Land capability and Crop Suitability using RS and GISijtsre.org › papers › 2018 › ev1c2 › 1846794291.pdf · 2019-08-03 · Land capability and Crop Suitability using RS and

International Journal of Technical & Scientific Research Engineering www.ijtsre.org

Volume 1 Issue 2, July-August 2018

Anjan Kumar Page 9

Land capability and Crop Suitability using RS and GIS

Anjan Kumar1, Ravikumar A.S

2

1(PG student, Water Resource Engg, Dept. of Civil Engg, UVCE /Bangalore University, India) 2(Associate Professor, Dept. of Civil Engg, UVCE /Bangalore University, India)

ABSTRACT: In the coming decades accelerating population growth, surface water pollution, and climate change together may produce a drastic decline in fresh water supply. Keeping the above factors in view, the quantification

and conservation of surface water resource is needed for ensuring livelihood. The study area chosen is Kabini

command area spread in Mysore and Chamrajnagar districts, Karnataka. The objective of the study is to assess the

capability of land for agriculture in order to make the land more profitable and also to determine the best suitable

areas for the crops viz,. Paddy, Sugarcane and Cereals. For LCC, slope, soil depth, texture and land use/ land cover conditions are assessed through remote sensing and GIS environment. The analysis reveals that 5 classes I, II, III, IV and V are present in the command area.

The evaluation of land in terms of the suitability classes is based on the method as described in FAO guideline for land evaluation for rainfed agriculture. A land unit resulting from the overlay process of the selected theme

layers has unique information of land qualities for which the suitability is based on. The selected theme layers includes soil texture, soil depth, land use/ land cover, rainfall, slope, aspect, pH, erosion, drainage density and

temperature which are overlaiyed in ArcGIS using Analytical Heirchical Process (AHP). It reveals that 4

classes has been identified as highly suitable (S1), moderately suitable (S2), less suitable (S3) and not suitable (N). KEYWORDS – Agriculture, AHP, GIS, land capability classification, land suitability, RS.

I. INTRODUCTION The most important aspects of land are its role in providing anchorage space to all resources and the

fact that most human activities take place on land. In the light of increasing population, the demand is also

increasing, thus optimum use of land has become a necessity. Land is also unevenly distributed in terms of its

qualities. Land can be improved for a particular use by certain measures, it can also be improved by a certain

kind of land-use or at least sustained production can be assured. Land can deteriorate by its mismanagement,

wrong land use or by certain cultivation practices, thus in order to avoid misuse and wastage of land. It should

be used judiciously considering its capability and suitability for particular use. To reduce the human influence

on natural resources and to identify an appropriate land use, it is essential to carry out scientific land

evaluations. Such kind of analysis allows identifying the main limiting factors for the agricultural production

and enables decision makers to develop crop managements able to increase the land productivity. The

importance of land classifications are divided based on the quality and intensity of its use hardly needs any

elaboration in land-use studies. Classifications of land mean assigning each tract, or piece of land within a

specific area its proper class within a system of classes. The system relates to a quality or characteristics of land

or distinctions of the quality or particular characteristics. In simple way land classification means dividing the

land into different categories or classes according to a single factor or a particular interpretation.

The capability classification provides three major categories of soil grouping viz., capability unit, capability sub-

class, and capability classes. According to this system the first category i.e., „Capability Unit‟ is a grouping of

one or more individual soil-mapping units having similar potentials and continuing limitations or hazards. The

soils in a capability unit are sufficiently uniform to produce similar kinds of cultivated crops and pasture plants

with similar management practices, require similar conservation treatment and management under the same kind

and condition of vegetative cover, and have comparable potential productivity.


Anjan Kumar Page 10

The second category, i.e. the capability „sub-class‟ is a grouping of capability units having similar kinds of

limitations and the symbol given for each is used as suffix to the concerned land capability class:

a) Erosion and runoff (including risk of erosion and post erosion damage)

b) Excess of water (wetness, high water-table, problem of drainage, overflow)

c) Root zone limitations (Shallow depth, low water holding capacity, salinity or alkalinity)

d) Climatic limitations.

