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
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