figure 3).

Figure 3: Landslide Inventory Map


4. Methodology

The different causative factors of landslides identified in the study area are rock types (Pachauri and Pant, 1992), soil surface texture, soil depth texture, soil erosion behavior (Samra and Sharma, 2002), soil drainage behavior, soil depth, soil stoniness, soil hydraulic conductivity (Bianca and Nelson, 2004), land use and land cover, distance to road, distance to river, slope (Roth, 1983) and foliation. Table 1 depicts details of the data used for this study with their source and resolution. The analyses performed to carry out the task of vulnerability zonation comprise the following steps. First, the spatial layers pertaining to the fourteen causative parameters with their 39 sub-categories identified in the study area were intersected in ArcGIS along with the landslide inventory layer that was developed with the help of satellite images and field survey that resulted in the formation of 78256 numbers of polygons out of which 614 polygons were associated with landslides of the past. Second, the landslide density for each sub-category was calculated by dividing the number of polygons associated with landslides by the total area covered by the respective sub-categories within the study area. Table 2 depicts the computation of the landslide densities for the sub-categories pertaining to the various pedologic parameters where as the computation of the landslide densities of the various sub-categories corresponding to the different non-pedologic parameters are shown in Table 3. For example, moderate deep type of soil occupies 26.82 sq km area and contains 199 polygons with landslides. Hence its landslide density was computed as (199/26.28 = 7.572). A higher value of landslide density of a sub-category indicates its higher vulnerability to landslides while a lower value of landslide density of a sub-category renders it to be less vulnerable to landslides. In the next step, for each polygon, the Landslide Information Value (LSIV) was determined by summing up the landslide density of the various sub-categories present within the polygon. The LSIVs determined for the various polygons present in the entire study area were categorized into five classes of landslide vulnerability based on the natural breaks (Jenks) technique available with ArcGIS. These categories are named as the Least vulnerable, Low vulnerable, Moderately vulnerable, Highly vulnerable and the Most vulnerable in the order of their increasing vulnerability to landslides. Based on the ranges of LSIV in the different vulnerability zones a Landslide Vulnerability Zonation map was prepared. Finally prediction accuracy of the proposed technique was determined by calculating the percentage of the polygons with landslides occurring in the higher three vulnerability zones out of the total number of landslide containing polygons in the entire study area. The flow chart depicting the methodology adopted to perform the research presented in this chapter is shown in Fig 4.

Figure 4: Methodology Flowchart

Table 1: List of Spatial Data Used Sl. No.

Names of Thematic Layers

Resolution/ Scale of Map

Data Source

1 Slope Map 1:25,000 DEM/25K Toposheets

2 Land Use & Land Cover Map

1:50,000 Digitized from Cartosat Pan Image

3 Geological Map 1:50,000 Geological Survey of India, Gangtok, Sikkim, India

4 Soil Map 1:50,000 National Bureau of Soil Survey and Land Use Planning, India.

5 Road Map 1:50,000 Digitized from Cartosat Pan Image

6 Drainage Map 1:50,000 Digitized from Toposheets

7 Toposheets 1:25000 Rural Management Development Department, Govt. of Sikkim, Gangtok, India

8 Cartosat Pan Image

2.5 m National Remote Sensing Centre, Hyderabad, India.

9 Quick Bird Image

60 cm Wikimapia (www.wikimapic.org)

10 Landslide Events Map

1:10,000 Developed from GPS Survey & Wikimapia image.


Table 2: Computation of Landslide Densities for Pedologic Parameters

Soil Parameter

s

Sub- categories

Area (sq. km)

No. of Polygons

with Landslides

Landslide Density

Soil Depth Moderate Deep(75-100

cm)

26.28 199 7.572

Moderate Shallow(50-75

cm)

0.87 0 0.000

Shallow (<50cm)

66.78 415 6.214

Drainage Character

istics

Well Drained 42.66 200 4.688

Some What Excessively

Drained

51.27 414 8.075

Excessively Drained

0.004 0 0.000

Texture Fine Loamy 49.16 320 6.509

Loamy 28.5 187 6.561

Coarse Loamy 16.28 107 6.572

Hydraulic Conductiv

ity

Low 49.16 320 6.509

Moderate 28.5 187 6.561

High 16.28 107 6.572

Stoniness High 0 0 0.000

Moderate 19.87 123 6.190

Low 74.06 491 6.630

Erosion Character

istics

Low 0 0 0.000

Moderate 68.81 395 5.740

Severe 25.12 219 8.718

Surface Texture

Fine Loamy 0 0 0.000

Loamy 70.71 419 5.926

Coarse Loamy 23.22 195 8.398

Table 3: Computation of Landslide Densities for Non-Pedologic Parameters

Non-Pedologic

Parameters

Sub-categories

Area (sq. km)

No. of Polygons

with Landslides

Landslide Density

Geology Lingtse Gneiss

9.9 76 6.387

Chungthang Sub-Group

0 0 0.000

Chlorite Phyllite/ Schist, Sericite

82.03 538 6.559

Foliation Yes 0.71 2 2.817 No 92.22 612 6.636

Road in 40 Metres

Buffer

Yes 13.52 91 6.731 No 79.41 523 6.586

Slope (%) 0-15 36.54 91 2.490 15-30 15.6 95 7.372 30-45 17.55 141 8.034 45-60 10.57 95 8.988 >60 13.68 172 12.573

Land Use Dense 43.28 228 5.268 and Land

Cover Forest Open Forest

1.63 31 19.018

Scrub Land

0 0 0.000

Cultivable/Barren Land

49.02 355 7.242

Drainage in 30

Metres Buffer

Yes 21.03 163 7.751 No 71.9 451 6.273

Among the pedologic parameters, the highest landslide density is associated with the sub-category viz. severe erosion characteristics (8.718) while the sub-categories exhibiting landslide density value of greater than 7 are namely coarse loamy surface texture, somewhat excessively drained drainage characteristics and moderately deep soil. On the other hand, the lowest landslide density is associated with the sub-category viz. well-drained drainage characteristics (4.688) with slightly higher landslide density values exhibited by sub-categories representing the loamy surface texture and moderate erosion characteristics (Table 2). Among the non-pedologic parameters, there occurs considerable variation between the lowest landslide density (2.817) and highest landslide density (19.018) (Table 3). The highest landslide density is exhibited by the open forest sub-category while the lowest landslide density is associated with the foliation. Slope also exhibits significantly high landslide density of 12.573. These observations are also corroborated during the field survey.

1. Calculation of Landslide Information Value (LSIV) Based on Landslide Density Based on the Landslide Density determined for the various sub-categories, the Landslide Information Value (LSIV) for the jth polygon is determined as follows.

LSIVj= ijLDn

i�1

where LDij represents the Landslide Density of the ith sub-category in the jth

polygon (j = 1 to 78256).

27.577 25.91522.421

13.872

4.14

0

10

20

30

LeastVulnerable

Low Vulnerable ModeratelyVulnerable

HighlyVulnerable

Most Vulnerable

Are

a(s

q.km

.)

Figure 5: Bar Chart Showing the Area (sq. km.) of Different Vulnerability Zones


The Landslide Information Value computed for the different polygons is found to range between 73.316 and 123.298. The ranges of LSIV determined for the five vulnerability categories based on the natural breaks (Jenks) technique are shown in Table 4 and the area under each range is depicted in figures 5 - 7 in form of bar chart. The zonation map generated based on the LSIV ranges in different vulnerability categories is shown in Figure 8. 5. Computation of Vulnerability Assessment Accuracy The areas vulnerable to landslides are characterized by high occurrences of landslides. Looking at the five vulnerability zones classified on the basis of grouping of the Landslide Information Values derived from the density of landslides in the area covered by the individual sub-categories, it can be said that the three vulnerability zones namely moderate, high and most vulnerable zones can be expected to have maximum number of landslides. In optimal situation all the landslides of the past should have occurred in these three zones only. Hence we computed Vulnerability Assessment Accuracy (VAA) as the percentage of landslides in these three zones. Hence VAA is computed as :

TlMoVZLHVZLMVZLVAA 100*)}()()({

614100*}163183189{

VAA

=79.64%80% where 189, 183 and 163 are the landslide polygons in moderate, high and most vulnerable zone and 614 are the total number of landslide polygons in the study area. 6. Results and Discussion

The various statistics of the different classes of landslide vulnerability are presented in Table 3. The statistics comprise the LSIV range, area and its percentage, number of polygons with landslides along with the percentage value, and landslide density pertaining to the different vulnerability zones respectively. Fig 6 shows the percentage of area and percentage of landslides while the landslide density is shown in form of bar chart in Fig 7. The landslide vulnerability zonation map is shown in Fig 8. From the analyses of the Table 3 and Figures 6 and 7, the following observations are made. The maximum numbers of polygons with landslides occur in the moderately vulnerable zone while the maximum area is covered by the least vulnerable zone. The most salient observation that stems out from the examination of the table is the continuous increase in the landslide density values from the least vulnerable zone to the most

vulnerable zone. However, another prominent finding is that the landslide density in the most vulnerable zone alone is greater than the sum of the landslide densities in the remaining four lower vulnerable zones. The efficacy of the landslide density based vulnerability classification technique employed in the present investigation is proved by the occurrence of significantly high prediction accuracy of 80%.

Table 4: Statistics of the Different Vulnerability Classes

Sl. No.

LSIV Ranges

Area (sq. km.)

No. of Polygons

with Landslides

Landslide Density

Vulnerability Zones

1 73.316 - 84.787

28.4 (30%)

42 (7%) 1.48 Least Vulnerable

Zone 2 84.78701

- 91.226 23.76 (25%)

83 (14%) 3.49 Low Vulnerable

Zone 3 91.22601

- 96.24 21.88 (23%)

189 (31%) 8.64 Moderately Vulnerable

Zone 4 96.2401

- 102.412

14.02 (15%)

137 (22%) 9.77 Highly Vulnerable

Zone 5 102.412

- 123.298

5.86 (6%)

163 (27%) 27.82 Most Vulnerable

Zone Total 93.92

(100%) 614

(100%)

30%25% 23%

15%6%7%

14%

31%22%

27%

0%

10%

20%

30%

40%

Least Low Moderate High Most

Vulnerability Zones

Perc

enta

ge

%Area% Landslides

Figure 6: Bar Chart Showing Percentage of Area and of Landslides in Different Vulnerability Zones

Bar Chart for Landslide Densities in Different Zones

1.48 3.498.64 9.77

27.82

0.005.00

10.0015.0020.0025.0030.00

Least Low Moderately Highly Most

Vulnerability Zones

Land

slid

e D

ensi

ty

Figure 7: Bar Chart Showing the Number of Landslides under Different Vulnerability Zones


Figure 8: Landslide Vulnerability Zonation Map

7. Conclusions The research presented in this paper is based on a simple and straightforward deterministic technique that makes it versatile for performing the task of landslide vulnerability assessment and zonation. The technique uses the landslide density of the various causative sub-categories present within the individual polygons as a potential indicator of the landslide vulnerability of the respective polygons. The Landslide Information Value (LSIV) of an individual polygon was determined by summing up the landslide densities of the various sub-categories present within the respective polygon. The LSIVs computed for the various polygons in the study area were categorised into five classes of vulnerability such as the Least Vulnerable, Low Vulnerable, Moderately Vulnerable, Highly Vulnerable and Most Vulnerable respectively. The prediction accuracy for the landslide vulnerability determined from this technique was found to be 80 percent which can be considered to be significantly high in terms of the simplicity of the technique employed. References Atkinson, P.M and R. Massari (1998). Generalized linear modeling of susceptibility to land sliding in the central Apennines, Italy Comput Geo-science, 24, pp 373-385

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Ramakrishna, D.M.K. Ghose, R. Vinu Chandra and A. Jeyaram (2005). Probabilistic techniques, GIS and remote sensing in landslide hazard mitigation: a case study from Sikkim Himalayas, India. Geocarto International, 20(4), pp 53-58. Roth, R.A. (1983). Factors affecting landslide susceptibility in San Mateo County, California. Bull Assoc Eng Geol 20(4), pp 353-372. Sakellariou, M.G. and M.D. Ferentino, (2001). GIS-based estimation of slope stability. Natural Hazards Review 2(1), pp 12-21.

Samra, J.S. and Sharma U.C. (2002). Soil Erosion and Conservation. Fundamental of Soil Science, Indian Society of Soil Science, IARI, New Delhi, India, p 162.