Land capability sub-classes have been used widely in many parts of the world with slight modifications mostly

incorporating local hazards. The third category and broadest category in the capability classification is the

„Capability Classes‟. Capability classes are groups of capability sub-classes or capability units that have the

same relative degree of hazard or limitation. The risks of soil damage limitation in use become progressively

grater from class I to class VIII. The capability classes are useful as a means of introducing the map user to

obtain the more detailed information on the soil map.

Class I: Soils in land class I have either no or only slight permanent limitations or risks of damage. The soils are

deep, productive, easily worked, and nearly level and can be cultivated safely with ordinary good farming

methods.

Class II: Soils in this class, subjected to moderate limitations and moderate risks of damage. They can be

cultivated with easily applied practices. Soils in this class have gentle slopes, moderate depths which are subject

to occasional overflows and are in need of drainage.

Class III: Soils in class III are subject to severe limitations in use of cropland. They are subject to severe risks or

damages. They are moderately good soils and they can be used regularly for crops, provided they are planted

according to good rotations and given the proper treatment. Soils in this class have moderately steep, slopes and

are subject to more severe erosion.

Class IV: Class IV land is composed of soils, which are very severe permanent limitations or hazards if used for

cropland. The soils are fairly good and are frequently on steeps slopes and subject to severe erosion. They

should usually be kept in hay or pasture, although a grain crops may be grown once in five or six years.

Class V: Soils in class V should be kept in permanent vegetation. They should be used for pasture or forestry.

They have few or no permanent limitations and not more than slight hazards. Cultivation is not feasible,

however, because of wetness or other limitations.

Class VI: Class VI soil should be used for grazing and forestry and may have moderate hazards when in use.

They are subject to moderate permanent limitations and are unsuited for cultivation and are steep, or shallow.

Class VII: Soils in class VII are subject to severe permanent limitations or hazards when used for grazing or

forestry. They are steep, eroded rough, shallow, droughty or swampy. They are either fair or poor for grazing or

forestry and must be handled with care.

Class VIII: Soils in class VIII are rough even for woodland or grazing. They should be used for wildlife,

recreation or watershed uses.

Agricultural land suitability is an interdisciplinary approach. Determination of optimum land use type for an

area involves integration of data from various domains and sources like soil science to social science,

meteorology to management science. All these major streams can be considered as separate groups, further each

group can have various parameters (criteria). However, all the criteria are not equally important, every criteria

will contribute towards the suitability at different degrees. The relative degree of contribution of various criteria

can be addressed well when they are grouped into various groups and organized at various hierarchies.

Agricultural land suitability also involves major decisions at various levels starting from choosing a major land

use types (LUT), selection of criteria, organization of the criteria, deciding suitability limits for each class of the

criteria, deciding the preferences (qualitative and quantitative). Relative importance of these parameters can be

well evaluated to determine the suitability by multi-criteria evaluation techniques (Ceballos - Silva and Lopez-

Blanco 2003).

The present popular methods that are followed for land suitability analysis includes ranking and ratings,

weighted summation, requirement matching etc. The weights are arbitrarily chosen, and are aggregated using

simple Boolean overlay methods. Although these methods are simple and straight forward and lack solid

mathematical foundations.


Anjan Kumar Page 11

II. STUDY AREA The area chosen is Kabini command area which spreads in Chamrajnagar and Mysore districts. The

study area geographically lies between 760 12‟ 0‟‟ E and 77

0 12‟ 0‟‟ E longitude and 11

0 58‟ 0‟‟ N and 12

0

16‟ 0” N latitude with an area of 631.337 sq.km which is covered on Survey of India (SOI) Toposheet numbers

57H04, 57H03, 57D16, 58E01, 58A13, 57D12, 57D08, 58A05 and 58A01 on 1:50000 scale. The maximum

length and width of the command is 78.48 km and 53.39 km respectively. Kabini river is one of the major rivers

in Cauvery basin and constitutes C-2 subbasin. The river originates in Western Ghats at an altitude of 2134 m in

Wynad tauk, Kerala state and flows for a length of 212 km before joining the river Cauvery at Tirumakudalu

Narasipura, Karnataka state. Fig.1 shows the location map of Kabini command area.

