Terrain Characterization for Land Suitability Analysis of the Igo River Basin,
Eastern Himalaya, Arunachal Pradesh, India
J.S. Rawat
Research Scholar, Department of Geography
Rajiv Gandhi University, Rono Hills, Itanagar
Arunachal Pradesh-791111, INDIA
E-mail: jsr_06@rediffmail.com, Phone: 919863388150
Fax : 91-360-2277889
R.C. Joshi
Professor, Department of Geography
Kumaun University, Nainital,
Uttrakhand, INDIA
E-mail: joshircj@rediffmail.com, Phone 919410938847
Gibji Nimachow
Associate Professor, Department of Geography
Rajiv Gandhi University, Rono Hills, Itanagar
Arunachal Pradesh-791111, INDIA
E-mail: gibjirgu@gmail.com, Phone: 919436896698
Fax: 91-360-2277889
Terrain Characterization for Land Suitability Analysis of the Igo River Basin,
Eastern Himalaya, Arunachal Pradesh, India
Abstract
Terrain characterisation is a process through which fractal nature of terrain and its
biophysical processes are quantified or attributed into thematic layers. The synthesis
of thematic layers results different terrain units or land suitability class which is
important for planning, land-use and land management. This paper attempts to carry
out terrain analysis and land suitability classification of the Igo River basin, West
Siang District, Arunachal Pradesh (India). The terrain characteristics are generated
into physical, morphological, hydrological and other remote sensing based thematic
layers. The Spatial Multi-Criteria Evaluation (SMCE) module is used to standardize
and weigh the data. The final output, a composite index map, is sliced into three
suitability categories as highly suitable (S1), moderately suitable (S2) and marginally
suitable (S3). S1 and S2 occupy 96.50 km2 (31.45%) and 56.34 km2 (18.36%) area
respectively while S3 constitutes 154 km2 area forming 50.19% of the study area. The
terrain characterization based land suitability classification using remote sensing,
Geographical Information System (GIS) and SMCE is very significant in the
mountainous and inaccessible area like Arunachal Pradesh.
Keywords
DEM, Terrain Analysis, Land Suitability, SMCE, Remote Sensing, Arunachal
Pradesh
1. Introduction
Terrain consists of the physiography, lithology, morphometry, soil geography and to
some extends land cover (Meijerink, 1988). The abiotic attributes (relief, geological
or geomorphological processes, lithology, soil, etc) and hydrological condition
complemented by vegetation/land-cover types characterises the terrain, (Van Zuidam,
1985). Terrain analysis for land suitability is a process through which fractal nature of
terrains along with various biophysical processes are quantified or attributed into
thematic layers. These thematic layers containing information of local land system are
then synthesized through an overlay function in Geographical Information System
(GIS) which helps in delineating different suitability classes of terrain. The resultant
terrain units presented in the form of map and report are meaningful to a local user
(Ceballos-Silva and Lopez-Blanco 2003a). Quantification of terrain for land
suitability necessitates compilation of data on requirements of landuse/landcover,
determination of biophysical potentials and identification of more or less
homogeneous land mapping units (Kilic, et al., 2005). Thus, land suitability analysis
is an inventory on land resources in terms of limitations and potentials which is useful
in land management and planning. The process of land suitability classification is the
evaluation and grouping of specific areas of land in terms of their suitability for a
defined use (Chen et al., 2010; Bhagat et al., 2009). The objective of land evaluation
is to predict inherent capacity of a land unit to support a specific landuse for long
period of time without deteriorating, in order to minimize the socio-economic and
environmental costs (de la Rosa 2000). Since the dawn of agriculture and industrial
revolution, the pattern of human development rate and consumption of world resource
has brought unprecedented change on earth. In many areas the earth’s surface is
bearing scars of thousand years of human interferences. Moreover, about three-
quarters of the land surface is already unsuitable for crop cultivation, suffering from
severe constraints of being too cold (13%), too dry (27%), too steep (12%) or having
poor soil (40%) (Bhagat et al., 2009). The global concern about food security, quality
of future life and growing awareness of environmental degradation is posing serious
question to the achievements of science (Lashkar, 2003). Evaluation of land resource,
their management and planning, therefore, has become an important component of
sustainability throughout the world (Hall et al., 2000). The concept ‘sustainable
development’ popularized by World Commission on Environment and Development
(1987) and Earth Summits (1992 and 1997) is interpreted in several ways by various
disciplines. Application of this sustainability principle in land resource management
underscores both ecological suitability and economical viability (Van Lier, 1994).
