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CHAPTER 4

LAND DEGRADATION STUDIES IN THE NILGIRIS

4.1 INTRODUCTION

Land degradation is a composite term; it has no single readily

identifiable feature, but how one or more of the land resources (soil, water,

vegetation, rocks, air, climate, relief) has changed for worse. The land

degradation generally signifies temporary or permanent decline in the

productive capacity of the land. The main causes of land degradation are soil

erosion and deforestation. In the Nilgiris the degradation is caused by the

conversion of natural forest into commercial plantations, increased

agricultural/horticultural activities, urbanization and human influence and

natural causes due to inherent characteristics. For example the area under tea

has increased from 19,191 hectare in 1957 to 46,542 hectare in 1994.

Considering the fragile condition of the ecosystem of Nilgiris, it is

necessary to take up conservation measures in judicious manner to maintain

the ecological balance. In order to take up the remedial measures we have to

identify the highly degraded area which are to be tackled on priority basis.

The study has been conducted by preparing various thematic maps such as

landuse, slope, drainage density, lineament density and geomorphology and

assigning weight and rank based on their influence on land degradation. The

degradation status map is prepared by using weighted overlay analysis

method in GIS environment.

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4.2 METHODOLOGY

The study has been carried out in a phased manner. The different

phases are as follows:

Phase-1: Collection of data (Satellite, Conventional)

Phase-2: Preparation of various thematic maps/layers

Phase-3: Ground truth-using GPS survey

Phase-4: Post Ground Truth mapping

Phase-5: Final preparation of various thematic maps/layers

Phase-6: Digital Database Creation

Phase-7: Integration and overlay analysis

Phase-8: Zonation of Ecodegradable areas.

Figure 4.1 is a flow chart depicting the methodology adopted in this

study.

4.3 DATA COLLECTION

The first phase involved collection of various data and information

such as IRS-1B and Landsat-MSS satellite image data and conventional data

such as topographical maps from survey of India, Geological survey of India,

publication and reports from different line departments.

55

Figure 4.1 Flow chart

Satellite Data Conventional Data

Thematic Maps

Geomorphology Lineament LU/LC-1973 LU/LC-1993

Thematic Maps

Digitization

Analysis (GIS Integration)

Zonation of degradation prone areas

Topographical Data

Thematic Maps

* Drainage * Drainage Density * Slope

LAND DEGRADATION

Geology (From GSI)

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4.4 DATA USED

4.4.1 Satellite Data

Indian Remote sensing satellite, IRS IB LISS II (1993, Feb),

Geocoded, FCC.

LANDSAT MSS Data-1973.

4.4.2 Collateral Data

Survey of India. Top sheet No.58A and 58E

Geological survey of India – Geological

Map of Tamil Nadu on 1:500,000 scale and land slide report.

4.5 FIELD OBSERVATION

After carrying out preliminary interpretation of satellite data,

extensive field transverse in the district have been carried out between

Feb. 18-28, 2000. Detailed data / information were collected on various

features during the field survey. And GPS survey also carried out as a part for

ground truth verification.

4.5.1 GPS Survey

The acronym GPS stands for Global Positioning system which

comprised of 24 satellites or more. With a GPS receiver any one, any where

in the world can determine his/ here location at any time of day or night. The

GPS aided navigation has many advantages over conventional systems.

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GPS has enormously improved rescue operations because it has

now become possible to pinpoint the exact location of where the disaster has

occurred. It has also found application in the field of forestry.

GPS survey was carried out in the study areas to know the locations

of specific land use / land cover types as this information may be correlated

with the image data. Table 4.1 shows the coordinates of a few location

obtained by GPS survey and the corresponding land use/ land cover found in

the locations.

The above mentioned table has been helpful to understand the

different land use types and also in correcting the land use maps prepared

using satellite data.

4.6 DESCRIPTION OF THEMATIC MAPS

The entire Nilgiri district is covered in 2 top sheets on 1:250,000

scale. They are 58A and 58E, for each theme map has been described below:

4.6.1 Base Map

The base map is a cartographic base on which various thematic

information generated for the study is compiled. SOI maps were used for the

preparation of base map on 1:250,000 scale. All the major roads, railway

lines, rivers, major streams, big tanks, reservoirs, important settlements etc.,

are shown on base map.

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Table 4.1 GPS surveyed ground truth location

S. No. Lat Long

Ht. in Meters

Above MSL Land use / land cover

1 N11˚

26’30’’ E 76˚

40’ 20’’ +2123 m

Around the point the dry lake is present and opposite to the road the eucalyptus and pine trees are seen along with the built up land. Some settlements are also present nearer to the built up land.

2 N11˚]

26’ 34’’ E76˚

38’ 42’’ +2041 m

Around the point tall eucalyptus trees and tea plantation is seen. In the upland side there is some settlements for sheep breeding and above that forest area is starting.

3. N11˚

29’45’’ E76˚

39’ 39’’ +1742 m

The point is in the upland side around that the tea plantation along the down land and far away from the point there is some continuous hills are seen with dense forest and some of them are to being converted in to tea plantation.

4 N11˚

25’ 38’’ E79˚

55’ 49’’ +1664 m

Around the location both in the up and down land side tea plantation is seen named as Deepdale tea estate.

