Characterization of tea plantations of Barak valley, Assam using remote sensing and GIS

CHAPTER-IV

LAND SUITABILITY ASSESSMENT FOR TEA PLANTATION

AND IDENTIFICA TION OF AREAS HA VING POTENTIAL

FOR NEW TEA PLANTATIONS

Chapter! 4

Land suitability assessment for tea plantations and identification of areas having potential for new tea plantations

4.1 Introduction

Tea cultivation is confined only to certain specific regions of the world due to

specific requirements of climate and soil. Majority of the tea producing countries are

located in the continent of Asia where China, India, Sri Lanka are the major producers.

Tea plantations in India are concentrated in Assam, West Bengal, and Himachal Pradesh,

and regions of Kerala, Kamataka and Tamil Nadu. The state of Assam is known

worldwide for the dominant role it has played in the field of tea production. It has

suitable geographic conditions necessary for tea plantations and as such the contribution

of tea towards state domestic product is very high in this state.

The most important categories of environmental information required forjudging

land suitability are climate, soil, topography and water availability. Assessing the

suitability of an area for crop production requires considerable effort in terms of

information that presents both opportunities and limitations to decision-makers.

Crop production is determined by land characteristics namely elevation, slope,

aspect, soil, landcover and climatic factors. All these factors collectively determine the

suitability of a given area for a particular type of crop cultivation. Thus in order to build

up an efficient crop production system, evaluation of land suitability from time to time is

essential. Land suitability classification is done through evaluation and grouping of

specific areas of land in terms of their suitability for a defined use. This suitability is a

function of crop requirements and soil/ land characteristics. Matching the land

characteristics with the crop requirements gives the suitability. Hence, suitability is a

50

measure of how well the qualities of a land unit match the requirements of a particular

form of landuse (FAO 1976).

Predictive modelling of species geographic distributions based on the

environmental conditions of sites of known occurrence constitutes an important technique

in analytical biology, with applications in conservation techniques and studies related to

ecology, evolution, invasive species management and other fields (Corsi et al. 1999,

Peterson and Shaw 2003, Peterson el al. 1999, Scott et al. 2002, Welk et al. 2002, Yom-

TovandKadmon 1998).

In this study, a modelling algorithm known as Maximum Entropy (MaxEnt) was

selected which works with presence only data. MaxEnt is a high performing Species

Distribution Modelling (SDM) method that uses species occurrence and environmental

data for predicting potential species distribution (Phillips et al. 2006, Elith et al 2006). It

is a machine algorithm that compares presence locations to environmental variables at

those locations and then across the study region to generate predictions of species

distributions in un-sampled locations. The algorithm was written by Steven Phillips, Miro

Dudik and Rob Schapire, with support from AT and T labs-Research, Princeton

University, and the Centre for Biodiversity Conservation , American Museum of Natural

History. Currently, it is one of the most commonly used niche modeling software. The

niche model for a species is constructed from a set of environmental raster layers which

precisely consists of grid cells together with a set of sample locations where the species

have been encountered. The model summarizes the suitability of each of the grid cells as

a function of the environmental data at the grid cells. High value indicates conditions

suitable for the species in the particular grid cell. The overall model is a representation of

the probability distribution over all the grids cells. MaxEnt was selected for the present

study because of the following reasons: It is deterministic in nature, it has a precise

mathematical definition, it can consider interactions and non-linear (quadratic)

relationships of the environmental data, it can handle both continuous as well as

categorical environmental data, it is possible to investigate variable importance through

jack knife procedure and finally it gives a continuous probabilistic output. Unlike other

modelling algorithms, it performs well with small sample sizes. MaxEnt facilitates

51

replicated runs to allow cross-validation, bootstrapping and repeated subsampling. After

the replicated run, the results of the analysis are combined in a HTML file for easy

interpretation.