Fig. 1. Location map of the study area

Data products The following data products are used in the present study:

i. Survey of India (SOI) Topomaps on 1:50,000 scale ii. Digital Elevation Model (DEM ) data iii. Rainfall Data iv. Hydrometeorological data v. Soil data vi. LANDSAT7 imagery and LISS III imagery (Table 1)

Table 1: List of data used Type of data Description of data Sources of data

Soil data Soil PH, soil depth, soil texture NBSS & LUP / KSRSAC , bengaluru

Climate data Rainfall and temperature data IMD, Bengaluru

Digital Elevation ASTER DEM (resolution: 90 m) USGS (Earth Explorer) Model

Satellite image Land use/land cover LISS III Imagery

III. METHODOLOGY 1. Data collection and preparation of Thematic layers:

A common base map is prepared from SOI topomaps on 1:50000 scale before thematic mapping. The base map

provides the basic details such as watershed boundary, latitude, longitude, major roadway, railway, rivers/

streams, water bodies, taluk boundaries, location of important settlements, etc. which serves as control points

during interpretation of remotely sensed data Land use/ land cover and soil data (soil pH, soil texture, and soil

depth) are collected from Karnataka State Remote Sensing Application Centre (KSRSAC and NBSS and LUP,

Bengaluru respectively. Climate data (rainfall and temperature) is obtained from Indian Meteorological Dept.


Anjan Kumar Page 12

(IMD), Bengaluru and Hydrometerological (HM) stations. There are four H.M stations are present in the study

area viz,. Gundal, Kabini, Suvarnavathy and Nanjangudu. Digital Elevation Model (DEM) is obtained from

USGS (Earth Explorer, SRTM 90m resolution). The mosaic has been processed and rectified to WGS84

coordinate system. Slope information is obtained from SOI Toposheets.

Thematic maps for each of the soil parameters and slope are prepared using ArcGIS10.1 software.

Annual rainfall and mean temperature, thematic maps are generated using Inverse Distance Weighted (IDW)

interpolation. IDW interpolation determines cell values using a linearly weighted combination of a set of

sample points.

Fig.2 shows the methodology adopted for preparation of thematic maps as per the IMSD (NRSA, 1995)

Technical Guidelines, NRSA, Hyderabad. Fig 3- 13 shows the various thematic maps of the study area.

Fig. 2 Methodology for preparation of thematic maps Fig.3 Land use/ land cover map of Kabini command Fig.4 Land use/ land cover map of Kabini command Fig.5. (DEM) of Kabini command


Anjan Kumar Page 13

Fig.6 Slope map of Kabini command Fig. 7 Soil depth map of Kabini command

Fig.8 Erosion map of Kabini command Fig. 9 Rainfall map of Kabini command

Fig. 10 Temperature map of Kabini command Fig. 11 PH map of Kabini command


Anjan Kumar Page 14

Fig.12 Aspect map of Kabini command Fig.13 Drainage density map of Kabini command

2. Land Capability classification The generalized methodology for Land Capability Classification (LCC) using GIS is depicted in the form of a

flow chart (Fig. 14). The geospatial technique helps in generation of a reliable spatial and non-spatial

information database. Such a database helps immensely in the efficient and scientific decision-making. The

procedure essentially consists of 2 stages;

i. To map the controlling and indicative parameters with the existing information and

ii. Integration of the controlling and indicative parameter layers digitally through GIS.

The land capability classification has been carried out by applying parameters like soil depth, soil texture, land

use / land cover, erosion and slope of land. This information is used as a basis for placing lands in capability

classes and subclasses. These informations are related with soil depth and soil texture about study region has

been used from National bureau and soil survey and land use planning (NBSS and LUP). Shuttle Radar

Topographic Mission (SRTM) data of the study region is used to assess the terrain conditions. 90 m data is

resampled to 30m by using 3D surfacing utility of Erdas 9.0 software to get better accuracy. Landsat ETM

image is used to assess the land use/land cover categories by applying Supervised Classification technique.

Here, the advance navigation technique like GPS has been used to collect training site data and to field check

classified datasets. These parameters are integrated in GIS environment by using intersect overlay technique.

Moreover the surface and overlay analysis capabilities in GIS can effectively facilitate in handling vast amount

of spatial information. The Intersect tool calculates the geometric intersection of input feature classes. The

features or portion of features that are common to (intersect) all inputs will be written to the output feature class

(Final LCC map).