The Food and Agricultural Organization (1976) proposes an approach for land
suitability evaluation in terms of suitability ratings from highly suitable to not suitable
based on the suitability of land characteristics. Hopkins (1977) presents a comparative
evaluation of alternative methods of assessing land suitability. Anderson (1987)
surveys different methods of land potentiality/suitability analysis ranging in degrees
of computational and analytical sophistication. Steiner (1983, 1987) reviews land
evaluation and site assessment (LESA) using USDA-recommended standards.
According to FAO (1993) Land suitability evaluation and agricultural land use
planning is very necessary and is the basic information for right decision making
afterward (Van Chuong 2008). Suitability analysis generally involves determining an
appropriate approach to combine these factors. The principal problem of suitability
analysis is to measure both the individual and cumulative effects of the different
factors. Some approaches of combining the factors are composite rating including
weighted composite rating (Anderson 1987), weighted factor method (Hopkins 1977),
various multi-criteria approaches such as compromise programming (Pereira and
Duckstein 1993), Prioritized Land Use Suitability (Xiang and Whitley 1994),
modified weighted factor (Diamond and Wright 1988), etc. Martin and Saha (2001),
Boonyanuphap et al., (2004), Kilic et al., (2005), Chen et al., (2010), Pirbalouti and
Golparvar (2008), Bhagat et al., (2009), etc., uses Remote Sensing and GIS for land
suitability classification. On the other hand, Prakash (2003), Keshavarzi et al., (2010),
Hartati and Sitanggang (2010), etc., employs fuzzy technique assess land suitability
for different crops while Pereira and Duckstein (1993), Lashkar (2003), CeballosSilva and Lopez-Blanco (2003a), Geneletti (2007), Van Chuong (2008), etc makes
use of Multi-criteria Spatial Evaluation in GIS.
Although FAO (1976 and 1985) proposes land suitability analysis mainly for crops
based on factors like crop requirement and limitations, management, land
development conservation and socio-economic conditions. The present study focuses
overall land suitability classification on terrain parameters for Igo river basin in
Arunachal Pradesh (India) using GIS and Spatial Multi-Criteria Evaluation (SMCE)
system. Since various morphological, hydrological and physical terrain conditions
sets different degree of limitation or potentialities on a parcel of land for its general
use, it is assumed that these terrain characters by and large helps in determining the
most suitable and sustainable use of the land particularly in the hilly area like
Arunachal Pradesh.
2. Study Area
For this study the Igo River basin in the West Siang District of Arunachal Pradesh is
delineated using Survey of India Topographical Maps (Figure 1). Bounded by 27o 46’
36” N to 27o 57’ 17”N latitude and 94o 35’ 35” E to 94o 54’ 39” E longitude, the Igo
basin covers about 306 km2 area. The southern part of the study area comprises Kimin
and Dafla formations (loose conglomerate, shale and sandstone) of Outer Himalaya
corresponding Miocene to Pleistocene. A tectonic belt i.e. Main Boundary Thrust
(MBT) passes through the middle of the study area delineating the Lesser Himalaya
from the Outer Himalaya. The maximum area contains rocks of the Lesser Himalaya
belonging to Paleoproterozoic and Lower Permian periods. The Dolimestone of the
Bomdila group occurs in the north-eastern part, Chilliepam formation of the Lower
Gondwana group is found in the western tip while the remaining parts of the Lesser
Himalaya consist of Miri formation. Physiographically, the undulating hills of low to
moderate altitude forms maximum parts of the area with scattered patches of plain
lands along river corridors. On an average, the area receives 2370 mm annual rainfall
with mean minimum temperature between 7.9º C in the month of January to 22.4º C
in July and the mean maximum temperature fluctuating from 16.4º C to 28.5º C in the
January and August respectively. The sub-tropical evergreen, tropical evergreen,
tropical semi-evergreen and moist deciduous forests form the natural vegetation in
study area.