4.2 Review of literature

Species distribution models (SDMs) have been used to predict the potential

distribution of living organisms, linking records or species abundance with environmental

constraints or spatial characteristics (Guisan and Zimmermann 2000). The models can be

used to provide understanding and generate predictions about species distributions across

a landscape. It has been successfully applied to terrestrial, freshwater and marine

organisms in studies investigating the conservation and management of species,

conservation of areas with high biodiversity, and studies on biogeography, invasive

species and global climate change (Carroll 2010, Gallien et al. 2010, Franklin 2009,

Benito et al. 2009, Demas et al. 2009, Elith and Leathwick 2009, Ferrier et al. 2002,

Guisan and Zimmermann 2000). The great demand for these kinds of ecological studies

has promoted the development of several SDMs computer-based programs using

different algorithms that search for accuracy of a given model and the best prediction

power (Fielding and Bell 1997). While knowledge on the distributional ranges of species

is clearly needed, the ranges of most species either remain largely unknown or are

incomplete, and most of the available studies are based on records from museum

specimens, which may be biased (Stockman et al. 2006). Regarding environmental

constraints, SDMs based on climate variables are considered useful tools for proposing

criteria to select strategic areas for the conservation of species (Acosta 2008, Rubio et al.

2010). Based on the expected climate tolerance of a species, it is possible to predict the

potential distribution area for that species when considering all field sites with similar

climate conditions (Acosta 2008). Furthermore, these models can be used for species with

few geographic records and scarce information on natural history (Guisan and

Zimmermann 2000, Brito et al. 2009, Baasch et al. 2010) and are therefore very

important when making decisions regarding the conservation of threatened species and

preservation of biodiversity (Brooks et al. 1999). These models have been applied

successfully in the design of conservation plans for rare species (Guisan et al. 2006).

52

Habitat distribution modeling or species distribution modeling (SDM) helps to

identify the areas for species reserves, reintroduction, and in developing effective species

conservation measures. It has been successfully used in restoring critical habitats and

predicting the impact of environmental and climate change on species and ecosystems

(Brooks et al. 2004, Giriraj et al. 2008, Franklin 2009, Gogol-Prokurat 2011, Barik and

Adhikari2011).

SDM have been used to predict potential suitable areas for the preservation of

endangered and rare species (Papes and Gaubert 2007, Solano and Feria 2007, Ko et al.

2009, Thorn et al. 2009, Gallagher et al. 2010, Rebelo and Jones 2010), for the

identification of potential sites for reintroduction or restoration (Klar et al. 2008, Kumar

and Stohlgren 2009) and for assessing potential effects of future climate change on

species distribution as well as on local species diversity (Pearson and Dawson 2003, Hole

et al. 2009).

Published studies on the suitability analysis of Camellia sinensis are scarce. The

present study proposes an alternative approach to land suitability analysis. Most of the

suitability analysis techniques described above rely on significant amount of information

to be useful, applicable, and effective. Species distribution modelling tools are becoming

increasingly popular in ecology and are being widely used in many ecological

applications (Elith et al. 2006, Pearson et al. 2006). These models established

relationships between occurrences of species and biophysical and environmental

conditions in the study area. It has performed well for mapping invasive species (Ward

2007, Stolgren et al. 2010, Jarnevich and Reynolds 2011). All research papers reviewed

depicted MaxEnt is superior in performance (Sergio et al. 2007, Phillips et al. 2006) than

ENFA and GARP methods. Phillips et al. (2006), described MaxEnt method performs

well even for small sample size. Hence, MaxEnt was adopted in the present study.

53

4.3 Methodology

4.3.1 GIS platforms for SDM

Arc GIS 9.2

Arc GIS is Geographic Information System (GIS) software for visualizing,

managing, creating, and analyzing geographic data. DEM, Slope, Aspect were prepared

using the spatial analyst extension in Arc GIS.

DIVA GIS

DIVA GIS is a free geographic information system (GIS) software which can be

used for mapping and geographic data analysis. It can be downloaded from www.diva-

gis.org, and the installation procedure is very simple.

ArcView 3.2

The spatial analyst extension along with some avenue scripts of the software is

required to import, slice, resample, and view the raster data on environmental variables.

The software is also needed to import and view the SDM results i.e. the ASCII files

containing the predicted distribution.

4.3.2 Generating data for SDM

For generating data for SDM, inputs required are Primary and Secondary Data

Sources. The primary inputs for running a niche model are:

4.3.2.1 Primary data: Georeferenced species locality data (i.e. species, longitude,

latitude)

28 primary distributional records of the species were collected randomly through

field surveys. The coordinates of all the occurrence points were recorded to an accuracy

of 30 m using a global positioning system (GPS). The co-ordinates were then converted

to decimal degrees for use in modelling the distribution of potential habitats of the

species in its native range as shown in Table 4.1.