Sharma, L.P., N. Patel, M.K. Ghose and P. Debnath (2009). Geographical Information System Based Landslide Probabilistic Model with tri-variate approach - A case study in Sikkim Himalayas. 18th United Nation’s Regional Cartographic Conference-Asia and the Pacific, Bangkok 26-29 October 2009. Sharma, L.P., N. Patel, M.K. Ghose, and P. Debnath (2010a). Influence of Shannon’s entropy on landslide-causing parameters for vulnerability study and zonation—a case study in Sikkim, India. Arab J Geoscience 5(3), pp 421-431. Sharma, L.P., N. Patel, M.K. Ghose and P. Debnath (2010b). Assessing landslide vulnerability from soil characteristics-A GIS based analysis. Arab J Geoscience 5(4), pp 789-796. Sharma, L.P., N. Patel, M.K. Ghose and P. Debnath (2011). Landslide vulnerability assessment and zonation through ranking of causative parameters based on landslide density-derived statistical indicators. Geocarto International 26(6): 491-504. Sivakumar, G.L. and M.D. Mukesh (2002). Landslide analysis in Geographic Information Systems. Department of Civil Engineering, Indian Institute of science, Bangalore. India. www.gisdevelopment.net/application/natural_hazards/landslides/ nhls009pf.htm. Accessed on 03.03.2011. Uromeihy, A. and M.R. Mahdavifar (2000). Landslide hazard zonation of Khorshrostam area, Iran. Bull Eng Geol Environ. 58, pp 207-213. Van Westen, C.J, N. Rengers, M.T.J Terlien and R. Soeters (1997). Prediction of the occurrence of slope instability phenomena through GIS-based hazard zonation. Geol Rundsch 86, pp 404-414. Varnes, D.J. (1984). Landslide hazard zonation: a review of principles and practice. UNESCO, Paris, pp 1-55. Wadge, G. (1988). The potential of GIS modeling of gravity flows and slope instabilities. International Journal of Geographical Information Systems 2, 143-152. Wang, Shu-Quiang and D.J Unwin (1992). Modeling landslide distribution on loess soils in China: an investigation. International Journal of Geographical Information Systems, 6 (5).


Inventory and change detection of wetlands in Barak Valley, Northeast India: A remote sensing and GIS approach

Anwarul Alam Laskar1 and Parag Phukon2

1Indian Statistical Institute, North-East Centre, Tezpur - 784 028, India 2Department of Geological Sciences, Gauhati University, Guwahati - 781 014, India

Email: [email protected] ; [email protected]

(Received: November 12, 2011; in final form May 5, 2012)

Abstract: Barak Valley comprising the three districts of South Assam, viz, Cachar, Hailakandi and Karimganj, is endowed with numerous wetlands dotting the alluvial plain. From the viewpoint of their origin they can be divided into Riverine and Palustarine, where the former group is directly related to the cut-off processes of the rivers and the later group is formed as a result of water logging. Study of six temporal data sets reveals that the palustarine wetlands show large variation in the water-spread area with time while riverine wetlands show lower rate of change for the same duration. The variation in water-spread area of the palustarine wetlands is because of their shallow nature, high rate of sediment generation and dispersal from the adjoining hills as well as agricultural activities along the fringe areas. The overall water-spread area and density show a declining trend without much shifting in their position and thus show shrinkage in the wetland inventory of the region. Keywords: Wetland, Barak valley, change detection, riverine, palustarine 1. Introduction The Barak Valley, a contiguous region of three South Assam districts viz, Cachar, Hailakandi and Karimganj, is endowed with numerous wetlands spread in the floodplain as well as slightly elevated alluvial plains developed in the inter-montaine topographic lows (Figure 1, Figure 2). These wetlands have been traditionally linked with the socio-economic life of the inhabitants. Some of them are directly linked to the cut-off processes of the river regime (Riverine) and as such represent the abandoned channels which are locally known as ‘anua’. Another set of wetlands, usually shallower than the former group are mostly developed due to blocked drainage and water logging (Palustarine) within the vast tract of the alluvial plain. These type of wetlands are locally known as ‘haors’. Many of these wetlands are more than 1km2, prominent among them are Bakri haor, Chatla haor etc. These wetlands have a great influence on the socio-economic condition of more the 2.6 million population of Barak Valley. Wetlands are lands transitional between terrestrial and aquatic eco-systems where the water table is usually at or near the surface or the land is covered by shallow water. Wetlands can be broadly divided into two categories - coastal and inland. Inland wetlands fall into four general categories, viz, marsh, swamp, fen and bog. Marshes are those wetlands which are dominated by soft stemmed vegetation. Swamps are dominated by woody plants. Fens are fresh-water, peat-forming wetlands covered mostly by grasses, sedges, reeds and wild flowers. Bogs are old fresh-water wetlands, often formed in old glacial lakes characterized by spongy peat deposits, evergreen trees and shrubs and a floor covered by thick carpet of

sphagnum moss indicating poor water circulation and poor nutrient. Remote sensing and GIS assisted study of the spatial distribution of wetlands and their spatio-temporal distribution is done by many workers throughout the globe (Ghosh et al., 2004; Ower et al., 2007; Hui et al., 2008; Ruan et al., 2008; Bhaskar et al., 2010). Ower et al, (2007) have mapped the spatio-temporal change of wetland north of Lake George, Uganda using multi-temporal Landsat data. Ruan et al. (2008) have assessed the change detection of wetlands in Hongze Lake in the Northern Jiangsu Province of China using three sets of Landsat data. Hui et al. (2008) have mapped the spatial temporal change of Poyang Lake waterbody and the temporal process of water inundation of marshlands using multi-temporal Landsat imagery. In the present study an attempt is made to study the spatio-temporal distribution and variation of the wetlands of Barak Valley. Analysis of the distribution and temporal variation of the wetlands, both riverine and non-riverine is done in a GIS environment taking Survey of India (SoI) topographic map and satellite image as primary spatial data source. 2. Geomorphological setup Barak Valley exhibits a unique landscape of ridge and valley with low lying hills and intervening valleys. The area is bound to the north by Barail range, to the east by Bhuban and Manipur hills, to the south by Mizoram fold belt and to the west by the Bangladesh plains. On an average the elevation increases towards north and east reaching 800-1000m above mean sea level (amsl). The Barail form the highest hill range found in north with elevation more than 1000m amsl. The low lying hills of the southern part are subject to Late Quaternary



geomorphic processes leading to the formation of minor erosional as well as depositional landforms. Barak is the main river draining the area which runs from east to west across the regional structural trend. All the major tributaries follow the prevailing structural trend and join the trunk channel orthogonally. A number of wetlands of various dimensions dotting the area is also one of the most important geomorphological feature of the region. Some of these wetlands are of riverine origin while many of these are formed as a result of water logging.

Figure 1: Distribution of wetlands in Barak valley based on IRS P6 LISS-3 satellite data

Figure 2: IRS P6 LISS-3, FCC (2006) image showing major wetland areas in Barak valley. Clusters of small wetlands viz, Chatla Haor, Bakri Haor etc. become large, single sheet of water during monsoon which are shown in Figure 1 as waterlogged areas 3. Database and methodology Six spatial datasets (Table 1) belonging to 1918, 1965, 1988, 1999, 2003 and 2006 were used to assess the change of wetlands. At first all the datasets were

georeferenced and brought into a common projection system. All these datasets were then overlaid to assess their spatial accuracy.

Table 1: Details of data (primary and secondary) used

Data type Year of survey/

Date of acquisition

Scale / Resolution

Survey of India (SoI) Topographical maps

1918 1:63,360 1965 1:50,000

Sate

llite

Dat

a

Landsat TM (Multispectral) 10.11.1988 30m

Landsat ETM+ 19.12.1999

30m (multispectral)

15m (panchromatic)

IRS 1D PAN 27.02.2003 5.8m IRS 1D LISS-III 24.03.2003 23.5m IRS P6 LISS-III 06.02.2006 23.5m

The lentic wetlands as well as the water logged areas for six different years (1918, 1965, 1988, 1999, 2003 and 2006) are captured from different spatial data sources. The data sources used are Survey of India topographic maps of scale 1:50000 and 1:63360, IRS P6 LISS-3 images of 23.5 m spatial resolution and Landsat images of 30 m spatial resolution resolution. All the spatial datasets were brought into common datum and projection for understanding the temporal variations in morphometric analysis of each wetland. Base information was taken from 1:50000 scale topographical maps and delineated by superimposing satellite data of different years. This process enabled to understand temporal variation in each wetland. These wetlands were then classified into two categories based on their origin, viz, Riverine and Palustarine (Garg, 2004). These two types of wetlands were identified based on their pattern and association. Further analyses of twelve major wetlands have been done in order to identify their spatio-temporal variation in detail. Density maps for each set of the data considering a 4km×4km grid have also been computed. 4. Result and discussion From the study it is observed that the total area covered under lentic wetlands bodies during 1918 was 36.5 km2 with an average density of 1.2 ha km-2 and maximum 37.4 ha km-2 (Table 2). In 1965 there was a decline in the wetland area (34.2 km2) with the average density of 1.1 ha km-2 (Table 2). In 1988 total wetland area increased to 52.4 km2 with average and maximum density of 1.8 and 35.9 ha km-2 respectively. This increase could be attributed to seasonal fluctuation in rainfall pattern in the catchment of Barak River. The total areas covered by wetlands are 42.2, 27.0 and 26.6 square kilometres respectively during 1999, 2003 and 2006. Accordingly the average density varies from 1.8 in 1988 to 1.4, 0.9 and 0.8 in 1999, 2004 and 2006 respectively while maximum density for the same period varies from 35.9 to 32.0, 23.9 and 22.5.


Although the two types of spatial data i.e. SoI topographic maps and satellite image show variable results but individual group shows gradual decline in water-spread area (Table 2). Further, the wetland area extracted from the satellite image also show a declining trend (Figure 3). From the study of water body density maps prepared for all the six temporal data sets it is seen that there is a gradual decrease in density without any major shift in their pattern and position (Figure 4). There is an overall decline in wetland area of the region for about 88 years from 1918-2006. The decline in the area extent of the wetlands can be associated with the continued siltation by the river system during high flood levels, fluctuating monsoonal rainfall and agricultural activity along the fringe of the wetlands. Rainfall data from the study area however do not show

significant declining trend in the annual rainfall pattern (Figure 5). Thus, the changes in the wetland area and density cannot be attributed to the rainfall.

Figure 3. Changes in wetland area during 1988-2006

Figure 4: Spatio-temporal variability of the wetland density (1988-2006)


0

200

400

600

800

1000

1200

1400

1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

mm

of r

ainf

all

Table 2: Change in areal extent of wetland

Data Type Season Wetland Area (km2)

Water-logged Area (km2)

Total Water-spread Area

(km2)

Average Density

(ha km-2)

Maximum Density

(ha km-2)

SoI Topographic map 1918 36.5 -- -- 1.2 37.4 1965 34.2 -- -- 1.1 25.2

Satellite Data

Nov. 1988 52.4 193.2 245.6 1.8 35.9 Dec. 1999 42.2 142.6 184.8 1.4 32.0 Mar. 2003 27.0 126.2 153.2 0.9 23.9 Feb. 2006 26.6 121.1 147.7 0.8 22.5

Figure 5: Monthly rainfall at Silchar for the period between 1977 and 1997 In all 114 major wetlands were selected using 1965 topographical maps. This is presented in a tabular format (Table 3). The present status of wetland extent was demarcated and studied from satellite data 2006. Out of 114 wetlands 15 show an increase in wetland area statistic and 47 have been reduced in size while 43 of these have been disappeared. Twelve large wetlands were taken for a detailed study of their variation in area over time. The area for each of these wetlands during 1965, 1988, 1999, 2003 and 2006 are plotted in a graph and it is seen that there is a declining trend for all the wetlands with an exception

of a few which show variable or increasing trend (Figure 6).

The palustarine wetlands (marshes, swamps and bogs) within the alluvial plain are formed as a result of water logging and are supported by marshy vegetation. Results clearly indicate that there is a marked variation in aerial extent of palustarine wetlands. Whereas riverine wetlands extent which are formed as a result of fluvial activity show little variation in their areal extent. The reason for this is because of the fact that palustarine wetlands are shallow and have very low slope. A slight variation in the water level has an extensive effect on the areal extent of the water-spread. Further these are subjected to siltation from the nearby hills composed of very soft easily detachable sand, silt and clay. Siltation of these wetlands makes them shallower causing more prone to areal variation. Shallowing of these wetlands lead to decrease in their water bearing capacity which ultimately results in expansion of their areal extension.

On the other hand riverine wetlands which have a well defined and steep banks show less variation in water-spread area. These being much deeper, have little effect on their water bearing capacity because of siltation. Further, siltation in these wetlands is much lower as it happens only during high over-bank flood which is very rare.