There are 15 settlements in the study area, which are mainly villages. They are Garu,
Garu Camp, Rilu, Tapo, Igo Camp, New Dari, Old Dari, Dali, Dali Hydel, Dali Camp,
Ichi, Chisi, Padi and Rimi. The distant villages, like Tapo, New Dari, Old Dari and
Ichi, are connected by village road or foot-path tracks. Other settlements are found
along the road which cut across study area. The inhabitants being tribal communities
predominantly practice traditional slash-and-burn method of cultivation known as
jhum cultivation along the hillslopes and settled wet rice cultivation in the plains of
river corridors.
3. Data use and Methodology
The litho-structural characteristics are carried out following Kumar (1997), which is
compared with satellite images and cross checked during field survey. Since there is
no meteorological station in the study area, the rainfall data in the surrounding
stations are used for interpolation. Similarly, physiography and soil information are
digitized from the Natural Resource Atlas of Arunachal Pradesh. Digital Elevation
Model (DEM) is created by interpolating digitized contours combined with spot
heights of Survey of India (SOI) Topographical Maps. ITC methodology of Hengl et
al., (2003) is followed to optimize DEM and to remove the artifacts. Filtered DEM
with a pixel size of 10 m is then used for the extraction of morphological and
hydrological parameters. Besides, basin morphometry and drainage pattern analysis
are carried out with the help of drainage vector layer digitized from SOI Maps.
The IRS 1D multi-spectral LISS III 8 bits data of path/row 113/052 on 16th November
2002 (Table 2) is used for preparing landcover raster layer. The Normalized
Difference Vegetation Index (NDVI) and different colour composites are obtained
from Band2, Band3 and Band4 of LISS III. NDVI is used for masking vegetative and
non-vegetative areas. The vegetative layer is converted into fraction vegetation cover
in percentage (Ve) following relationship of the Zhang et al., (2002): Ve =
93.07466*NDVI + 8.79815. On the basis of Ve (canopy density) four classes of forest
are obtained using the classification scheme of Forest Survey of India. These are
Dense Forest (density > 70%), Moderately Dense Forest (40 to 70%), Open Forest (10
to 40%) and Scrub Forest (density < 10%). The non-forest layer is treated separately
to classify into settlement area, road, water bodies, and cultivated area, using False
Color Composite (FCC), Normalized Difference Water Index and Hybrid Color
Composite (HCC). HCC is PCA1 (First Principal Component of Band 1, Band 2,
Band 3 and Band 4 of LISS III), Ratio1 (Band 3/ Band 2 of IRS LISS III ) and Ratio2
(Band 3/Band 1) passed through red, green and blue respectively. With adequate
ground truthing, at every stage, the area is classified into different landuse/landcover
categories with an overall accuracy of 73.81%.
The lineament map is derived using LIN algorithm in the PCI Geomatica and
lineament density is calculated in raster format. Landslide Hazard Zonation is carried
out following landslide index method of ITC, Netherlands. The factors used are slope
gradient, slope length, slope aspect, slope type, generic landforms, physiography,
geology, lineament distance, road distance, drainage distance, altitudinal zone and
landuse/landcover. The soil loss intensity is obtained using Universal Soil Loss
Equation (USLE) developed by United States Department of Agriculture (USDA),
Wischmeier and Smith, (1978). Since DEM is prepared with a pixel size of 10 m and
all its derivates by default inherits this property, all other raster layers are resampled
in 10 m pixel size for raster analysis.
Finally, terrain analysis is performed using SMCE Module. The whole raster data
cube of the above mentioned terrain characters are submitted to SMCE module in
three sub-sets i.e. morphological, hydrological and other. They are further grouped
into constrains or factors and cost or benefits, standardize and weighed in a criteria
tree (Figure 2). The final output is a composite index map which is sliced into three
suitability categories as highly suitable (S1), moderately suitable (S2) and marginally
suitable (S3).