54

Table 4.1 Geo-referenced species locality data

Species

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Camellia sinensis

Latitude

24.58385

24.59235

24.55885

24.45391

24.39634

24.55743

24.95414

24.65611

24.57333

24.58150

24.63250

24.94000

24.94794

24.82250

24.82217

24.66361

24.61528

24.62198

24.84528

24.62083

24.72667

24.94033

24.94906

24.99131

24.97709

24.91277

24.97869

24.94028

Longitude

92.54214

92.51765

92.65684

92.69368

92.54176

92.53688

92.83520

92.64944

92.48361

92.46718

92.66806

92.88778

92.88806

92.87806

92.87610

92.79639

92.65972

92.65395

92.93417

92.70222

92.78944

92.99438

92.00267

92.49566

92.69375

92.99236

93.04346

92.99429

55

4.3.1.2 Secondary data: GIS coverages for the environmental variables in ASCII

raster format

In the present study, remotely sensed data on elevation and topography were obtained

from CGIAR-CSI (http://srtm.csi.cgiar.org, Jarvis et al. 2008). Subsequently slope and

aspect were also obtained. These four environmental layers were used as input

environmental layers for running MaxEnt.

4.3.3 Maximum Entropy (MaxEnt Model)

Maximum Entropy (MaxEnt) is a general-purpose machine learning method with

a precise mathematical formulation (Phillips et al. 2006). The basic idea of MaxEnt is "to

estimate (approximate) unknown probability distribution of a species" (Phillips et al.

2006). The best approach is to ensure that the approximation should have maximum

entropy. Entropy is defined by Shanon, (1948) as how much 'choice' is involved in the

selection of an event". Thus, maximum entropy refers to maximum choice. Maximum

choice is available when there are fewer constraints (environmental layers), i.e.

unnecessary constraints should be avoided (Phillips et al. 2006). The technique first

constrains the modelled distribution to match certain features (environmental layers) of

empirical data (training data) and choosing the probability condition that satisfies these

constraints being as uniform as possible (Buehler and Ungar 2001). In this study MaxEnt

software, version 3.1 was used (Pilliphs et al. 2004, 2006). This algorithm was chosen

because it is applicable to presence-only data (Philliphs et al. 2004, 2006). It has been

shown to perform well when compared to other methods (Deblauwe et al. 2008, Wisz et

al. 2008). Default value for the conversion threshold (10" )̂, the maximum number of

iterations (500) and the logistic output format were used (Philliphs and Dukik 2008,

Morueta-Holme et al. 2010). Seventy five percent of the records were used for model

training and twenty five percent for testing.

56

4.3.4 Model evaluation

Evaluation of model was done using threshold - independent evaluation. It is an

approach which compares model performance using receiver operating characteristic

(ROC) curves. The main advantage of ROC analysis is that area under the ROC curve

(AUC) provides a single measure of model performance, independent of any particular

choice of threshold. Sensitivity and specificity are the terms used for positive and

negative instances. Sensitivity is also known as the true positive rate, and represents

absence of omission error. The quantity 1 - specificity is also known as the false positive

rate, and represents commission error. The ROC curve is obtained by plotting sensitivity

on the y axis and 1-specificity on the x axis for all possible thresholds. ROC analyses the

performance of a model at all possible threshold by a single number called, AUC (Elith et

al. 2006, Pearson et al. lOQl, Philliphs et al. 2009, Hu and Jiang 2010). The use of ROC

with presence only data, the maximum achievable AUC is less than 1 (Wiley et al. 2003).

Thuiller et al. (2003) have established a scale to enable interpretation of AUC

values and for model validation: 0.90 - 1.00 = excellent, 0.80 - 0.90 = good, 0.70 - 0.80 =

average, 0.60 - 0.70 = poor, 0.50 - 0.60 = insufficient. Random prediction corresponds to

an AUC of 0.5.

For an appropriate model, area of high probability will cover the majority of

presence records and areas with low probability will be characterized by low presence of

records (Yost et al. 2008).

4.3.5 Predictor Variable Importance

Importance of each environmental predictor variable was assessed using jackknife

operation. Jackknife operates by sequentially excluding one environmental variable out

of the model and running a model using the remaining variables. It also runs a model

using only the excluded variable in isolation. As a result, the gain contribution of each

variable to the total gain of the model (inclusive of all variables) can be calculated. A

variable which decreased the total gain of the model higher than all the other variables

when excluded and as well as a variable which contributed the highest gain when used

alone was identified as the most important variable. Variables that produce the highest

57

training gains are considered to be the most important predictor variables (Kouam et al.