Anjan Kumar Page 15

Fig. 14 Methodology used for Land Capability Classification

Table 2 shows land cover classification into land capability groups. Table 3 shows land capability classes,

characteristic features and recommendations. Table 4 shows Slope Classification.

Table 2 Land cover classification into land capability groups Land Capability Group

typ

e

I II III IV V

Kharif crop Tank (dry) Wasteland Wasteland with Settlement

Co

ver

with/without stony waste/

Double crop

Tank scrub rockout crops

La

nd

Agricultural plantation Stream/river

Forest

The depth of soil determines the effective rooting depth for plants, in accordance with texture, mineralogy and

gravel content the capacity of the soil column to hold water. Seven depth classes given by Sehgal et al. (1987)

ware used to classify the soil map in to depth class association (Table 5).

3. Crop Suitability Analysis 3.1 Selection of evaluation criteria:

The study of Kabini has shown that it is important to consider other terrain features in addition to soil

characteristics in order to arrive with a more informed decision on optimal crop growing areas. Opinions of

agronomist and literature review of various references helped in identifying criterias viz,. soil PH

, soil depth,

drainage density, rainfall, slope, temperature, aspect, erosion, soil texture and land use/land cover necessary to

determine suitable for growing paddy, sugarcane and cereals. Fig.15 shows the step by step procedure for

Hierarchical process.


Anjan Kumar Page 16

Fig. 15 Hierarchical organisation of the criteria considered for the study. The suitability levels are based on the Food and Agriculture Organization (FAO) land suitability classification

and ranked as highly suitable (S1), moderately suitable (S2), marginally suitable (S3) and not suitable (S4).

Suitability levels for each of the criteria are defined according to the FAO guideline for rainfed agriculture,

literature review and agronomist opinions (Table 6)

Table 6 Standard criteria for Major crops (NBSS & LUP)

1. Paddy crop

Sl Suitability class

Criteria

Highly suitable,

Moderately

Marginaly

No.

Not suitable, N S1

suitable, S2

suitable, S3

1 Temperature (o C)

30 - 34 35 - 38 39 - 40 > 40

21 - 29 15 - 20 < 15

2 Rainfall (mm) 750 - 900 < 750

3 Texture c, sic, cl, sicl, sl scl, sil, l sl, ls s

4 pH 5.5 - 6.5 6.4 - 7.5 7.6 - 8.5 > 8.5

4.5 - 5.4

< 4.5

5 Soil depth (cm) > 75 51 - 75 25 - 50 < 25

6 Slope 0 - 1 1 -- 3 3 --5 > 5

2. Cereals/Pulses

1 Temperature (o C) 25 - 28 22 - 24 20 - 21 < 20

2 Rainfall (mm) 800 - 1000 600 - 800 400 - 600 < 400


Anjan Kumar Page 17

3 Texture sl, sclsil, cl, sic, sicl, c ls

4 pH 6 -7.5 7.6 - 8 8.1 - 9 > 9

5.5 - 5.9

4.5 - 5.4

5 Soil depth (cm) > 100 85 - 100 40 - 45 < 40

6 Slope < 3 3 - 5 5 -10

3. Sugarcane crop

1 Temperature (o C)

30 - 34 35 - 38 39 - 40 > 40

26 - 38 25 - 20 < 20

2 Rainfall (mm) 750 - 900 < 750

3 Texture c, sic, cl, sicl, sl scl, sil, l sl, ls s

4 pH 7 - 8 6 - 6.9 4 - 5.9 < 4

8.1 - 9

9.1 - 9.5 < 9.5

5 Soil depth (cm) > 100 150 - 75 75 - 50 < 50

6 Slope < 3 3 - 5 5 - 8 > 8

( s- sand, ls – loamy sand, sl – sandy loam, scl – sandy clay loam, cl – clay loam, sil – silt loam, l – loam,

sic – silty loam, sc – sandy clay, c- clay) Suitability levels S1, S2, S3 and N are assigned score 9, 7, 5 and 3 respectively. Classes with higher scores are

most suitable for suitability evaluation. Using these scores and the defined suitability levels, all thematic maps

area reclassified.