Table 3: Wetland inventory of the study area

Sl.No. Name of the wetland Area in 1965 (ha)

Area in 2006 (ha)

Most recent trend in size

Wetland Co-ordinates

1 Duba Bil* 15.65 31.0 Increasing 92.51E:24.93N 2 Kharumara Bil* 16.90 13.7 Decreasing 92.52E:24.96N 3 Pukhara Bil* 7.11 12.0 Increasing 92.52E:24.96N 4 Mahichatal Bil* 39.32 17.3 Decreasing 92.56E:24.93N 5 Lara Bil* 9.59 0.0 Disappeared 92.52E:24.92N 6 Nanua Bil* 82.52 49.6 Decreasing 92.55E:24.91N 7 Lukka Bil* 57.76 30.0 Decreasing 92.54E:24.89N 8 Hinggarguri Bil* 8.53 6.0 Decreasing 92.55E:24.89N 9 Bar Bil** 13.46 5.8 Decreasing 92.60E:24.89N

10 Ghatta Bil* 21.74 12.8 Decreasing 92.57E:24.90N 11 Raua Bil* 19.98 9.8 Increasing 92.57E:24.91N 12 Duba Bil* 18.66 6.8 Increasing 92.61E:24.93N 13 Saudia Bil* 7.20 6.9 Decreasing 92.61E:24.92N 14 Chhatadari Bil* 23.24 9.5 Decreasing 92.57E:24.93N


15 Andurua Bil* 30.10 35.0 Increasing 92.59E:24.93N 16 Arua Bil* 10.29 8.6 Decreasing 92.61E:24.94N 17 Janwarmara Bil* 23.01 4.4 Decreasing 92.61E:24.96N 18 Karkuri Bil* 63.25 30.3 Decreasing 92.59E:24.97N 19 Garwala Bil* 4.82 0.0 Disappeared 92.62E:24.91N 20 Duba Bil* 30.10 31.2 Increasing 92.62E:24.91N 21 Charal Bil* 11.05 0.0 Disappeared 92.64E:24.90N 22 Dhubria Bil* 4.81 0.0 Disappeared 92.64E:24.91N 23 Jurkuni Bil* 6.00 2.2 Decreasing 92.66E:24.89N 24 Ramnagar Anua Bil** 70.21 36.8 Decreasing 92.64E:24.85N 25 Anua Bil** 46.33 57.9 Increasing 92.65E:24.84N 26 Anua Bil** 36.48 39.4 Increasing 92.66E:24.82N 27 Tapang Bil* 36.57 29.1 Decreasing 92.66E:24.74N 28 Mokam Bil* 42.45 11.6 Decreasing 92.77E:24.71N 29 Alua Bil* 12.25 0.0 Disappeared 92.76E:24.71N 30 Chinkuri Bil* 15.38 26.2 Increasing 92.75E:24.80N 31 Malin Bil* 7.95 0.0 Disappeared 92.79E:24.82N 32 Dhubri Bil* 1.50 0.0 Disappeared 92.78E:24.82N 33 Chiru Bil** 6.32 0.0 Decreasing 92.77E:24.82N 34 Anua Bil** 28.23 20.7 Decreasing 92.76E:24.83N 35 Dharam Bil** 8.46 2.0 Decreasing 92.77E:24.84N 36 Haltuli Bil* 30.29 9.2 Decreasing 92.76E:24.88N 37 Khangra Bil** 15.17 16.0 Increasing 92.72E:24.89N 38 Anua Bil** 10.24 6.0 Decreasing 92.72E:24.91N 39 Dalu Bil* 41.74 45.3 Increasing 92.79E:24.92N 40 Kharil Bil* 34.57 18.8 Decreasing 92.80E:24.85N 41 Harin Jhil* 11.12 0.0 Disappeared 92.88E:24.61N 42 Benga Jhil* 6.24 0.0 Disappeared 92.89E:24.61N 43 Puni Haor* 13.09 14.5 Increasing 92.87E:24.64N 44 Anua Bil** 21.63 33.9 Increasing 92.88E:24.75N 45 Algapur Anua Bil** 40.96 32.2 Decreasing 92.89E:24.77N 46 Anua Bil** 12.55 10.2 Decreasing 92.85E:24.80N 47 Hatichara Bil* 21.73 23.5 Increasing 92.85E:24.93N 48 Silghat Anua Bil** 12.21 5.9 Decreasing 92.91E:24.79N 49 Banskandi Anua Bil** 38.59 31.0 Decreasing 92.92E:24.81N 50 Rupair Anua Bil** 76.09 74.9 Decreasing 92.93E:24.78N 51 Dharam Bil** 5.31 8.4 Increasing 92.94E:24.78N 52 Isa Bil* 32.55 28.1 Decreasing 92.93E:24.75N 53 Bara (Borapati) Bil * 19.06 0.0 Disappeared 92.95E:24.76N 54 Rangir Bil** 2.64 0.0 Disappeared 92.92E:24.74N 55 Dhubri Bil* 15.13 0.0 Disappeared 92.95E:24.71N 56 Arala Bil* 32.23 0.0 Disappeared 92.90E:24.68N 57 Singara Bil* 18.28 0.0 Disappeared 92.98E:24.73N 58 Jora Bil* 5.13 0.0 Disappeared 92.97E:24.74N 59 Maukuri Bil* 4.46 0.0 Disappeared 92.97E:24.74N 60 Chiri Anua Bil** 17.96 17.9 Decreasing 92.95E:24.80N 61 Rana Bil* 9.44 8.4 Decreasing 92.58E:24.92N 62 Baghemara Bil* 5.09 0.0 Disappeared 92.66E:24.90N 63 Padhoa Bil* 2.07 0.0 Disappeared 92.65E:24.90N 64 Raua Bil* 103.10 53.6 Decreasing 92.68E:24.88N 65 Anua Bil** 28.60 30.9 Increasing 92.63E:24.87N 66 Upar Bil* 21.81 0.0 Disappeared 92.78E:24.71N 67 Jugirguri Bil* 7.15 0.0 Disappeared 92.71E:24.85N 68 Kangra Bil** 9.33 3.9 Decreasing 92.70E:24.85N 69 Hakaiti Bil** 7.53 10.2 Increasing 92.51E:24.87N 70 Angang Bil** 39.71 13.4 Decreasing 92.51E:24.86N 71 Medal Bil* 96.37 37.0 Decreasing 92.54E:24.83N 72 Sorgul Bil* 7.11 4.9 Decreasing 92.55E:24.84N 73 Deochara Bil* 73.21 0.0 Disappeared 92.56E:24.85N


74 Naidal Bil* 4.48 0.0 Disappeared 92.51E:24.82N 75 Piajura Bil* 2.00 0.0 Disappeared 92.50E:24.80N 76 Bawa Bil* 74.16 23.6 Decreasing 92.58E:24.64N 77 Singari Bil* 12.48 8.9 Decreasing 92.59E:24.64N 78 Kaiya Bil* 25.67 0.0 Disappeared 92.60E:24.65N 79 Chorgul Bil* 26.43 20.8 Decreasing 92.60E:24.66N 80 Jogirkuri Bil* 3.93 0.0 Disappeared 92.60E:24.68N 81 Chepti Bil* 8.42 2.0 Decreasing 92.60E:24.70N 82 Boyali Bil* 6.37 0.0 Disappeared 92.58E:24.70N 83 Chubal Bil* 46.00 0.0 Disappeared 92.55E:24.73N 84 Kalchakuri Bil* 15.97 0.0 Disappeared 92.58E:24.75N 85 Dhalikuri Bil* 1.78 0.0 Disappeared 92.57E:24.75N 86 Kalkakuri Bil* 6.47 22.9 Increasing 92.59E:24.82N 87 Gopira Bil** 15.56 17.7 Increasing 92.63E:24.85N 88 Anua Bil** 13.44 14.5 Increasing 92.61E:24.84N 89 Charchirua Bil** 4.94 0.0 Disappeared 92.61E:24.83N 90 Bakri Haor* 19.93 0.0 Disappeared 92.61E:24.82N 91 Pitala Bil* 25.98 30.7 Increasing 92.62E:24.82N 92 Furia Bil** 2.26 0.0 Disappeared 92.63E:24.83N 93 Duburia Bil** 3.06 0.0 Disappeared 92.63E:24.83N 94 Bala Bil* 26.14 16.4 Decreasing 92.62E:24.81N 95 Baia Bil* 5.53 0.0 Disappeared 92.63E:24.81N 96 Barakali Bil* 30.40 9.1 Decreasing 92.62E:24.79N 97 Bhatlata Bil* 1.55 0.0 Disappeared 92.61E:24.79N 98 Mirjamara Bil* 6.45 0.0 Disappeared 92.62E:24.79N 99 Sandoli Bil* 27.36 45.8 Increasing 92.63E:24.78N

100 Badirkuri Bil* 4.95 0.0 Disappeared 92.62E:24.78N 101 Karas Ala Bil* 9.12 0.0 Disappeared 92.62E:24.77N 102 Padma Bil* 4.22 3.9 Decreasing 92.61E:24.77N 103 Kalnakuri Bil* 3.52 1.4 Decreasing 92.61E:24.75N 104 Tikuni Bil* 5.70 0.0 Disappeared 92.64E:24.75N 105 Chengkuri Bil* 1.60 0.0 Disappeared 92.60E:24.74N 106 Dubri Bil* 14.42 10.6 Decreasing 92.64E:24.73N 107 Hinglai Bil* 2.72 0.0 Disappeared 92.62E:24.64N 108 Saukonia Bil* 12.96 43.6 Increasing 92.66E:24.81N 109 Balsuri Bil* 29.50 0.0 Disappeared 92.89E:24.63N 110 Hauti Bil* 65.31 49.0 Decreasing 92.55E:24.90N 111 Patan* 84.21 41.8 Decreasing 92.67E:24.77N 112 Barachatta* 17.24 9.3 Decreasing 92.63E:24.81N 113 Mokachapari Bil* 5.65 11.9 Increasing 92.81E:24.73N 114 Mokachapari Bil* 5.83 0.0 Disappeared 92.81E:24.73N

*Palustarine wetlands **Riverine wetlands 5. Conclusion The vast alluvial tract of Barak Valley dotted with numerous wetlands has been studied from the satellite images and SoI topographic maps spanning over 88 years from 1918–2006, for the spatio-temporal distribution and variation of the wetlands. The study reveals that those wetlands which are formed as a result of water logging and blocked drainage are subject to much variation in their water spread area. On the other hand those formed as a result of cut-off processes of the rivers (ox-bow lakes) show relatively less variation. Based on only dry season satellite data it is observed that overall water-spread area and wetland density show a declining trend without much shifting in their positions and thus show

shrinkage in the wetland inventory of the region. The differential spatio-temporal variability of these two wetland categories reflects the variation in sediment generation in the region. The riverine wetland dotting the floodplain area are subjected to siltation mainly through overbank flooding which is not a recurrent process and the embankment system largely control the silt within the confines of the channels. Contrary to this there is high rate of sediment generation in the low lying hills composed of friable sandstone and shale which find their way into the wetlands of the palustrine category which are largely distributed adjacent to these hills. Further, agricultural activities along the fringe areas of the wetlands also contribute to generation of silt contributing to the overall sediment budget of these wetlands. This


study shows a general trend of shrinking wetlands due to natural processes (sedimentation) coupled by anthropogenic activities in the surrounding areas of wetlands.

Figure 6: Temporal variation of water-spread area of major wetlands Acknowledgement The authors are thankful to the two reviewers for constructive suggestions which helped to improve the manuscript.

Reference Bhaskar, B. P., U. Baruah, S. Vadivelu, P. Raja and D. Sarkar (2010). Remote sensing and GIS in the management of wetland resources of Majuli Island, Assam, India. Tropical Ecology, 51, pp.31-40. Garg, J. K. (2004). Wetlands and geomatics-An overview. ISG Newsletter, 10, pp.34-45. Ghosh, A. K., N. Bose, K. R. P. Singh and R. K. Sinha (2004). Study of spatio-temporal changes in the wetlands of North Bihar through remote sensing. International Soil Conservation Organisation Conference – Brisbane, pp.1-4. Hui, F., B. Xu, H. Huang, Q. Yu and P. Gong (2008). Modelling spatial-temporal change of Poyang Lake using multitemporal Landsat imagery. International Journal of Remote Sensing, 29, 5767-5784. Ower, M., A. Muwanga and W. Pohl (2007). Wetland change detection and inundation north of Lake George, Western Uganda using Landsat data. African Journal of Science and Technology, 8, 94-106. Ruan, R., Y. Zhang and Y. Zhou (2008). Change detection of wetland in Hongze Lake using a time series of remotely sensed imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII, pp.1545-1548.