2010). The output of maxEnt model is a continuous map.

4.4 Results

4.4.1 Digital Elevation Model (DEM) and Topography

Elevation of a geographic location is its height above a fixed reference point.

DEM are data files that contain the elevation of the terrain over a specified area, usually

at a fixed grid interval over the surface of the earth. DEM is expressed in meter.

Topography is the study of surface shape and features on the earth. In the present study,

remotely sensed data on elevation and topography were obtained from CGIAR-CSl

(http://srtm.csi.cgiar.org , Javis et al., 2008). Based on the DEM, the elevation of the

study area, ranges from 10 to 1,471 meters above the sea level as shown in Fig. 4.1.

4.4.2 Slope and Aspect

Slope is the incline or gradient of a surface and is commonly expressed in

degree. Slope is irhportant for soil formation and management because of its influence on

runoff, soil drainage, erosion, use of machinery, and choice of crops. Slope map was

prepared using surface analyst function in ArcGIS 9.2. Four slope categories were

delineated in the slope map. The slopes were calculated in degrees (°). The lowest slope

category (1-10°) represented very gentle slope and the slope levels gradually increased

from this stage to gentle sloping (11-20°), moderate sloping (21-30°), steep slope (31-40°)

and the category above 40° represented the very steep slope.

Aspect is the direction in which a slope faces. Aspect map was also prepared

using surface analyst function in ArcGIS 9.2. The study area falls into four categories:

North East, South East, South West and North West. The southern aspects i.e.. South

east. South west occupied more area than the northern aspect and is more suitable aspect

for tea plantation. Slope and aspect maps are important in deciding the suitability of any

landuse as the degree and direction of slope determines the type of landuse it can support.

They are also determinants of the distribution of plants. Thus slope and aspect maps

assist in prioritizing areas for developmental activities. The maps have been given in Fig.

4.2 and 4.3.

58

2 5 " 0 ' 0 " \ -

2443'0" N -

24"30'0" N •

24 15'0" N

24 O'O" N

92 15'0"F, 92'30'0"1': » i

92 45'()" E t

93 15'0"K 93 O'O" F. 1 I

-1 1 1 1 1 1 1 0 01 02 04Km

92"I5'fl" E 92°30'0" E 92 .15'()" E

» # ^ S U 25 !:-'()" \

- 25 O'O" N

j j j ^ l 10-66

( ^ • e 6 - 2 0 3

I [ 203 - 458

m 458 - 810

^ • e i O - 1.471

1 93'0'0" E

•24 45'0" N

24 30'0"N

- 2415'0". \

Fig. 4.1 Elevation map of study area

59

92°15'0"E 92°30 '0"E

25°0'0" N -

24 '45 '0" N

2 4 3 0 ' 0 " N -

24 r 5 ' 0 " N

240 ' ( ) " N

92°45'0" E 93 '0 '0" E

_ i 93°15'0" E

I

N

s

( — t — I — I 1 1—I 1 1 0 0 1 0 2 0 4 Km

H I 1 • 10 (Vety 0«nlto> m 11 • 20 (G«rrtl*)

I.. , I 21 - 30 (Moderate tiopa)

m 31 • 40 (StMp (lope)

^ B | > 40 (V»ry sMflp tlop«)

92 rS'O" E

• I

92 30 '0" E

1

92 45'()" E

1

93 O'O" E

25°15'0"N

25°0'0" N

24'45'0" N

- 24 30'0" IN

- 24M5'0"N

Fig. 4.2 Slope map of the study area

60

92'15 '0" E 1

92 30 '0" E 92 '45 '0" E 93 O'O" E I

93°15'n" E

N

S 25 15 '0" , \

25 O'O" N

24 45 '0" N '

24 3 0 ' 0 " N '

24 15 '0"N

. 25 O'O" N

24 O'O" N •

I 1 1 1 1 1 r 1 1

0 0 1 0 2 04 Km

Ncxth Easi

South East

South W M I

Nofth West

24 45'0" N

24°30'0" \

24 I 5 ' 0 " N

( 92-45'0" E

1

93 O'O" E 92°15'0" E 92°30'0" E

Fig. 4.3 Aspect of the study area

61

4.4.3 Species distribution map

The default output is logistic. It gives an estimate within 0 to ] of probability of

presence. The image uses colors to indicate predicted probability, with red indicating

high probability of suitable conditions for the species, green indicating conditions

typically of those where the species is found, and lighter shades of blue indicating low

predicted probability of suitable conditions as shown in Fig. 4.4.