4. Result and Discussion
4.1. Terrain Characteristics
The result of the terrain characterization, as presented in Table 3, shows that slope
comprises more than 87% of the area, only about 3% is plain and rest are ridges, peak,
pits, etc. Physiographically, moderately dissected steep slope moderate erosion
constitutes 79.38% of the area. On the basis of texture the main soil types are fine
loamy, coarse loamy, loamy skeletal, clayey, fine loamy mixed. Altitude ranges from
202 to 1780 m with mean height is 798.6. The maximum occurring elevation is 380
m. The relative relief in 100 x 100 m dimension ranges from 0 to 230.80 m with an
average of 59.32 and maximum experienced relief of 60 m. Although, range of slope
magnitude varies from 0 to 572.30%, maximum slope is found in the lower values
with mean 54.31% and mode 66.50%. The standard deviation of 30.74 and coefficient
of variation 56.60 suggests wide variation in slope. The mean value of slope aspect
represents southern declination, while modal value indicates the northern aspect. The
values of shape complexity index shows that maximum areas have highly complex to
complex terrain which together constitutes about 85% area.
The analysis of spatial arrangement of drainage revealed dendritic pattern as most
common drainage pattern associated with Lower Gondwana rocks which indicate
homogeneous lithologies and uniform resistance. The sub-parallel trellis is observed
along the MBT and Siwalik group resembling simple folds characterized by parallel
anticlinal ridges and synclinal valleys. The master streams have frequent right-angled
bends along fault lines and tributaries maintain parallelism to MBT displaying
structural control. The rectangular pattern occurs in the northern dolomite areas which
are likely to have faults and joints controlling the courses of streams with wide
spacing and perpendicular bends. Chisi River flowing between parallel ridges displays
a rib like pattern. The straight lateral streams are due to slope factor and short
distance between ridges to longitudinal valley stream. Besides, there are several
examples of streams resembling radial patterns influenced by isolated hills and peaks
along the ridges.
Both stream numbers and stream lengths in Igo river basin and its sub-basins maintain
law of inverse geometric series with stream orders. The bifurcation ratio (Rb) up to
3rd/4th order of all basins is concentrated towards higher values showing poor
integration between streams. Beyond these orders the ratio shows more stream
integration with low values. However, the weighted mean Rb of Igo basin is 3.86
ranging between 2.76 to 5.17 in the sub-basins. The stream length ratio of Igo basin
and its tributary basins vary slightly along the successive orders because of the
varying topography and slope. The relief ratio, as shown in Table 4, is high in the subbasins like Rimi, Sikki, Siggi and Sike which lie along faults and thrusts showing high
intensity of degradational processes. The elongation ratio suggests strong relief and
steep slopes which ranges from 0.5 to 0.8. Similarly, the circulatory ratio indicates
youthful stage of dissection in Igo basin and sub-basins. Only four sub-basins (Kudo,
Dachi, Tumru and Rimi) have slightly circular basins indicating a matured stage of
dissection.
The drainage density in 100 m grids (0.01 km2) ranges from 0 to 891.4 m with an
average density of 88.92 m and standard deviation 150.29. Value 0 indicates no
stream which constitutes 28% area followed by moderate stream density covering
25% (Table 2). Very high density with stream length more than 400 m covers only
2.92% area. The flow length, a measure of distance travelled by runoff and
transported soil mass along the slope, ranges from 0 to 1582 m in Igo River Basin.
More than a quarter (27.93%) experiences the overland flow length varying in
between 100 to 200 m followed by 21% area having 200 to 300 m and 18% area
experiencing 300 to 500 m overland flow. The Compound Topographic Index (CTI),
a measure of runoff concentration or moisture distribution, ranges from 1 to 14 in the
study area. However, 64% area falls under 4 to 6 value categorized as moderate CTI.
Only 5% area experiences more than 8 CTI value. The Sediment Transport Index
(STI) expressing relative effects of topography on soil loss ranges from 0 to 481 with
an average of 23.54 and standard deviation of 22.50. About 61% of the study area
experiences moderate and high STI value.
Among the landcover categories, settlements occupy 0.27% and road cover 0.19%
areas. The wet rice fields concentrated along valley bottom forms 1.62% area where
as shifting cultivations along the slope or ridges constitute 2.90% of the study area.