Chlorophyll variability in the Arabian Sea and Bay of Bengal during last decade (1997-2007)

M. Shah1, N. Chaturvedi2, Y. T. Jasrai1 and Ajai2

1Botany Department, Gujarat University, Ahmedabad 380015, India 2Marine and Planetary Science Group, Space Applications Centre (ISRO), Ahmedabad - 380 015, India


(Received: May 23, 2012; in final form September 05, 2012)

Abstract: The variability in spatial and temporal distribution of Chlorophyll-a (Chlorophyll) and Sea Surface Temperature (SST) in the Arabian Sea (AS) and Bay of Bengal (BOB) is studied using satellite data. For chlorophyll analysis Sea-Viewing Wide Field-of-View sensor (SeaWiFS) derived eight days average Chlorophyll (mg m-3) images from 1997 to 2008 have been used. The SST data derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) were used to study the SST pattern. In order to understand the spatial and temporal distribution of Chlorophyll in the AS and the BOB, few representative locations were analysed to evaluate the change in pattern from 1997-2007. During the study few Open Ocean locations were observed, where Chlorophyll remains high consistently throughout the study period. Five representative locations were identified and analysed, four in the AS (Southwest AS) and one in BOB (Northeast BOB). The time series analysis has been done for all these location to understand the intra-seasonal, inter-seasonal, and decadal variability. Chlorophyll pattern shows in general high values during February to March in the AS and November to December in the BOB for all the years studied. The Chlorophyll values in general observed high, during 2007 – 08 as compared to 1997 – 98. The Chlorophyll values in the Northwest AS were of the order #1.2 mg m-3 in the years of 2006-2008, as compared to ~0.8 mg m-3 during 1997-1999. The seasonality pattern is almost similar during the time span, but the duration of high productivity period has prolonged in recent years (2007-08. This has high relevance for the study of climate change/biogeochemical cycle analysis. Key words: Chlorophyll, SST, Arabian Sea, Bay of Bengal, Remote Sensing 1. Introduction Phytoplankton plays an active part in the control of atmospheric carbon dioxide through absorption in the process of photosynthesis, which partially affects the ocean surface heating and light penetration. In Open Ocean areas high productivity (Chlorophyll-a) is mainly connected with phytoplankton biomass. The changes in light intensity and temperature affect the nutrient availability leading to seasonal change in Chlorophyll concentration. The seasonal variation in mixed layer dynamics and upwelling is an important determinant of annual cycle of primary productivity in the Arabian Sea (Banse, 1987). The AS is a unique geographic area known for its high seasonally oscillating biological primary productivity resulting from monsoon driven circulations (Nair et al., 1999). Nutrient limitation interacting with changes in light intensity and temperature can lead to seasonality in primary and secondary production. Chaturvedi and Narain. (2003). studied chlorophyll variability in the Arabian Sea using SeaWiFS data, on intra-annual and inter-annual basis and observed that the seasonal periodicity is evident. It is known, that in the tropics there is sufficient light and heat throughout the year, so that there are continual small rises and falls of population of zooplankton and phytoplankton and nutrient levels interact. However, the intra-annual variability in chlorophyll at different locations shows differences in the relationship with SST. The Arabian Sea showed an inverse relationship at most of the locations, while a positive relationship was observed in

the northwest region during October to December and an inverse relationship during January to April, while Bay of Bengal showed positive relationships of northeast locations (Chaturvedi, 2005). There are also changes from year to year in plankton population. These can be linked to changes in the climate, through winter mixing variability, temperature and light availability. A basin-specific response of phytoplankton to large-scale climate oscillators has been studied by Martinez et al. (2009) in Pacific Oceans. In the present study Chlorophyll variability in the AS and BOB is analyzed on spatial and temporal basis. Changing pattern from 1997 to 2007 is also evaluated. Interrelationship with SST variability has been analyzed to understand the Chlorophyll and SST pattern. 2. Material and Methods 2.1. Chlorophyll concentration The Sea-Viewing Wide Field-of-View sensor (SeaWiFS) derived processed data has been used. These Chlorophyll images were generated by using Ocean Color- 4 (OC4) algorithm (O’Reilly et al., 1998). Eight days average, chlorophyll images have been analysed. This is level-3 global girded product with 9 km resolution. The Standard Mapped Images (SMI) generated from SeaWiFS data has been used. The cloud free data that is October-April has been used



for the period 1997-98 and 2007-08. The SeaWiFS and SIMBIOS projects maintained a local repository of in situ bio optical data, known as SeaBASS (SeaWiFS Bio-optical Archive and Storage System) to support and sustain their regular scientific analysis. Chlorophyll values, which were taken at certain location in the AS and BOB, reasonably come in the limits, while compared to satellite derived data (Desa et al., 2001). Werdell et al. (2003) have discussed in detail the validation of chlorophyll on a global basis. To study the seasonal variation in chlorophyll distribution pattern in the AS and BOB few Open ocean locations were identified and examine on intra-annual and inter-annual basis and evaluate the change in chlorophyll distribution pattern during the last decade. To understand the spatial and temporal distribution of Chlorophyll in the AS and BOB, five representative locations were identified at near coast, offshore and open ocean waters (Table 1). The Open Ocean locations were studied where high Chlorophyll is seen consistently throughout the study period (Figure 1). Table.1: Five High Productive locations (HP) in the

AS and BOB.

Figure 1: SeaWiFS derived Chl–a image of November, 1997 showing studied five High Productive locations (HP) locations in the AS and BOB

2. 2. Sea Surface Temperature (SST) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) pathfinder version 5 SST data with a spatial resolution of 4 km (daytime) has been used to study the correlation between Chlorophyll and SST

measurements. All the images were rescaled from 4km to 9km for consistency in all data set. 3. Results 3.1. Seasonal variability in the Arabian Sea and Bay of Bengal Seasonal variability is predominant and variation in magnitude over the years is observed. It is observed that the AS shows in general high Chlorophyll values, shows initiation during December, and peak is seen during February. Afterwards the values start declining becoming very low after March. If chlorophyll values are compared during 1997 – 98 and 2007 – 08 it is observed that the chlorophyll values are higher in 2007-08 as compared to values during 1997 – 98, particularly during the peak period.

Figure 2: Seasonal chlorophyll variability and Comparison of values during 1997-98 and 2007-08 at Northwest AS location (20°N-60°E) Here one plot is shown pertaining to a northeast location in the Arabian sea (20�N-60�E), which shows chlorophyll variability pattern from September to April during 1997 – 98 and 2007 – 08. It is evident that there are two peaks observed one during September and another during February (Figure 2). During 2007-08 the Chlorophyll values are higher as compared to that of during 1997 –98 specifically from December to February.and its ~1.2 mg/m3 during 2007 – 08 peak period (February) against that of during 1997-98 where its ~ 0.9 mg/m3. For the same location seasonal pattern of SST during 1997 – 98 and 2007 – 08 is shown in Fig. 3. The SST values vary in general from � to 31� C in the AS during the study period. At this location SST values range from 24� to 29�C (Figure 3). A decline in SST is observed from December to February, and again there is an increase in temperature from March onwards. The pattern remains similar during 1997-98 and 2007 – 08 but during February to April higher SST observed in 1997 – 98 as compared to that of during 2007-08. In general an inverse relationship is observed between chlorophyll and SST. However, the strength of relationship may change from year to year. A strong


inverse relationship (r2 = 0.74) at this location is observed during 2007-08.

Figure 3: Seasonal SST variability and Comparison of values during 1997-98 to 2007-08 at Northwest AS location (20°N-60°E)

Figure 4: Seasonal variability and Comparison of Chl- values during 1997-98 to 2007-08 at 13°N-83°E a Western BOB location In general the BOB shows relatively high Chlorophyll values (~0.9 mg m-3) in the northwestern part as compared to southwest BOB. To understand the chlorophyll variability in BOB, the Northwest BOB (13°N-83°E) location has been indentified where chlorophyll concentration remains high throughout all the months in 2007-08 as compared to that of 1997-98. At this location the Chlorophyll values in 2007-08 start increasing from October (~0.3 mg m-3), and attend the peak in the month of December (~0.6 mg m-3). (Figure 4). In 1997-98 the peak is observed in October (~0.31 mg m-3).The chlorophyll range is 0.1 to 0.62 mg m-3 in 2007-08, which is higher than the 1997-98 (0.1-0.31 mg m-3). The BOB shows Temperature variation from 22 to 29 �C. At same location in Northwest BOB the highest temperature is observed in March (~29�C) and lowest in December (~24 �C) during 2007-08.Chlorophyll is high in December-January and SST is low at the same time showing an inverse relation at this location.

3.2. High productive open ocean locations To analyse the high productive region in Open Ocean, five representative location were identified where high chlorophyll concentration have been observed throughout the study period. The analysis has been done on seasonal and inter-annual basis during October to April. Overall for all the high productive locations, Chlorophyll values are higher during 2007-08 as compared to that during 1997-98. To understand the Seasonal variation in Chlorophyll,monthly average has been analysed for the years 1997-98 and 2007-08 years. The northwest open ocean high productive location in BOB (14°N-83°E) shows high Chlorophyll values during November December (~0.6 mg m-3). At this location SST values are from 24-28�C during productive months, and as high as 30�C in March -

Figure 5: Seasonal variability and Comparison of Chl-a values during 1997-98 to 2007-08 in open ocean locations (5°N-58°E) April, which shows inverse relationship between chlorophyll and SST. Open Ocean location in southeast AS (6°N-79°E) shows high values from December to February in 2007-08 (~0.6 mg m-3).The chlorophyll range is 0.2-0.6 mg m-3 in 2007-08. The SST values are low in December to February (23 to 26 �C); Here Chlorophyll shows inverse relationship with SST. In the Figure 5, it is shown that at one location in The Northwest AS (5°N-58°E) shows higher Chlorophyll values in the beginning of winter months i.e. is September to December (~0.2-0.3 mg m-3), during 2007-08. From February to April the chlorophyll values remains close to the vaues in 1997-98. In 1997-98 the peak period remains same (December-February) but the values are observed lower than the 2007-08 in the high productive months. The other Southwest AS location (8°N-62°E) as well shows comparatively higher chlorophyll values in 2007-08 than 1997-98 (Figure 6). The Chlorophyll values shows peak in September, decline by November and again increase from 0.15 mg m-3 to 0.26 mg m-3 in


December and shows peak in January with higher value as 0.39 mg m-3. In January, 1997-98, Chlorophyll concentration is 0.24 mg m-3. The peak value is 0.26 mg m-3 in the month of December which is very low then recent years (Figure 5). The SST at this location is low in December to February (25-26�C). Rest of the months doesn’t show much variation. So there is no such relationship between Chlorophyll and SST.

Figure 6: Seasonal variability and Comparison of Chl-a values during 1997-98 to 2007-08 in open ocean location (8°N-62°E) 4. Discussions It is observed that Chlorophyll distribution pattern shows high values from November to February in the AS because of winter cooling and convective mixing. During February-March (winter monsoon), the northern latitudes become more productive due to winter cooling and convective mixing (Madhupratap et al., 1996). In the BOB it shows high values (~0.9 mg m-3) in December and February due to river runoff from a large number of rivers. The distribution pattern of Chlorophyll changes with season and location and is more in near coastal regions than in Open Ocean areas (Chaturvedi, 2003). Brock and McClain (1992) studied the inter-annual variability of summer phytoplankton bloom in the Northwestern Arabian Sea from 1979 – 1981 with CZCS data. They concluded that in all 3 years the bloom was driven by spatially distinct upward nutrient fluxes to the euphotic zone forced by physical processes of coastal upwelling and offshore Ekman pumping. The Arabian Sea (AS) and Bay of Bengal (BOB) divide the northern portion of Indian Ocean into two gulfs. The southern part of the BOB is not much productive as compared to the Ganges-Brahmaputra delta in the northern BOB (Dey and Singh, 2003). During the study it is evident that the extent of high Chlorophyll period, has also increased along with the values in the recent years. The vertical mixing leads to entrainment of nutrient-rich waters from the thermocline region to the upper layers and this in turn promoted increase in primary production. (Prasanna Kumar et al., 2002). SST shows high values in October and March for all the years. There is inverse relationship observed between SST and chlorophyll

(Chaturvedi, 2005). The 1990’s were the warmest decade since measurements began in the mid-nineteenth century, and the warmest individual years in order were 1998, 2002, 2003, 2004 and 1997. Since 1997, 1998 are reported to be very warm years this may be the reason of low productivity during that year. The present analysis can revealed that the productivity has increased from 1997 to 2008 even if the SST was high. Low temperature (SST) restricts phytoplankton growth due to physiological response whereas the waters with high temperature (SST) inhibits indirectly due to the deplition of nutrients. It appears that besides season, location also plays an important role in determining the relationship between various parameters (Chaturvedi et al., 1998). Global Chlorophyll indicated a decrease from the CZCS record to the present the decadal changes are indicated in the analysis. The decrease is almost ~6%. Larger reductions occurred in the northern high latitudes (Gregg et al., 2002). Warmer ocean temperatures increase stratification of the surface mixed layer, which inhibits the entrainment of nutrients from below to support ocean primary production (Polovina et al., 2008). SW and NE monsoon are periods when new nutrients were at or above the saturation concentration for uptake. The bulk of aquatic productivity (75%) occurs in the open ocean (gyre) systems, by virtue of their vast extent. Present studies shows that Chlorophyll has increased in this region in the recent years as compared to the last ten years. Though the seasonal pattern remains same, but there is change in magnitude. The duration of high productivity period also appears to have increased. Our studies shows that chlorophyll variability over the years when considered as average for three consecutive years for the beginning (1997-98) and later part of the study period (2006-2008) as well shows high chlorophyll during the recent years. High chlorophyll open ocean areas are also observed which may be contributing significantly towards productivity. Acknowledgements The data obtain from NASA/GSFC-DAAC for SeaWiFS derived Level-3 Chlorophyll and NOAA-AVHRR derived pathfinder version 5 SST data received from Physical Oceanography Distributed Active Archive Center (PODAAC), at Jet Propulsion Laboratory (JPL), US is thankfully acknowledged. References Banse, K. (1987). Seasonality of phytoplankton chlorophyll in the central and southern Arabian Sea, Deep Sea Research, 34, 713-723. Bhattathiri, P. M., A. A. Panta, S. Sawant, M. Gauns, S.G.P. Mantodkar and R. Mohanraju (1996). Phytoplankton production and chlorophyll distribution in the eastern and central Arabian Sea in 1994-95, Current Science, 71, 857-862.