3.2 Applying MCE and assigning weights : To determine the relative importance/weights of criteria, AHP method of MCE is used. In order to compute the

weights for the criteria, a pairwise comparison matrix (PWCM) is constructed using the information obtained

through interviews, each factor has compared with the other factors, relative to its importance, on a scale from

1/9 to 9 introduced by Saaty (2008). Table 7 shows the scale of comparison used for the present study.

Table 7 Scale of comparison

Degree of preference

Equal importance

Moderate importance of one thematic layer over another

Strong importance

Very importance

Extreme importance

Values for inverse comparison

The diagonal elements of PWCM are assigned the value of unity (i.e., when a factor is compared with itself).

Since the matrix is symmetrical, only the lower triangular half actually needs to be filled in. The remaining

cells are then reciprocals of the lower triangular half (Kihoro, Njore & Murage 2013).

The normalized matrix is derived from pairwise comparison by adding the entries in each column of

comparison matrix and then dividing each entry ajk with the sum of the entries the corresponding column ∑ajk

of the comparison matrix. The sum of normalized entries in each column will be equal to 1. Therefore, each entry in the normalized matrix = ∑ajk When performing pairwise comparison, some incosnistencies may typically arise. The AHP incorporates an

effective technique for checking the consistency of the evaluations made by the decision maker. In the AHP,

the pairwise comparison in a judgment matrix are considered to be adequately consistent if the corresponding

consistency ratio (CR) is less than 10% (Triantaphyllou & Mann 1995).


Anjan Kumar Page 18

To calculate CR, the consistency index (CI) is estimated by multiplying judgement matrix by the approximated eigen vector. Each component of the resulting matrix is then divided by the corresponding approximated eigen vector. This yields an approximation of the maximum eigen value (⋋ ). Then, the CI value is calculated by using the formula: CI = (⋋ max − )/( − 1). Finally, the CR is obtained by dividing the CI value by the Random Consistency Index (RCI) as recommended by Saaty, (1980). Table 8 shows the random index (RI) values, Table 9 shows the pairwise comparison matrix and Table 10 shows the normalized matrix for the thematic layers.

Table 8 Values of Random Index (RI) for number of thematic layers (n)

n 1 2 3 4 5 6 7 8 9 10

RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.51

Table 9 Pairwise comparison matrix of thematic layers

Table 10 Normalized matrix / priority matrix

3.3 Overlaying map layers: The reclassified thematic maps / layers of each variable are weighted using the weights derived from the

AHP process. The weighted maps / layers are combined by performing the weighted overlay using spatial

analyst tools. Finally, the suitability map is prepared.


Anjan Kumar Page 19

IV. RESULTS AND DISCUSSIONS For LCC, slope, soil depth/ texture and land use/ land cover conditions are assessed in remote sensing and

GIS environment. As these parameters have greater influence on capability of land. Five different classes

(Fig.16) are observed in the command and tabulated in Table 11.

Table 11. Agriculture land

capability classification for Kabini

command area

Sl Land Capability Area %

no. Class (sq.km) Area

1 Class I 134.65 21.95

2 Class II 215.46 35.13

3 Class III 245.53 40.03

4 Class IV 16.63 2.71

5 Class V 1.086 0.18

TOTAL 613.35 100.00

Fig. 16 Agriculture Land Capability Classification map for Kabini command area The weighted overlay analysis carried out using the criteria layers with their respective weights generated a

combined suitability map (Fig. 17, 18 & 19). From the Table 12, reveals that 17.07 % as highly suitable,

226.64 sq.km (36.92%) as moderately suitable and 266.83 sq.km (43.41%) as marginally suitable and 15.98

sq.km (2.6%) not suitable for Paddy in Kabini command area. About 94.31 sq.km (15.57%) as highly

suitable, 271.13 sq.km (36.92%) as moderately suitable and 29.16 sq.km (4.82%) & 195.11 sq.km (5.09%)

not suitable for Sugarcane crop in Kabini command. About 207.66 sq.km (133.53%) as highly suitable,

333.52 sq.km (54%) as moderately suitable and 23.55 sq.km (3.81%) not suitable for cereals crops in Kabini

command. Fig. 17 Paddy suitability map for Kabini command area Fig. 18 Sugarcane suitability map for Kabini

command area


Anjan Kumar Page 20

Fig. 19 Cereals suitability map for Kabini command area

Table 12 Crop Suitability for Kabini command area

Sl Paddy Sugarcane Cereals

Class Area

% Area % Area

% Description

no.