The maximum area of the basin is found under the dense forest spreading over
42.23%, moderately dense forest covers 22.10%, open forest 27.32% and scrub forest
constitutes 1.99% area. The lineament density under 250 m grids (0.063 km2) varies
from 0 to 790.9 m. About 50% of area falls under no lineaments category with
lineament density 0. The maximum area falls under high lineament density varying
from 200 to 400 m which comprises 26.50%. About 11% of the area has moderate
lineament density ranging from 100 to 200 m. Due to the undulating topography and
loose lithology, Igo basin experiences frequent landslides. About 30% area falls under
each moderately low and moderate landslide category followed by high landslide
category covering 22% area. The very high and extremely high landslide together
constitutes about 10% of the area. Similarly, very high annual soil loss occurs in Igo
river basin which ranges from 0.01 to above 770 ton ha-1 year-1. Although maximum
area falls under very slight soil loss of below 1 ton ha-1 year-1 followed by 24% area
under moderate soil loss of 20 to 50 ton ha-1 year-1, severe to extremely severe
together constituting 15% area experience soil loss to the tune of 100 to 200 ton ha-1
year-1 and above.
4.2. Land Suitability Classification
The synthesis of physical, morphological, hydrological and other remote sensing
based parameters through composite index of SMCE results into three suitability
classes (Figure 3). These suitability classes represent the three suitability degrees of
FAO (1976). Accordingly, the classes are named as highly suitable (S1), moderately
suitable (S2) and marginally suitable (S3). The composite index ranging from 0.576 to
0.86 are grouped under S1 category (Table 5). It covers 96.50 km2 area forming
31.45% of the basin. The composite index varying from 0.29 to 0.57 has been
assigned S2 class which occupies 56.34 km2 area forming 18.36% of the total. The
areas having composite index from 0 to 0.29 are named as S3. S3 covers almost half
of the study area accounting 154 km2. This is mainly because of the rugged nature of
topography with high gradient of slopes.
Land suitability classification carried out by Ceballos-Silva and López-Blanco (2003a
& b); Van Chuong (2008); Pirbalouti and Golparvar (2008); Martin and Saha (2009);
Bhagat et al., (2009); Keshavarzi (2010) focuses on the crop requirement using
climate, soil properties and relief or slope parameters. On the other hand Chen et al.,
(2010) conducts biophysical evaluation of land suitability for irrigation intensification
or extensification on five criteria like slope, soil texture, depth to water table,
electrical conductivity of ground water and hydraulic conductivity of soil. Similar to
the result of Chen et al., (2010), S1 in the present study is mainly concentrated in the
valley plains and S3 in extremely steep slope and rugged topography. The valley
plains being good in soil depth, well drained, and conveniently irrigated are used for
paddy cultivation. S2 mainly occurs between S1 and S3 in patches and sometimes
parallel to other two categories. The degree and magnitude of constraints in this
category is relatively higher than S1 and less severe than S3. However, in S3 there are
severe limitations and constraints on the land for its sustainable application to any
type of use.
Land suitability indices reflect inherent capacity of the land (Braimoh et al., 2004). In
well applicable land suitability approach subtle differences in land characteristic is of
the major interests (Keshavarzi et al., 2010). Further, the multiple integration options
in GIS are of immense use for data integration and overlay analysis to obtain better,
faster and cost-effective assessment for judicious utilization and allocation of natural
resources (Chen et al., 2010; Martin and Saha 2009). S1 is characterized by gentle to
very gentle slope, very low to low relief, least severe soil loss and landslide hazards,
etc. The areas with gentle slope are found to be ideal for shifting cultivation. In Dali,
Chisi and Padi, people grow fruits, palm tree (Livistona jenkinsiana Griff - locally
called Toko) and bamboo along the gentle to moderate slope areas in S1. Plain and
gently sloping areas along the ridges are used for growing chilly as cash crop through
shifting cultivation. However, in the higher reaches and far off areas such gentle slope
ridges are still covered by dense forest. In S2, slope is moderately steep to steep, relief
is moderate, and the hazards are less severe. Although some areas are already brought
under the human use (mainly shifting cultivation), the major chunk is still under
forest. However, the density of the forest cover in many cases is not up to the level to
ensure protection of soil loss hazards especially in open and scrub forests. S3 is
constrained by the combination of unfavourable factors like very steep to extremely
steep slope, high to very high relief, high soil loss due to shifting cultivation, landslide
hazard controlled by structure or often triggered by anthropogenic activities and areas
covered by water bodies (river bed including flood plains). Although some pockets
are under dense forest cover; but to bring these areas under particular land use is cost
prohibitive. Due to the high cost and hard nature of terrain the degree of human
interference in this category is already very low. However, some portions have their
genesis to anthropogenic causes especially those patches highly affected by severe
soil loss and landslide hazards. The magnitude of the soil loss is very high ranging
from 50 to above 200 ton ha-1 year-1.