Brock, J. C. and C.R. McClain (1992). Intrannual variability in phytoplankton blooms observed in the northwestern Arabian Sea during the southwest monsoon, Journal of Geophysical Research, 97, 733-750. Chaturvedi, N. (2003). Application of Remote Sensing to biological Oceanography: Indian Context in Recent Advances in Environmental Science, Discovery publishing house, New Delhi, India. Chaturvedi, N. (2005). Variability of Chlorophyll concentration in the Arabian Sea and Bay of Bengal as observed from SeaWiFS data, 1997-2000 and its interrelationship with Sea Surface Temperature (SST) derived from NOAA AVHRR, International Journal of Remote Sensing, 26(17), 3695-3706. Chaturvedi, N. and A. Narain (2003). Chlorophyll distribution pattern in the Arabian Sea: seasonal and regional variability, as observed from SeaWiFS data, International Journal of Remote Sensing, 24, 511- 518. Chaturvedi, N., A. Narain and P.C. Pandey (1998). Phytoplankton pigment/temperature relationship in the Arabian Sea, Indian Journal of Marine Science, 27, 286–291. Desa, E., T. Suresh and S. G. P. Mantodkar (2001). Sea truth validation of SeaWiFS ocean colour sensor in the coastal waters of the eastern Arabian Sea, Current Science, 80, 854–860. Dey, S., and S.P. Singh (2003). Comparison of chlorophyll distributions in the northeastern Arabian Sea and southern Bay of Bengal using IRS-P4 Ocean Color Monitor data, Remote Sensing of Environment, 85,424–428. Gregg, W. and M. Conkright (2002). Decadal changes in global ocean chlorophyll, Geophysical Research Letter, 29 (15), 1730.

Madhupratap, M., S. Prasanna Kumar, P. M. A. Bhattathiri, M. Dileep Kumar, S. Raghukumar, K.K.C. Nair and N. Ramaiah (1996). Mechanism of the biological response to winter cooling in the north eastern Arabian Sea, Nature, 384, 549– 552. Martinez, E., D. Antoine, F. D’ Ortenzio and B. Gentili (2009). Climate-Driven Basin-Scale Decadal Oscillations of Oceanic Phytoplankton, Science, 326, 1253. Nair, K.K.C., M. Madhupratap, T.C. Gopalkrishnan, P. Haridas and M. Gauns (1999). The Arabian Sea: Physical environment, zooplankton and myctophid abundance, Indian Journal of Marine Science, 28, 138-145. O’Reilly, J., S. Maritorena, B. G. Mitchel, D. A. Seigal, K. L. Carder, S. A. Garver, M.Kahru and C.R. Mc Clain (1998). Ocean Color chlorophyll algorithms for SeaWiFS, Journal of Geophysical Research, 103 (C11), 24937- 24953. Polovina, J., E. Howell and M. Abecassis (2008). Ocean’s least productive waters are expanding, Geophysical Research Letter, 35, 2-5. Prasanna Kumar, S., P.M. Muraleedharan, T.G. Prasad, M. Gauns, N. Ramaiah, S.N. de Souza, S. Sardesai and M. Madhupratap (2002).Why is the Bay of Bengal less productive during summer monsoon compared to Arabian Sea?, Geophysical Research Letter, 29 (24), 8.1-88.4. Werdell, P.J., S. Bailey, G. Fargion, C. Pietras, K. Knobelspiesse, G. Feldman and C. McClain (2003). Unique data repository facilitates ocean color satellite validation, EOS transactions, American Geophysical Union, 84, 379385-375387.


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Development of 3D rural geospatial database using high resolution satellite images, GIS, total station and GPS

Y. Navatha, K. Venkata Reddy, Deva Pratap, D.C Prashanth Babu and A. Jayatheja

Department of Civil Engineering, NIT Warangal, Warangal – 506 004 Email: [email protected]

(Received: August 5, 2011; in final form June 17, 2012)

Abstract: Villages are to be given prime importance in development of infrastructural facilities to reduce the socio-economic gap between urban and rural areas. The new technologies like Geographical Information Systems (GIS) will help to create a geospatial database which is useful in analyzing the available facilities and to plan developmental activities to be carried out. Present paper discusses the development and analysis of 3D geospatial data for Gangadevipally village of Warangal district, Andhra Pradesh, India using high resolution satellite data and GIS. Different thematic layers have been prepared for the study area and the non-spatial data has been collected by house hold surveys. 3D model of the village has been developed in ArcScene with height of extrusion taken from the height attribute that is collected from total station survey. The 3D texture map of the village has been prepared using Google SketchUp. The developed 3D model has been analysed using attributes representing the characteristics of infrastructural facilities in the study area. The methodology presented in this study will help in planning and management of infrastructural facilities in rural areas and better governance at the village level. Keywords: 3D GIS model, Texture mapping, Geo spatial database, spatial analysis 1. Introduction

3D modeling is an effective tool in representing the real world features. 3D modeling applied with Geographic Information System (GIS) technology will help to solve many problems related to rural area development. The applications of 3D models include urban modeling, traffic network distribution, watershed modeling and disaster management. Murata (2004) explained about the application developed for visualizing 3D city model and performed various analyses on it. He also explained the applications of 3D city model. Kim & Ilir (2005) studied the use of 3D GIS to simulate the past and future physical and socio economic conditions of High Springs in Florida town. They also proposed texture mapping for the houses which is the best way for the realistic view. Raghuveer and Pramod (2007) carried out the 3D city modeling for a part of Kolkata city by which they have concluded that these models are very much useful in micro cellular urban planning. Gadal et al. (2010) studied the 3D dynamic geo-visualization models of Delhi-Mumbai corridor of India by using multi temporal data. 3D models can be prepared in a number of ways in GIS. It all depends on the type of satellite images used and the type of software available to develop the 3D model. Shiode (2001) described the recent developments in the visualization of urban landscapes and the different techniques adopted for the development of 3D contents and how they could contribute to geographical analysis and planning of urban environment. Usman (2009) summarized the development of 3D model using photogrammetry as a

basic data collection technique and also a tool for information extraction. Texture mapping is mainly used for analyzing the real world features and their importance. Ahmed and Nabeel (2006) studied the use of 3D GIS in analyzing the natural risks in Mina city, Saudi Arabia. They analyzed the slopes, water drafting paths, possible locations of falling rocks and gave some recommendations to avoid future environmental hazards. They also carried out the texture mapping of the study area for its realistic 3D view. Tunc et al. (2004) presented the various methods of generating a 3D city models and were tested by taking pilot projects at various locations. They mentioned that GIS and CAD systems are more effective in information storage for 3D models. Villages are said to be the back bone of any developing country and have to be given the prime importance in development of infrastructural facilities. The use of 3D GIS models in micro level planning and infrastructural development of villages is beneficial to the rural community. Anuj et al. (2007) have described the importance of GIS in micro level planning and developed a graphical user interface for visualizing the spatial data infrastructure provided at the village level by considering various demographic and socio-economic data. They developed a user interface using Visual Basic 6.0 and Arc Objects of ArcGIS for a village in Allahabad district, Uttar Pradesh, India. Al-Hanbali et al. (2010) described the 3D model that was built for the Public Park, University area and commercial area in Jordan city and shows how the analysis can be made using the 3D models. Gupta et al.



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(2010) developed a Web GIS frame work for planning the infrastructural facilities for Karchana tehsil of Allahabad district, Uttar Pradesh, India. The infrastructural facilities include medical and educational facilities at different levels, power supply, physical connectivity, water supply, bank and post office. They have prepared various spatial utility maps for planning these infrastructural facilities. From the above research studies, it has been observed that 3D modeling has become an important tool in geo spatial modeling of real world features. This 3D modeling technique, if applied to the rural areas, it will be useful for the better planning, development and management of infrastructural facilities in rural areas. It will also help in prioritizing the villages according to their developmental activities. Hence, an attempt is made in this paper for preparation and analysis of 3D geospatial features of village using high resolution images, GIS, GPS and total station. 2. Methodology

The methodology followed for the development of 3D rural geospatial database for the study area is shown in Figure 1. The methodology involves three stages. Stage one includes the preparation of geospatial database of the study area. For this purpose high resolution images are downloaded from Google Earth at same eye altitude covering the entire study area. The individual images are georeferenced using ground control points and mosaiced image of the study area is prepared. Attribute data of the study area including height of the features is collected by field studies. Digitization of various feature layers of the study area is carried out in ArcGIS environment. The non-spatial data is given as the attribute data to the respective feature layers. Second stage involves preparation of 3D spatial features. For this purpose, the same feature layers are added to ArcScene environment. The features are first converted into 3D features and then are extruded to a height of the feature given in the respective attribute table to see the 3D features of the study area. The 3D model created is exported to Google SketchUp for realistic modeling and texture mapping of spatial features. The third stage consists of analysis of spatial attribute characteristics of 3D features. 3. Study Area and Database Preparation

Gangadevipally village of Geesgonda Mandal, Warangal District, Andhra Pradesh, India has been chosen as the study area for the development of 3D geo spatial database. The village is situated between east longitudes of 79�4238and 79�433 and north latitudes of 17�571 and 17�5737. It is situated at14 km from Warangal city. The village is well known for the success achieved in wining many awards from state and central government. It is recognized as a “Model

Village” and serves as an icon to other villages for its development in providing drinking water to all households, 100% tax payment and ban on alcohol consumption. The location map of the study area is shown in Figure 2.

Figure 1:Flowchart showing the adopted methodology

Figure 2: Location map of the study area The high resolution images have been downloaded from Google Earth at an eye altitude of 832 meters. Individual images are geo referenced using the ground control points. The Ground Control Points (GCP’s) are collected from ‘iTouchmap’ (www.iTouchMap.com). These GCP’s are ground verified using Trimble GeoXM Global Positioning System (GPS) instrument. Table 1 shows the values of the GCPs that were collected from iTouchmap and ground verified using

Spatial Data collection (Google Earth Images, GPS, Total station and

field studies)

Non-Spatial Data Collection ��(Data collection from house

hold surveys)

Creation of Geospatial Database in ArcMap (Feature layers of study area)

Feature layers added in ArcScene Environment

Converting features to 3D using 3D Analyst

Enabling the extrusion of property

Exporting the geospatial features to Google SketchUp model

GIS Analysis of 3D model

3D Texture mapping of housing unit Google

SketchUp (Based on photo images of the

housing unit).


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the GPS instrument for one image. Each individual image is geo-referenced and all the geo-referenced images are mosaiced together using mosaic operation. In the Field survey, heights of individual houses were collected using Trimble 3600 series total station instrument, using remote object elevation preset method. Photographs have been collected for all the houses of village. Household survey has been carried out to know the facilities available to the people. The mosaic map is used as base map and various feature layers are digitized. The roads, electric lines, drainage lines are digitized as line feature, houses are digitized as polygon feature and street lights are digitized as point feature. The height and the photographs of the houses collected from the field survey have been added as attribute data to the polygon features.

Table 1: Table showing the values collected from iTouchMap and the GPS instrument for one image

GCP Point iTouchMap Values

GPS Instrument Values

GCP 1 Lat: 17° 57’ 36.3198”

Long: 79° 42’ 52.4988”

Lat: 17° 57' 36.418"

Long: 79° 42' 52.549"

GCP 2 Lat: 17° 57’ 33.2532”

Long: 79° 42’ 54.795”

Lat: 17° 57' 33.342"

Long: 79° 42' 54.781"

GCP 3 Lat: 17° 54’ 48.844”

Long: 79° 36’ 9.474”

Lat: 17° 54' 48.920"

Long: 76° 36' 9.376"

GCP 4 Lat: 17° 57’ 34.3398”

Long: 79° 42’ 55.1478”

Lat: 17° 57' 34.451"

Long: 79° 42' 55.181"

Figure 3: Map depicting the geo spatial features of

study area

The non- spatial data such as drainage, type of roof, type of house, number of family units and number people residing per plinth area of each housing unit are prepared as database in the Microsoft Access. The map with different geospatial features of the study area is shown in Figure3. In ArcScene environment, the houses are converted into 3D features and are extruded to a height given in the attribute table. For realistic 3D modeling the feature layers are exported to Google SketchUp model using ESRI Sketch up plug-in. 3.1 Non Spatial Data Base Preparation The non spatial data preparation includes the collection of details of each and every individual house. The heights are collected using “remote object elevation preset” present in total station. Photographs of individual houses are collected and are added as attribute data to the spatial feature layer. The details include information such as roof type, type of drainage present, number of families staying in particular house, total number of members staying in a house, type of house, houses that are constructed under government schemes and stage of construction of the houses allotted under Government Housing Scheme (GHS). The type of road and width of road is also collected and is given as the attribute to the road feature layer. Photographs of the houses are added as attribute data to the polygon feature layer. Figure 4 shows the scanned copy of the filled questionnaire.

Figure 4: Scanned copy of the filled questionnaire


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4. 3D Model Generation

The non spatial data is updated to the respective feature layers. The feature layers are exported into ArcScene Environment for the generation of 3D model. The features are converted into 3D features using 3D analyst tool bar. The features are extruded to height by enabling the extrusion property in the properties of the feature layer. The 3D model of the village depicting the height features of the thematic layers is shown in Figure 5. The closed view of height variations of the thematic features is shown in Figure 6. For realistic modeling, exact texture is given to individual features in Google SketchUp. The ESRI sketch up plug-in is added as a “dll” file in ArcMap environment. The ESRI feature layer is converted into Google SketchUp model using the conversion tool. In Google SketchUp, the texture mapping has been carried out to view the realistic model of the study area. Photographs of all the four sides of individual houses are collected and texture mapping has been carried out by observing the photographs. In Google SketchUp, different tools are available for texture modeling of the features. Extrude is tool used to extrude features depending on the height. The other textural feature like, roof type, wall type, stairs etc. have been prepared using the tools available in Google SketchUp.