(sq.km) Area (sq.km) Area (sq.km) Area

1 S1 104.89 17.07 94.31 15.57 207.06 33.53 Highly suitable

2 S2 226.84 36.92 271.13 44.77 333.52 54.01 Moderately suitable

3 S3 266.68 43.41 210.99 34.84 53.44 8.65 Marginally suitable

4 N 15.98 2.6 29.16 4.82 23.55 3.81 Not suitable

TOTAL 614.39 100 605.59 100 617.57 100

V. CONCLUSIONS

The methodology adopted here to indicate land capability classes for decision-making intervention. The

analysis reveals that 5 classes I, II, III, IV and V are present in the command area. Out of that Class V which is

not suitable for agriculture accounts 0.18 %. Class I is a dominating class as far as the areal extent is concerned

with 62.92 %. Class III & IV are most susceptible to land degradation which accounts for 10 per cent. The FAO (1976) has given a framework for land suitability analysis for crops in terms of suitability

classes from highlysuitable to not suitable based on the crop specific soil, climatic and topographic data. The

same framework has been incorporated in the study with some modification in order to make the situation more

compatible to Indian cases. Land suitability evaluation for crop suitability also highly dependent on specific

crop requirement. Parameters used for analysis of crop suitability are slope, drainage, depth, texture, rainfall,

temperature, etc. Suitability maps for all the crops are also developed. The suitability map for each crop is

classified I to IV suitability classes. Among these crops Paddy & Sugarcane has the more suitability in the

present study area.

REFERENCES [1] Ashwini R., (2007) Analysis of Land use Capability Classification using Remote Sensing and Geographical

Information System, M.E. Dissertation, Dept. of Civil Engineering, UVCE, Bangalore University, Bangalore.

[2] Bhaskar J. Das, Singal S. K., Ranvir Singh., (2008) Estimation of erosion in an ungauged Wetland Catchment,

Hydrology Journal, 31 (1-2), pp. 65-76.


Anjan Kumar Page 21

[3] Chow V.T., Maidment, D.R., and Mays W., (1988). Applied Hydrology, Mc Graw Hill, New York.

[4] Clarke J.I., (1966) Morphometry from Maps, Essays in Geomorphology, Elsevier Publ. Co., New York, pp. 235-274.

[5] National Bureau of Soil Survey and Land Use Planning (NBSS and LUP, 1998), Soils of Karnataka for optimizing

land use – Executive summary in co-operation with Government of Karnataka, Bangalore.

[6] Singh A.K., Bhattacharya A.K., (2006) Water management and crop production, Handbook of Agriculture,

Directorate of information and publication of agriculture. ICAR, New Delhi, pp. 300-348.

[7] Feizizadeh, B & Blaschke, T (2012), „Land suitability analysis for Tabriz country, Iran: a multi-criteria evaluation

approach using GIS‟, Journal of Environmental Planning and Management, vol. 1, pp 1- 23.

[8] Saaty, T, L 2008, „Decision making with the analytic hierarchy process‟, Int. J. Services Sciences, vol. 1, no. 1, pp.83-

98.

[9] Triantaphyllou, E & Mann, S,H (1995),‟using the analytic hierarchy process for decision making in engineering

applications: some challeges‟,Inter‟l Journal of Industrial Engineering: Applications and Practices, vol.2, no.1, pp.35-

44.

[10] FAO (1976). A framework for land evaluation, FAO Soil Bulletin, 32, Rome, Italy.

[11] Cellabos-Silva, A.and J. Lopez-Blanco (2003). “Delieation of suitable areas for crops using Multi-Criteria Evaluation approach and lan use/cover mapping: a case study in Central Mexico”. Agriculture systems 77(2): 117-136.

[12] Sehgal J. (1996). Pedology concepts and applications, New Dehli, Kalyani Publishers.

Documents

Land capability and Crop Suitability using RS and GISijtsre.org › papers › 2018 › ev1c2 › 1846794291.pdf · 2019-08-03 · Land capability and Crop Suitability using RS and