The output map was then extracted and reclassified into highly suitable, moderately

suitable and marginally suitable giving thresholds. The map is shown in Fig. 4.5. Area

under different suitability grades for the optimal average model is shown in Fig. 4.6.

Potential habitat with high suitability thresholds were distributed in the lower

elevations with gentle slope of the three districts i.e., Cachar, Hailakandi and Karimganj

of Barak valley. Most of the areas fall under high suitability class and covers an area of

2825 km". Areas of very moderate suitability was restricted only to about 1489 km , and

area of low suitability was 2608 km^ as shown in Table 4.2.

Table 4.2: Area of habitat suitability for Camellia sinensis

Suitability

High

Moderate

Marginal

Total

Total area (Km^)

2825

1489

2608

6922

Area (%)

40.77

21.5

37.7

100

62

Fig. 4.4 Preliminary map of Distribution of Camelia sinensis in Northeast India

64

25 O'O" N -

24'45'0",N -

24 30'0" \

24°I5'0"N

24 O'O" N

92I5'0" !•: I

92"30'0" E 9215'0" F.

n_n_r

93"0'0" E (

93 I.VO" E

N

S

Legend

- 25 15'0" N

_̂ 25'0'0" N

-2445 '0 "N

I Kilometers 0 8,501)7,000 34,000 51,000 68,000

Highly suitable

Moderately suitable

Marginally suitable

1 1 93°15'0" E 92°30'0" E

1 92'45'0" E 93=0'0" E

- 24°30'0" N

24°15'0"N

Fig 4.5: Potential habitat distribution of the species in Barak valley, Assam

65

40.77 45 -

40

35

_ 30

£• 25 n £ 20 <

15 -I

10

5

0 •¥

ZIJ

High Moderate Marginal

Habitat suitability

Fig 4.6. Area under different suitability grades for the optimal average model

(The figures at the top of each bar represent the area under each class).

66

4.4.4 Analysis of variable contributions

Table 4.3 gives estimates of relative contributions of the environmental variables

to the MaxEnt model. To determine the first estimate, in each iteration of the training

algorithm, the increase in regularized gain is added to the contribution of the

corresponding variable, or subtracted from it if the change to the absolute value of

lambda is negative. For the second estimate, for each environmental variable in turn, the

values of that variable on training presence and background data are randomly permuted.

The model is reevaluated on the permuted data, and the resulting drop in training AUC

and is normalized to percentages. Values shown are averages over replicate runs.

Among the input environmental variables, elevation was the most influential and

contributed 90.2 % to the MaxEnt model. Slope and topography contributed 9.2 and 0,7

%. Aspect has no contribution to the maxEnt model. A possible reason could be the

resolution of SRTM data (90m), which may not be good enough to resolve the

importance of the variables.

Considering the permutation importance, elevation also had the maximum

influence on the habitat model and contributed 78.7 %. Slope contributed to 19.4 % and

topography contributed to 0.9 % while aspect contributed the lowest with 0.1 % as shown

in Table 6.2.

Table 4.3: Estimates of relative contributions and permutation importance of the

predictor environmental variables to the MaxEnt model

Environmental

variable

DEM

Slope

Topography

Aspect

Percent

contribution

90.2

9.2

0.7

0

Permutation

78.7

19.4

1.9

0.1

67

4.4.5 Analysis of receiver operating characteristic (ROC) curve

The red (training) line shows the 'fit' of the model to the training data. The blue

(testing) line indicates the fit of the model to the testing data, and is the real test of the

model predictive power. The turquoise line shows the random prediction. In the present

study blue line falls above the turquoise line. It indicates that the model is good. Fig 4.7 is

the receiver operating characteristic (ROC) curve for the same data, again averaged over

the replicate nms. The average test AUC for the replicate runs is 0.95, and the standard

deviation is 0.002.

ROC curves for species distribution map indicated high accuracy (AUCrraning =

0.96 (S.D. = 0.002) and AUC.est = 0.95 (S.D. = 0.002). AUC value shows that the model

predicted is a good model.