Figure 5: 3D model of different thematic layers in ArcScene Environment

The 3D texture map of the study area modeled in Google SketchUp is shown in Figure 7. A typical 3D texture model for a house in Gangadevipally is shown in Figure 8.

Figure 6: Close view of the height variations of thematic features of the village

Figure 7: Texture Mapping carried out in Google Sketch Up for the study area

Figure 8: The typical housing unit and its 3D texture model; (a) Photo of the house (b) 3D texture model of the house

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5. Analysis of 3D Geospatial Model of the Study Area

Different kind of analysis has been carried out on the developed 3D model. Some of the analysis includes height, plinth area, roof type, number of rooms, number of families, total number of members, owned/rented, number of GHS sanctioned and their stage of construction. Density and drainage analysis also has been carried out on the developed model. The roof type analysis carried out on the 3D model is shown in Figure 9. From roof type analysis, 60% of the houses have the roof constructed with R.C.C. Slab. 36% of the houses have tilled roof and 4% of the houses have asbestos roof.

Figure 9: Roof type analysis carried out on 3D Model of Gangadevipally village The height analysis carried out on the developed 3D model is shown in Figure 10. From height analysis, it is seen that 80 % of the houses are having the heights in the range of 2.5 to 3.5 meters. 15% of the houses have the heights in the range of 3.5 to 5 meters and 4% of the houses have the heights in the range of 0-2.5 meters. There are two water tanks constructed in the heights range of 5-15 meters which constitutes to 1%. From drainage analysis, it is seen that there is one temple where drainage is not applicable and 54% of the houses have open drainage system followed by 31% of houses have septic tank. 15% of the houses have underground drainage system. From GHS sanctioned analysis, it is observed that there is more than one family staying in a single house and the analysis is based on the total number of families present. Families staying in 34% of houses have been sanctioned houses to be constructed under

GHS scheme. From the plinth area analysis, it is observed that around 30.88% of the houses have a plinth area in the range of 50 to 75 m2 followed by 27.72% within the range of 100 to 200 m2. The highest plinth area is in the range of 300 to 500 m2 which is occupied by school building of the village.

Figure 10: Height analysis carried on 3D Model of Gangadevipally village

6. Conclusions In this paper, 3D modeling of a Ganagadevipally village of Warangal district using GIS, high resolution images, total station and GPS has been presented. Texture mapping for the houses has been carried out using Google SketchUp. Various analyses have been carried out on the developed 3D model. From the analysis, it is observed that the drainage system has to be improved for the village. The methodology presented in this study will help in planning and management of infrastructural facilities in rural areas for better governance at the village level.

Acknowledgements Authors are thankful to the DST- SERC of India for the financial assistance to carry out this work through project No. SR/FTP/ES-104/2009. Authors are also thankful to K. Raja Mouli, Sarpanch, Gangadevipalli village, Andhra Pradesh, India for his help during the field survey.


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References Ahmed, M.S. and A.S. Nabeel (2006). Using 3D GIS to access Environmental hazards in built environments (A case study: Mina). Journal of Al Azhar University-Engineering sector-ISSN, 1110-640 Al-Hanbali, N., F. Eyad and D. Abdallah (2010). Building geospatial information system using IKONOS and SPOT images; creation of three dimensional model for the higher council of youth, Amman area, Jordan. Proc. 5th National GIS Symposium in Saudi Arabia, April 26-28, 2010, Le Meridian, Al-Khobar – Eastern Province. Anuj, B., R.D. Gupta and S.C. Prasad (2007). Development of GIS based spatial infrastructure for micro level planning. Proc. Map World Forum, January 22-25, 2007, Hyderabad, India Gadal, S., S. Fournier and E. Prouteau (2010). 3D Dynamic Representation for Urban Sprawl Modelling: Example of India’s Delhi-Mumbai corridor. S.A.P.I.EN.S, 2:2. Gupta, Y.K., R.D. Gupta and K. Kumar (2010). Web GIS for planning infrastructural facilities at village level. Proc. 13th Annual International conference and exhibition on Geospatial Information technology and applications, Map India, January 19-21, 2010, Epicentre, Gurgaon, India.

Kim, D. H. and B. Ilir (2005). Using 3D GIS Simulation for Urban Design. Proc. ESRI International User Conference, July 25-29, 2005, San Diego, California. Murata, M. (2004). 3D-GIS Application for Urban Planning based on 3D City Model. Proc ESRI International User Conference, August 9-13, 2004, San Diego, California. Raghuveer, J. and K. Pramod (2007). 3D city models for urban GIS. Proc. Map World Forum January. 22-25, 2007, Hyderabad, India Shiode, N. (2001). 3D urban models: Recent developments in the digital modeling of urban environments in three-dimensions. Geo Journal, 52, 263–269. Tunc, E., F. Karsli and E. Ayan (2004). 3D city reconstruction by different technologies to manage and recognize the current situation. Proc XXth ISPRS Congress, Commission IV, Vol XXXV, July 12-23, Istanbul, Turkey. Usman, K. (2009). Significance of photogrammetry in 3D visualization and building reconstruction. Geospatial industry magazine, Sept, 2009.

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Development of Costal Zone Information Systems (CZIS) using Arc objectes

Aviral Kulshreshtha1, H.B Chauhan2, Rajnikant J. Bhanderi2 and Anjna Vyas1 1 CEPT University, Ahmedabad

2 Space Applications Center (ISRO), Ahmedabad Email: [email protected]

(Received: May 02, 2012; in final form Sept. 21, 2012)

Abstract: Coastal zone is the area of interaction between land and sea. The interactions between various natural processes and human activities are important factors in the coastal area. The increasing pressure on coastal zone due to concentration of population, development of industries, discharge of waste effluents, municipal sewage, recreational activities and spurt in trade through sea route, has adversely affected the coastal environment. To manage and regulate the activities in the coastal zone, an information system comprising of spatial & non spatial data bases and query shells for decision making is required. In the present work, a decision making system, Coastal Zone Information System (CZIS), for the Goa state of India is developed using Arc objects. The present CZIS is simple and user friendly and thus helps even non GIS users to access and retrieve the required spatial as well non-spatial information. The outputs of CZIS can be used to prepare coastal zone management plans by the managers and authorities responsible for implementation of CRZ notification as well as development of the coastal areas. Key Words: Coastal Zone, GIS, Spatial Data, Goa, Coastal Zone Management Plan, High Tide Line, Low Tide Line, Coastal Regulation Zone, Information Systems

1. Introduction

Coastal zone is an area where land and sea interacts. It is dynamic in nature as its boundary keeps on changing with time due to both, the natural factors such as waves, tides, storm surge, tsunami, sea level rise etc as well as anthropogenic interventions (Ahmed, 1972, SAC, 2012). Terrestrial as well as marine environments influence this zone which encapsulates important natural resources, both, renewable and non-renewable. Coasts have always attracted humans for food, shelter, natural resources like various Minerals, Gas, and Oil and intercontinental trading. Tourisms have been a vital revenue generation for certain coastal states /countries. Coastal Zones are fragile in nature and is in severe threat in most countries due to rapid urbanization, pollution, tourism development, over exploitation of coastal resources (CRZ- 2011, Shah, 1997. The Coastal environment plays a vital role in India’s economy by virtue of resources, productive habitats and rich biodiversity. India has around 7517 km of coastline including coastlines of Andaman and Nicobar Islands and supports almost 30% of its population (www.wikipedia.org). Coastal zone in India assumes its importance because of high productivity of its ecosystems, concentration of population, exploitation of natural resources, discharge of municipal sewage, development of various industries, increasing loads on harbours and above all petroleum exploration

activities. Coastal landforms have an important role in protecting the coastline from erosion and flooding. Thus, there is need to protect the coastal environment while ensuring continuing production and development (Rathi, 1997). The coastal zones are also prone to natural disasters and has been degrading constantly due to exploitation of natural resources, urbanisation, pollution, industrialisation and dumping of its solid and toxic wastes, erosion due to manmade activities like ports, harbours etc. Therefore, a sustainable plan for management and conservation of resources and environment in the coastal zone is indispensible. Environmentally sustainable coastal zone management depends upon availability of accurate and comprehensive scientific data on which policy decisions can be made (Shah, 1997). Coastal Zone Management Plan requires spatial databases on LandUse/LandCover , landforms, shoreline and its dynamics, ecologically sensitive areas (ESAs ), demographic and socio-economic profiles etc (Nayak, 1997). Remote Sensing imageries, due to its synoptic coverage and repetitive ability have been widely used for mapping and monitoring of coastal areas. A wide spectrum of data with improved temporal, spatial, spectral and radiometric resolution imageries helps us to monitor regularly these fragile areas (Bahuguna et al.1997). These thematic datasets are available in spatial as well as non-spatial form and can be integrated with secondary data using GIS for developing decision making tools which can be



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used for Coastal Zone Management (Gupta, 2000). In order to standardize and organize these data sets in a spatial frame work and to integrate the spatial and non spatial data to aid decision support, a Coastal Zone Information System (CZIS) becomes imperative. Such information system must be user friendly so that even a non GIS user may be able to use it without any difficulty. Space Applications Centre has developed such information system using AML (Arc Macro Language) (Gupta, 2000). However, due to the subsequent development in this field over a period of time, AML based tools have become obsolete. AML is a set of command procedure executed in Arc Info GIS environment. It includes an extensive set of commands that can be used interactively or in AML programs (macros). Therefore, developers need to have extensive knowledge of Arc Info commands that has to be used in AMLs. Whereas Visual Basic provides object oriented based complete set of tools and thus provides easier way to create the graphical user interface (GUI). Instead of writing numerous lines of codes to describe the appearance and location of interface elements, it simply adds pre-built objects into place on screen. The objective of the present work was to develop a Coastal Zone Information System (CZIS) for coastal state of Goa using Arc objects based on Visual Basics. Development of such an information system involves, creation, standardization and organization of spatial and non spatial data bases, design and development of user friendly decision making tools to retrieve and integrate/ analyse the spatial and non-spatial information to generate a Coastal Zone Management Plan (CZMP). CZIS provides decision support tools to planners and administrators for implementing CRZ notification and also for preparation of coastal zone management plans (CRZ 2011, Shah 1997). 2. Study Area For development of Coastal Zone Information System, Goa, the smallest state of India having geographic area of 3,702 km2 located in the western region of the country, has been taken. The Goa state spans between the latitudes 14°53�54� N and 15°40�00� N and longitudes 73°40�33� E and 74°20�13� E. Location map of the study area is given in figure 1. Panaji is the capital of the state. It is bounded by state of Maharashtra in the north, eastern and southern part is compassed by state of Karnataka and Arabian Sea forms the western coast. Many good beaches of the Indian coast available in Goa and tourism are important for the state. In addition to this, Ore mining is also important activities in the Goa. Thus, prudent coastal zone information is vital for this state.

Figure 1: Location map of Study Area showing Goa State

3. Methodology Coastal Zone Information System (CZIS) is designed and developed in GIS environment to prepare an effective coastal zone management plan for the state of Goa. Available landuse and wetland maps prepared by Space Applications Centre, Ahmedabad using satellite images from IRS LISS-II, Landsat MSS/TM, SPOT of 1988-1994 (SAC 2012) have been used in this study.

Figure 2: Coastal Zone Information System User Interface CZIS is aimed to provide systematic retrieval as well as integration of wide range of data, both spatial and non-spatial for decision making in addition to generate various map outputs. Development of CZIS involved the following major tasks:

i. Development of a graphic user interface (GUI)

ii. Organization of a digital database comprising of various themes at different scales in GIS environment.


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iii. Development of Query shells to retrieve and use the database in user friendly and efficient manner.

iv. The CZIS User interface developed using visual basics (Arc objects) for the state of Goa is shown in Figure 2.

Framework for development of CZIS is given in Figure 3.

Figure 3: CZIS Framework

Further, the following tasks were performed:

i. Available maps on various themes and scales (prepared using remote sensing satellite data over a period of 1988-1994 to cover the coastline of Goa) have been digitized and used in the information system.

ii. Collection of non-spatial data such as name of settlements, population from different sources.

iii. Generation of different thematic layers using references frames.

iv. Linking non-spatial data to spatial data base. CZIS is divided into three main components – Display System which displays districts and talukas, Theme Based System reflects themes like LandUse, Habitat, High Tide Line (HTL), Low Tide Line (LTL), Coastal Regulation Zone (CRZ) boundary and Rail/Road network along with SOI toposheets and Map Output Generation System can be used to generate landuse or coastal zone regulation in selected toposheets numbers at various scales. CZIS mainly provides GUI to the non-expert GIS users to retrieve spatial as well as non-spatial information and composing maps as per their requirements.

4. Results & Discussions The outputs of such system can be useful for assessing environmental impacts, preparation of conservation and management plans, development plans for coastal zone development, tourism development and in general creating a data base of

collective layers for use by planners/decision makers. CZIS is a customized tool for coastal applications which can be used as a) to retrieve all the information of data base in a user friendly manner, b) to generate category wise land use information for all entire state or for any district or taluka or for any SOI toposheet on different scales and c) to generate final map layout by providing SOI toposheet number and theme as an input. As an example, the output generated using CZIS for one of the grid for Goa state is shown in figure 4.