SMCE of climate, soil and relief environment-components is useful to delineate
suitable areas for production and the SMCE–GIS combination has potentiality to
provide a rational, objective and non-biased approach for making decisions in
agricultural applications (Ceballos-Silva and Lopez-Blanco 2003b). Terrain
characterisation (Table 3) shows that in Igo basin about 5% of the total area is under
the effective use for settlement and agriculture while another 29% area is under open
and scrub forests which are cumulative result of recurrent shifting cultivation and
extraction of forest product. The result of present land suitability, on the other hand,
shows about 50% area under highly suitable and moderately suitable category. This
shows the potentiality of extension for different landuse practices. The S1 in the
valley plain can be retained for wet rice cultivation with some improved support
practices (Chen et al., 2010). However, in study area maximum area under cultivation
is used for shifting cultivation which is associated with high rates of soil loss and
sometime landslides. The traditional protection methods in jhum are not adequate to
prevent top soil erosion. Unfortunately, concentration of the most erosive rain also
coincides with early stage of jhumming. On the other hand, jhum has become rather a
cultural choice and lifestyle than merely an agricultural practice for its being deeply
rooted in tradition, belief, taste, festivals, legends and myths of the tribal communities
(Rawat et al., 2010). It is due to this fact any alternative to shifting cultivation is not
acceptable to tribal people in Arunachal Pradesh. Therefore, the need of the hour is to
improve jhum on scientific basis with more conservation measures for protection of
environment and land management. The extension of silvi pastoral system, sericulture
cum agro-forestry, and other methods of multi-storied agro-forestry could serve as an
environmentally suitable and economically viable practice in the hilly parts of the
study area.
6. Conclusion
The land suitability classification based on terrain parameters like morphological,
hydrological and other physical parameters gives satisfactory result. Each land
suitability category is expressed in terms of the degree of limitation and potentials of
selected parameters for its sustainable application. GIS based terrain characterization
and its application for land suitability assessment is a new approach which may serve
as effective tool for land use planners and land management bodies. There has been
very scanty works carried out in Arunachal Pradesh and data for many variables are
not available. Thus land suitability classification on terrain characters using GIS and
remote sensing would be very effective and useful way of land assessment for the
mountainous and inaccessible area like Arunachal Pradesh. The SMCE allows
integration and synthesis of large numbers of terrain data and provides land suitability
classes as per the desired criteria and goal. In comparison to the conventional GISbased analysis, SMCE is more flexible, easy and efficient for handling large size data
cube in different sets and sub-sets. Since the jhum is predominant practice in
Arunachal Pradesh accompanied by other different method of cultivation, this study
demonstrates the overall land suitability classification. This approach can be extended
to a crop based or cultivation-type based suitability assessment and for other land use
planning. Determination of the parameters and their weighing is vital because they
directly influence the evaluation result. Hence, adequate precision is required to be
accorded while selecting parameters, standardizing and weighing parameters for the
defined goal.
7. Acknowledgment
This paper is a part of the Ph D thesis of Mr. J.S. Rawat under the Supervision of
Prof. R.C. Joshi, DEAN, Environmental Sciences, Rajiv Gandhi University, Rono
Hills, Itanagar, Arunachal Pradesh (India). Authors are thankful to Department of
Science & Technology, Ministry of Science and Technology; Government of India for
providing Senor Research Fellowship to Mr. Rawat in a Research Project entitled “An
Assessment of Soil Loss using GIS” which facilitated the accomplishment of Rawat’s
Ph D work.
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List of Tables
Table 1: Material used for land suitability analysis
Table 2: Spectral characteristics of IRS 1D LISS III data
Table 3: Terrain Characters on selected Parameters
Table 4: Drainage Basin Morphometry
Table 5: Land Suitability Categories
List of Figures
Figure 1: Locational Map of the Study Area
Figure 2: Spatial Multi-Criteria Evaluation (SMCE)
Figure 3: Land Suitability