Training data (AUC = 0.966) Test data (AUC = 0.952)

Random Prediction (AUC = 0.5)

0.0 0.1 0.: 0.3 0.4 0.5 0.6 0.7 0.1 1 - Specificity (Fractional Predicted Area)

Fig 4.7: Receiver operating characteristic (ROC) curve of training and test data.

68

4.4.6 Jackknife evaluation

The blue bars indicate variable contribution to the model. The red bar is the total

gain of the model including of all variables. The jackknife operation resulted DEM as the

most important variable for suitability analysis of tea followed by slope. Variables such

as topography and aspect did not have much contribution to the total gain of the models

as shown in fig. 4.8.

^ h_aspect

h dem

h_slope

5 hjopoind c LU

Jackknife of regularized training gain for cs 4

Without variable With onlyvariatile With all variables

0.0 0,: 0.4 0.6 0.8 1.0 1.2 1.4 regularized training gain

1.6 20

Fig 4.8: Results of jackknife evaluations of relative importance of predictor

variables for Camellia sinensis MaxEnt model (Note: dem is digital elevation model,

topoind is topography).

The results of the actual habitat assessment of tea were verified tlirough field

survey and through landuse/ landcover map of the study area. The predicted potential

areas of tea under all suitability threshold levels i.e., low to high suitability, encompassed

all the LULC categories encountered in the study area viz. forest patches, river banks,

cultivated land, plantation etc.

69

4.5 Discussion

The present study showed that SDM can be used to predict potential species

distribution. Elevation played a key role in determining the distribution of potential

habitats o^ Camellia sinensis. The restricted distribution of the highly suitable habitats of

the species to the higher elevations indicates that the steep slopes are not suitable for

plantation. The suitable slope of tea plantation ranged between 11-40 degrees. The study

showed that 40.7 % of the total area is highly suitable while 21.5 % is moderately

suitable and 37.7% is marginally suitable for tea plantation.

The potential areas with a very high suitability threshold were distributed in the

central part of the study area which is mainly comprised of flat areas and small hillocks

(tillahs) included flat areas and small hillocks (tillahs) surrounding the waterbodies and

the elevation ranged from 40-150 meters above msl (mean sea level). The slope of the

tillahs ranged from 1 to 10 degrees and the aspect was North West.

ROC curves for species distribution map indicated high accuracy (AUCTraning =

0.96 (S.D. = 0.002) and AUC,est = 0.95 (S.D. = 0.002). AUC value shows that the model

predicted is a good quality.

The results of the actual habitat assessment were identified through field survey

and through landuse landcover map of the study area. Primary field survey revealed that

the predicted potential areas showing very high to high suitability areas were mostly

located in the northern part of the study area which also has the highest number of tea

gardens. The areas near the Son beel also fill under highly suitable areas.

Moderately suitable area which was classified as class 2 includes area with slope

ranged from 1-10 degrees. The elevation of moderately suitable area is of less than 40

meters above msl. The aspect was north east and landuse classes were cultivation areas

near the river bank. Some of the gardens which fall under this threshold were Cossipore

(Cachar), Lakhipur (Cachar), Silicoorie (Cachar) and Pramodenagar tea gardens.

The very low suitable areas were the uppermost northern zone of the study area.

The area is of high elevation with steep slopes i.e., more than 40 degrees and the

elevation of more than 159 meters above msl. The aspects were south west and South

70

east. The landuse classes were forest areas with high elevation and steep slopes. The

areas were Borail reserve forest, Patheria reserve forest and Barak reserve forest.

One of the main premises of the GlS-based land suitability analysis is that the

method can help to minimize and even solve conflicts among competing interests

regarding land use, by providing more, better data and information to resolve the

problems. Additionally, the results of this study could be useful for other investigators

who could use these results for diverse studies. «

The present study demonstrated that species distribution modelling could be of

great help in predicting the potential habitats of Camellia sinensis. Therefore, the result

would be quite useful for policy makers and planners for identification of areas having

potential for establishment of new tea plantations. The results obtained in the study could

best be used as baseline data. This study could have yielded useful hints for prospective

tea garden planners thus helping in better management of the tea gardens. Further

improvements on the model prepared in the present study could be done with high

resolution data and with more environmental variables for better assessment of habitat

suitability eg., soil, rainfall, climatic data etc.

71