Figure 4: Landuse Information for Grid 48E15NE

CZIS developed and presented in this paper can support the coastal zone planners and managers in preparation can an effective Coastal Zone Management Plan for sustainable development of coastal areas due to its wide information retrieval capability along with the generation of varied information and automatic map outputs. This will not only guide for development of various activities such as industries, tourism, security from hazard devastation, mangrove conservation, setting up harbours or jetties all along the coast but also will help in proper utilization of coastal resources to avoid any adverse effect or damage to the coastal ecosystem. References:

Ahmed, E. (1972). Coastal Geomorphology of India. Orient Longman, New Delhi, India. Bahuguna, A., H.B. Chauhan and S.R. Naik (1997). Coastal vegetation of Gujarat. Proceedings of Workshop on Integrated Coastal Zone Management, Gandhinagar, pp. 88-95.

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CRZ (2011). Coastal Regulation Zone Notification, Ministry of Environment and Forest Government of India, New Delhi. Gupta, M. C. (2000). Coastal Zone Management Information System for Gujarat, Technical Report, SAC/MWRD/TR/04/2000. Space Application Centre. Ahmedabad. Rathi, A. K. (1997). Coastal Zone Management Plan for Gujarat. Proceedings of Workshop on Integrated Coastal Zone Management, Gandhinagar, pp. 127-128.

SAC (2012). Coastal Zones of India. Space Application Centre. Ahmedabad. ISBN: 978-81-909978-9-8 pp 1-5, 347-390.

H. Shah (1997). Coastal Zone and need for Integrated Management. Proceedings of Workshop on Integrated Coastal Zone Management, Gandhinagar, pp. 2-5.

Shailesh, Nayak (1997). Information needs of Integrated Coastal Zone Management: Role of Remote Sensing and Geographic Information System. Proceedings of Workshop on Integrated Coastal Zone Management, Gandhinagar, pp. 6-22. WikiPedia: www.wikipedia.org.

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Short Note : Implications and risks of technology change in the geomatics curriculum

Obade Vincent de Paul1 and Ogenga Anyango Masela2 1Plant Science Department, Medary Avenue, Brookings, South Dakota State University, U.S.A.

2Education and Human Development Department, 231 Centenial Drive Stop 7189, University of North Dakota, Grand Forks, U.S.A. Email: [email protected] ; [email protected]

(Received: February 23, 2012; in final form September 03, 2012)

Abstract: As technology advances to cope with the changing societal needs, a number of challenges have sprung in the respective educational curriculum to satisfy this demand. This paper investigates the impact of technology on geomatics curriculum, and discusses some measures that can be undertaken to cope with these changes. Keywords: change, curriculum, geomatics, technology. 1. Introduction Implementation, and efficient management of spatial information technology, is crucial for sustainable development. Spatial information processing, and management is the core activity of surveying. FIG (1991) defines the surveyor as “a professional with the academic qualifications, and technical expertise to practice the science of measurement and/or accurate positioning of land and sea, and structures thereon, and to instigate the advancement, and development of such practices”. The traditional surveyor’s role was to make maps, however with increasing computer speeds, and accessibility to digital technology, the role of the surveyor has become multi-disciplinary, and expanded to include managing and advising on mapping, or geospatial related issues, hence the name change to geomatics engineer. Geomatics is a new science utilizing digital information technology to make maps. The term “geomatics” is an acronym formed by “geo” (earth) information, and “matics” (measurement) (Bédard et al., 1988). The terms geospatial, and geomatics are used interchangeably in this paper. In order for the geospatial (survey) professional to meet the current market needs, sound training is crucial at various levels so as to impart the required skills. Disciplines such as geodesy, planning, statistics, computer programming, geography, photogrammetry, remote sensing, natural resource management, agriculture, mathematics, cartography, land law, civil engineering are involved at various levels, and from different perspectives. Societal evolution, increasing computer speed, and user expectations have created the need for review, and expansion of the geomatics curriculum. For example, the content of traditional surveying courses focused on the mathematics of map making, however because of the automation in mapping procedure, geomatics courses encompass a broad based education emphasizing not only intellectual development but also entrepreneurial skills, such as the providing added value in terms of map products, and advice on map applications. Upgrading curriculum to meet societal needs can improve student learning, motivation,

retention, and compliance of the educational institution with accreditation requirements (Froyd et al., 2006). A good curriculum should be designed to enable students develop critical thinking, and sufficient problem solving skills in the shortest possible time. Each body of knowledge should be made up of units that focus on the concepts, methodologies, techniques, and applications specific to that subject. This paper explores the circumstances influencing contents of technological curricula, such as: 1) societal/user expectations, 2) technological advancements in tools and equipments, and 3) role of instructor development. 2. Society expectations The societal benefit areas of mapping science include the study of weather patterns, natural disasters, ocean resources, climate variability, agriculture and forestry, human health, ecological forecasting, national security, water resource, and energy resource managementi. For example, digital maps can support the setting up of a suitable land management system that can enable the provision of up to date information useful to policy makers. The drawbacks in existing land management systems especially in developing countries such as Kenya are related to: (i) paper processing, which is a slow, inefficient process, and exposes much of the data to permanent loss, (ii) bureaucratic red tape due to excessive suspicion between land information users, and authorities, which hampers the flow of land information to users, and researchers who might suggest improvements, and (iii) a non-integrated approach, given that land information is useful to multidisciplinary users such as planners, farmers, construction engineers, policy makers (Mulaku and McLaughlin, 1996). 3. Technological advancements in tools and equipments Producing maps using survey equipment such as the odolites, levels, tape measures, the electromagnetic distance measurement (EDM) equipment, and so forth, is an expensive undertaking, especially when mapping over large areas. Technological advanced equipments


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such as Total stations and the GPS have transformed mapping. Total stations have EDM equipment incorporated into the theodolite, and are capable of storing data in digital format. GPS receivers on the other hand, utilize satellites to provide location data, which can be downloaded to produce digital or hard-copy maps. The GPS (global positioning system) utilizes signals received from at least four space based satellites, to determine the coordinate, elevation, and time with great accuracy, usually within a few meters or even less. GPS technology is used in mapping, cell phones, navigation etc. In recent times, the cost of GPS receivers has decreased substantially, therefore becoming more accessible to the general public. It is important to note that these current mapping equipments can be operated by a technician having basic high school education, and trained over a period of one week to less than six months. Therefore, is it necessary to train a student over a period of 3 to 5 years to make maps? 4. What to change in the geomatics curricula Just like humans have progressed from the stone, to the iron age, to the fossil fuel economy, and now to renewable energy, map making has evolved from the use of chains for measurement, to the digital age. Therefore, the geomatics curricula need to be re-invented in line with technological advancements. For example, apart from emphasis on computer science, the geomatics curricula will need to expand on theoretical aspects related to environmental sustainability such as soil science, climatology, land evaluation, ecology, and reduce time spent on geodesy, and cartography courses. However, deciding on the time allocation, and instructional approach for a specific course is a challenging undertaking. Felder et al. (2000) advises on the following approaches for improved teaching, and learning in academic institutions: (i) formulate and publish clear instructional objectives, (ii) establish relevance of course material, (iii) promote active learning in the classroom, (iv) give challenging but fair tests, and (v) convey a sense of concern about students’ learning. In the United States of America (USA), the higher education institutions that offer geomatics include the University of Connecticut, University of Alaska Anchorage, Michigan Technical University, University of Florida, Purdue University, and Utah Valley University who are in the process of introducing the program. Other universities have geomatics within the civil engineering program. In Kenya, geospatial training courses are offered at the University of Nairobi, the Kenya Polytechnic, the Kenya Institute of Surveying and Mapping, Regional Centre for Mapping of Resources for Development, and the Jomo Kenyatta University of Agriculture and Technology. However, geography, environmental science, and civil engineering departments in some institutions offer

geomatics training. In the USA, a majority of the instructors in the institutions offering geomatics are licensed to practice, or offer consultancy services. However, in Kenya, apart from the University of Nairobi, the other training institutions have instructors who are not licensed. 5. Obstacles to curricula change and role of instructors in change In order to meet industry demands, and end user expectations, the curricula for geomatics courses are prepared in consultation with the respective professional bodies. For example, in the USA the National Council of Examiners for Engineering and Surveying (NCEES) are involved whereas in Kenya, learning institutions planning to offer such courses need approval from the Institution of Surveyors of Kenya (ISK), or the Institution of Engineers of Kenya (IEK). If the course is to be offered at tertiary level institutions such as technical colleges and polytechnics, then the syllabus is prepared by the Kenya Institute of Education (KIE). Academic instructors contribute towards creating well structured support for curricula change, and innovation in teaching in a timely manner (Froyd et al., 2006). However, academic instructors need to re-invent themselves through learning new skills relevant to the current needs of the society. Furthermore, professional development for instructors is an expensive undertaking that institutions are reluctant to incur. Instructors may resist curricula changes that affect instructor research time, or necessitate the lay-off of academic staff whose courses are perceived to be irrelevant, and therefore not included in the new curricula. In addition, the costs of new curricula materials such as equipment may be expensive. The implementation of new curricula may be delayed in circumstances where institutions suspect duplication of courses. There is also the issue on how to assess changes in curricula, and which changes should be permanent. For example, if an instructor who teaches a course makes changes to the course content, and the delivery approach. The following year, another instructor who teaches the same course decides that the content, and pedagogical changes are not justified, and reverts to the course content as it was before the changes. Can such a course be considered changed? Another controversial issue is whether to consider education as an avenue to intellectual, and character development, or as a path to a future career, or both. Finally, it is important to note that the future of geomatics engineering lies on professionals trained with a multi disciplinary background who can provide innovative land management advice. 6. Summary This article discusses the impact of technology on the duration, and curricula of geomatics engineering. With

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globalization, continuing expansion of technology, increased ability of institutions to collaborate worldwide to create modules and programs, and the need to create uniform standards in professional disciplines, concerned authorities should provide a common format, and structure for sharing content and comparing programs. Developing the curriculum to satisfy the needs of students, educators, and industry, may serve as a starting point for national, and regional standards in geomatics curricula. It may also serve as the basis for creating exemplary pathways that can be used to define discipline in technological, and non technological courses for many different workforce domains. Continued participation, and interest from government, and professional organizations, through seminars and workshops, for example can also help improve teaching, and learning of geomatics engineering. References Bédard, Y, P. Gagnon and P.A. Gagnon (1988). Modernizing surveying and mapping education: The programs in geomatics at Laval University. Canadian Institute of Surveying and Mapping Journal, 42, 105-114.

Felder, R. M., D. R. Woods, J. E. Stice and A. Rugarcia (2000). The future of engineering education II. Teaching methods that work. Chemical Engineering Education, 34:26–39. FIG (International Federation of Surveyors) (1991). Definition of a surveyor. FIG publications, No. 2, Helsinki. Froyd, J., J. Layne and K. Watson (2006). Issues regarding change in engineering education. 36th ASEE/IEEE frontiers in education conference, October 28th to 31st, 2006, San Diego, CA. Future of land imaging interagency working group (2007). A plan for U.S. national land imaging program. National science and technology council report, Washington, D.C. 20502. Mulaku, G.C. and J. McLaughlin (1996). Concepts for improving property mapping in Kenya. South African Journal of Surveying and Mapping, 23, 211 - 216. ��

Reviewers for Journal of Geomatics, Volume 6 No. 1 & 2 Editorial Board places on record its sincere gratitude to the following peers for sparing their valuable time to review the papers for the Journal of Geomatics, Volume 6.

Dr. Ajai Chief Editor

Dr. Markand P. Oza Dr. P.C. Joshi Dr. R.M. Gairola Dr. P.M. Udani Mrs. Pushpalata Shah Dr. P.S. Dhinwa Dr. Ranendu Ghosh Dr. R. Ramakrishna DDr. Praveen K. Gupta Dr. R. Nagaraja Dr. C.J. Kumanan Dr. B. Gopalakrishna Dr. P.L.N. Raju Dr. P.K. Champati Ray Dr. A.S. Arya Dr. Mousami Gupta Mr. P. Jayaprasad Mr. T.P. Srinivasan Prof. Pradeep K. Garg Dr. Mehul Pandya Dr. R.P. Singh Dr. Ashish Shukla Dr. M.B. Potdar Mr. Shard Chander Dr. Shibendu S. Ray Dr. A.K. Sharma Mr. Binay Kumar Sahai Dr. A. Arunachalam Dr. Sameer Saran Dr. T.V.R. Murthy Dr. T.S. Singh Dr. S.G. Prabhu Matondkar

Journal of Geomatics v Vol.6, No.2 October 2012

Author Index Volume 6

Author Issue Page Ajai (see Shah, M) 2 127 Amoah, A.S. 1 07 Anandan, C. (See Sankar, Ram M.) 2 65 Babu, D.C. Prashanth (see Navatha, Y.) 2 132 Bagyaraj, M. (See Gurugnanam, B.) 1 49 Bhanderi, Rajnikant J. (see Kulshreshtha, Aviral) 2 138 Chaturvedi, N. (see Shah, M) 2 127 Chauhan, H.B. (see Kulshreshtha, Aviral) 2 138 Chingkhei, R.K. 2 85 Dasgupta, Arunima 2 76 Debnath, P. (See Sharma, L.P.) 2 113 Dhinwa, P.S. (See Dasgupta, Arunima) 2 76 Dubey, Abhishkh (See Ghosh, Jayant Kumar) 1 01 Duker, A.A. (See Amoah, A.S.) 1 07 Farah, Ashraf 1 55 Farah, Ashraf 2 71 Garg, P.K. (See Mondal, M. Surabuddin) 2 93 Ghose, M.K. (See Sharma, L.P.) 2 113 Ghosh, Jayant Kumar 1 01 Gurugnanam, B. 1 49 Hooda, R.S. (See Yadav, Manoj) 1 59 Jasrai, Y.T. (see Shah, M) 2 127 Jayatheja, A. (see Navatha, Y.) 2 132 Jnr, Osei Edward Matthew (See Nana, Osei Kingsley) 1 31 Joseph, Oshode (See Omogunloye, O.G.) 1 43 Kamini, J. (See Rajani, M.B.) 1 37 Kappas, Martin (See Mondal, M. Surabuddin) 2 93 Kulshreshtha, Aviral 2 138 Kumar, Arun 1 11 Kumar, Arun (See Chingkhei, R.K.) 2 85 Kumaravel, S. (See Gurugnanam, B.) 1 49 Kumar, C. Udhaya (See Kumar, R. Suresh) 1 17 Kumar, R. Suresh 1 17 Laskar, Anwarul Alam 2 120 Masela, Ogenga Anyango (see Paul, Obade Vincent de) 2 142 Menaka, C. (see Kumar, R. Suresh) 1 17 Mishra, Shweta (See Sharma, Shashikant A.) 2 109 Moawad, Moawad Badawy 1 23 Mohapatra, S.N. (See Sinha, D.D.) 2 104 Mondal, M. Surabuddin 2 93 Murthy, M.S. Krishna (See Rajani, M.B.) 1 37 Nana, Osei Kingsley 1 31 Nathawat, M.S. (See Dasgupta, Arunima) 2 76 Navatha, Y. 2 132 Olaleye, J.B. (See Omogunloye, O.G.) 1 43 Omogunloye, O.G. 1 43 Osei, E.M. Jnr. (See Amoah, A.S.) 1 07 Osei, K.N. (See Amoah, A.S.) 1 07 Pal, Om (See Yadav, Manoj) 1 59 Pani, Padmini (See Sinha, D.D.) 2 104 Patel, Nilanchal (See Sharma, L.P.) 2 113 Pathan, S.K. (See Dasgupta, Arunima) 2 76 Paul, Obade Vincent de 2 142 Phukon, Parag (see Laskar, Anwarul Alam) 2 120 Pratap, Deva (see Navatha, Y.) 2 132 Prawasi, R. (See Yadav, Manoj) 1 59 Rajani, M.B. 1 37 Rajawat, A.S. (See Rajani, M.B.) 1 37 Rajkumar (See Yadav, Manoj) 1 59 Rao, Srinivas (See Rajani, M.B.) 1 37 Reddy, K. Venkata (see Navatha, Y.) 2 132 Sajeeven, G. 1 05 Sarpong, Adjapong Adwoa (See Nana, Osei Kingsley) 1 31 Sasidhar, P. (See Sankar, Ram M.) 2 65 Sastry, K.L.N. (See Dasgupta, Arunima) 2 76 Shah, M. 2 127 Shankar, Ram M. 2 65 Sharma, L.P. 2 113 Sharma, M.P. (See Yadav, Manoj) 1 59 Sharma, Nayan (See Mondal, M. Surabuddin) 2 93 Sharma, Shashikant A. 2 109 Singh, Dinesh (See Ghosh, Jayant Kumar) 1 01 Singh, Th. Nixon (See Kumar, Arun) 1 11 Sinha, D.D. 2 104 Vasudevan, S. (See Gurugnanam, B.) 1 49 Vinoth, M. (See Gurugnanam, B.) 1 49 Vyas, Anjna (see Kulshreshtha, Aviral) 2 138 Yadav, Manoj 1 59

Journal of Geomatics vi Vol.6, No.2 October 2012

National Geomatics Awards - 2012

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Journal of Geomatics vii Vol.6, No.2 October 2012

Journal of Geomatics viii Vol.6, No.2 October 2012

INDIAN SOCIETY OF GEOMATICS FELLOWS

Prof. A.R. Dasgupta,AhmedabadDr. A.K.S. Gopalan, Hyderabad Dr. George Joseph, AhmedabadShri Pramod P. Kale, PuneDr. Prithvish Nag, KolkataDr. Baldev Sahai, Ahmedabad Dr. R.R. Navalgund Shri Rajesh Mathur

ISG - PATRON MEMBERS

P-1 Director, Space Applications Centre (ISRO), Jodhpur Tekra Satellite Road, Ahmedabad - 380 015P-2 Settlement Commissioner, The Settlement Commissioner & Director of Land Records-Gujarat, Block No. 13, Floor 2, Old Sachivalay, Sector-10, Gandhinagar – 382 010P-3 Commissioner, Mumbai Metropolitan Region Development Authority, Bandra-Kurla Complex, Bandra East, Mumbai - 400 051P-4 Commissioner, land Records & Settlements Office, MP, Gwalior - 474 007P-5 Director General, Centre for Development ofAdvanced Computing (C-DAC), Pune University Campus, Ganesh Khind, Pune - 411 007P-6 Chairman, Indian Space Research Organization (ISRO), ISRO H.Q., Antariksha Bhavan, New BEL Road, Bangalore 560 231P-7 Director General, Forest Survey of India, Kaulagarh Road, P.O. I.P.E., Dehra Dun – 248 195P-8 Commissioner, Vadodara Municipal Corporation, M.S. University, Vadodara - 390 002P-9 Director, Centre for Environmental Planning and Technology (CEPT), Navarangpura, Ahmedabad - 380 009P-10 Managing Director, ESRI INDIA, NIIT GIS Ltd., 8, Balaji Estate, Sudarshan Munjal Marg, Kalkaji, New Delhi - 110 019P-11 Director, Gujarat Water Supply and Sewerage Board (GWSSB), Jalseva Bhavan, Sector – 10A, Gandhinagar - 382 010P-12 Director, National Atlas & Thematic Mapping Organization (NATMO), Salt Lake, Kolkata - 700 064P-13 Director of Operations, GIS Services, Genesys International Corporation Ltd., 73-A, SDF-III, SEEPZ, Andheri (E), Mumbai - 400 096P-14 Managing Director, Speck Systems Limited, B-49, Electronics Complex, Kushiaguda, Hyderabad - 500 062P-15 Director, Institute of Remote Sensing (IRS),Anna University, Sardar Patel Road, Chennai - 600 025P-16 Managing Director, Tri-Geo Image Systems Ltd., 813 Nagarjuna Hills, PunjaGutta, Hyderabad - 500 082P-17 Managing Director, Scanpoint Graphics Ltd., B/h Town Hall,Ashram Road,Ahmedabad - 380 006P-18 Secretary General, Institute for Sustainable Development Research Studies (ISDRS), 7, Manav Ashram Colony, Goplapura Mod,

Tonk Road, Jaipur - 302 018P-19 Commandant, Defense institute for GeoSpatial Information & Training (DIGIT), Nr. Army HQs Camp, Rao Tula Ram Marg, Cantt., New Delhi - 110 010P-20 Vice President, New Rolta India Ltd., Rolta Bhavan, 22nd Street, MIDC-Marol,Andheri East, Mumbai - 400 093P-21 Director, National Remote Sensing Centre (NRSC), Deptt. of Space, Govt. of India, Balanagar, Hyderabad - 500 037P-22 Managing Director, ERDAS India Ltd., Plot No. 7, Type-I, IE Kukatpalli, Hyderabad - 500 072P-23 Senior Manager, Larsen & Toubro Limited, Library and Documentation Centre ECC Constr. Gp., P.B. No. 979, Mount Poonamallee

Road, Manapakkam, Chennai - 600 089.P-24 Director, North Eastern Space Applications Centre (NE-SAC), Department of Space, Umiam, Meghalaya 793 103P-25 Progamme Coordinator, GSDG, Centre for Development of Advanced Computing (C-DAC), Pune University Campus, Pune –

411 007P-26 Chief Executive, Jishnu Ocean Technologies, PL-6A, Bldg. No. 6/15, Sector – 1, Khanda Colony, New Panvel (W), Navi Mumbai – 410 206P-27 Director General, A.P. State Remote Sensing Applications Centre (APSRAC), 8th Floor, “B” Block, Swarnajayanthi Complex,

Ameerpet, Hyderabad- 500 038 P-28 Director, Advanced Data Processing Res. Institute (ADRIN), 203, Akbar Road, Tarbund, Manovikas Nagar P.O., Secunderabad –

500 009 P-29 Managing Director, LEICA Geosystems Geospatial Imaging Pvt. (I) Ltd., 3, Enkay Square, 448a Udyog Vihar, Phase-5,

Gurgoan- 122 016 P-30 Director, Defense Terrain Research Limited (DTRL), Ministry of Defense, Govt. of India, Defense Research & Development

Organisation, Metacafe House, New Delhi – 110 054 P-31 Chairman, OGC India Forum, E/701, Gokul Residency, Thakur Village, Kandivali (E), Mumbai – 400 101 P-32 Managing Director, ML Infomap Pvt. Ltd., 124-A, Katwaria Sarai, New Delhi – 110 016 P-33 Director, Rolta India Limited, Rolta Tower, “A”, Rolta Technology Park, MIDC, Andheri (E), Mumbai – 400 093 P-34 Director, State Remote Sensing Applications Centre, Aizawl – 796 012, Mizoram

Journal of Geomatics ix Vol.6, No.2 October 2012

Journal of Geomatics x Vol.6, No.2 October 2012

Journal of Geomatics xi Vol.6, No.2 October 2012

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Journal of Geomatics xii Vol.6, No.2 October 2012

Journal of Geomatics xiii Vol.6, No.2 October 2012

Indian Society of Geomatics (ISG) (www.isgindia.org)

Membership Application Form

To The Secretary Indian Society of Geomatics Building No. 40, Room No. 17, Space Applications Centre (SAC) Campus Jodhpur Tekra, PO AHMEDABAD – 380 015 Sir, I want to become a Member/ Life Member/ Sustaining Member/ Patron Member/ Foreign Member/ Student Member of the Indian Society of Geomatics, Ahmedabad for the year _____. Membership fee of Rs. _____ /- is being sent to you by Cash/ DD/ Cheque. (In case of DD/ Cheque No.__________, drawn on Bank ________________________________.(For outstation cheques, please, add clearing charges of Rs 65.00) I agree to abide by the Constitution of the Society. Date: Place: Signature 1. Name: _____________________________________________________________________________________

2. Address:___________________________________________________________________________________

_____________________________________________________________________________PIN: _____________

Phone: __________________ Fax: ________________ Email: ____________________________________________

3. Date of Birth: ________________________________________________________________________________

4. Qualifications________________________________________________________________________________

5. Specialisation: _______________________________________________________________________________

6. Designation & Organisation.: ____________________________________________________________________

7. Membership in other Societies: ___________________________________________________________________

____________________________________________________________________________________________

8. Mailing Address: _____________________________________________________________________________

__________________________________________________________________ PIN: _____________________

Proposed by: (Member’s Name and No) Signature of Proposer

For Office Use

ISG Membership No: ISG

Receipt No.: Date:

Journal of Geomatics xiv Vol.6, No.2 October 2012

MEMBERSHIP FEES

MEMBERSHIP GUIDELINES

1. Subscription for Life Membership is also accepted in two equal installments payable within

duration of three months, if so desired by the applicant. In such a case, please specify that payment will be in installments and also the probable date for the second installment (within three months of the first installment).

2. A Member of the Society should countersign application of membership as proposer. 3. Subscription in DD or Cheque should be made out in the name of ‘Indian Society of

Gematics’ and payable at Ahmedabad. 4. Outstation Cheque must include bank-clearing charges of Rs. 65.00 and direct deposit in

ISG A/Cs must include bank fee Rs. 25/-. 5. Financial year of the Society is from April 1 to March 31. 6. For further details, contact Secretary, Indian Society of Geomatics at the address given

above. 7. ISG has chapters already established at the following places. Ahmedabad, Ajmer, Bhagalpur,

Bhopal, Chennai, Dehradun, Delhi, Hyderabad, Mangalore, Mumbai, Mysore, Pune, Tiruchirappalli, Srinagar, Vadodara and Visakhapatnam. Applicants for membership have the option to contact Secretary/Chairman of the local chapter for enrolment. Details can be found at the website of the Society : www.isgindia.org .

8. Journal of the Society will be sent to Life Members by softcopy only.

S.No. Membership

Category

Admission fee Annual

Subscription Rs.

(Indian) Rs.

(Indian) US $

(Foreign)

1. Annual Member 10.00 --- 300.00 2. Life Member

a) Admitted below 45 years

of age

b) Admitted after 45 years

of age

2500.00

2000.00

250.00

200.00

3. Sustaining Member

--- --- 2000.00

4. Patron Member 50000.00 3000.00 --- 5. Student

Member 10.00 --- 100.00

Revised membership subscription will be applicable from 1st January, 2013.

NRSC Pata Centre NatignaL Remote Sensing Centre Phone: +91(40)2388 4422,4423,4425 Fdx:+91(40)2387 8158, 8664 Ematk sales@nrsc:gov.in Website: http://www.nrsc.gov.in

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