5 N11˚

25’ 37’’ E76˚

54’ 57’’ +1652

Around the point of location the tea plantation is seen along both the sides. Behind the point dense forest is seen.

6 N11˚

28’ 33’ E76˚

54’ 38’’ +1895 m

The place is called Hallada around that the built up land cabbage, beans, carrot cultivation is going on. In the up land side dense forest is seen.

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Table 4.1 (Continued)

S. No. Lat Long

Ht. in Meters

Above MSL Land use / land cover

7 N11˚

26’ 47’’ E76˚

57’ 41’’ +1346 m

The point is present on the way to Bomman tea estate. The tea plantation is seen on both sides of the road and some dense forest is seen far away from the point

8 N11˚

28’ 08’’ E76˚

58’ 39’’ +1603

The point is located near Kadasolai, around that point tea plantation seen. The hill areas near this point are covered with dense forest and in between the hills the Bhavanisagar reservoir is seen.

9 N11˚

25’ 49’’ E76˚

57’ 94’’ +1257 m

The point is present in the KKTA area where intense cultivation of tea is going on. Around that point the dense forest is continuing.

10 N11˚

26’ 30’’ E76˚

07’ 27’’ + 2093 m

The location name is Piemund. Near the point some built up land is seen and hills with moderate vegetation are also seen.

11

N11˚ 25’ 87’’

E76˚ 08’ 37’’

+2248 m

The point is located in the School compound. Near by some shola vegetation is seen.

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Figure 4.2 Slope map of the study area (Nilgiris)

4.6.2 Slope Map

The slope and aspect of a region are vital parameters in deciding

suitable landuse for the area, based on the degree, nature of slope and landuse

that could be supported. It is also helpful in prioritizing areas for development

measures and in deciding sites for hydel and irrigation projects, rail-road

alignment forestation programmes etc.

The slope map on 1:250,000 scale was derived from SOI toposheets

by ‘WENTWORTH’ method (Figure 4.2). A 1 cm square grid was prepared

for the study area in 1:250,000 scales and then it is superposed over the

toposeheet. The total number of contour cuttings for each grid is counted, the

total no. of contour cutting value converted into degrees and minutes and for

this the following formula used.

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Total no. of contour cutting/km Slope in degrees = ×contour interval Contour interval= 100 mts.

The slope classes are described in the following Table 4.2.

Table 4.2 Slope classes

Slope class (%) Description

0 – 1 Nearly level

1 – 3 Very gentle sloping

3 – 5 Gently sloping

5 – 10 Moderately sloping

10 – 15 Strongly sloping

15 – 35 Moderately steep to Steep sloping

Above 35 Very steep sloping

4.6.3 Landuse / Landcover Map

The landuse / land cover map was prepared by using two different

satellite image data such as:

LANDSAT MSS – FEB -1973

IRS IB LISS II – FEB – 1993 FCC on 1:250,000 scales, geocoded

along with GPS field survey observations and use of collateral data for

finalizing the map (Figures 4.3 and 4.4).

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Figure 4.4 Landuse/Landcover map 1993

Figure 4.3. Landuse/Landcover map 1973

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Nilgiri district is characterized by the presence of vast forest, tea

plantations, vegetable crops and barren steep slopes. The northern part of the

district is of deciduous forest area (Mudumalai), the western part is

predominantly grassland and shola (Mukurthi). Tea plantations are seen

mainly in the central, eastern part of the district. Annual cropped areas are

seen in and around Ooty and Coonor and central part of the district.

Ever green forest, sholas are in seen Mukurthi and southeastern and

northeastern part of the district.

4.6.4 Geology Map

It is prepared from the preexisting GSI Geological mineral map on

1:500,000 scale converted to 1:250,000 scale (Figure 4.5).

Figure 4.5 Geology map of the study area

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Structurally the area is highly disturbed and is subjected to faulting.

Block faulting resulted in upliftment of the plateau. Due to the tectonic

activities the deep seated metamorphic rockshave undergone considerable

deformation during Precambrian times which resulted in different structural

features such as folds, faults and joints. Laterites are found in large quantities

in the district. Bauxite with other minerals are also found in the district.

4.6.5 Geomorphology Map

It is prepared using IRS-IB LISS II geocoded FCC on 1:250,000

scale of Feb. 1993 (Figure 4.6). The collateral data and field observation are

used for finalization of the map. The various land forms identified are

structural hill, denudational hill, hill slopes, bazada, barried pediment, barried

pediment (deep) and shallow pediment. The entire district is comprised of

plateau land forms Number of large and small fractures is seen in the district.

Figure 4.6 Geomorphology map

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4.6.6 Drainage Density Map

The drainage morphometry throws, light on lithologic, structural

controls of the watershed, relative run off, recharge, erosional aspects and etc.

in the study area the drainage pattern is parallel to sub parallel, dendritic to

sub dendritic and trellis.

Drainage density map was prepared by using watershed map. The

following formula is used to calculate D.D.

Total length of streams Drainage density = Area of microwater shed

The drainage density contour map was drawn using spatial analysis

in ARC VIEW VERSION (Figure 4.7).

Figure 4.7 Drainage density map of the study area

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4.6.7 Lineament Density Map

From lineament map, the lineament density map was prepared. The

lineament density map is divided into five groups. Finally suitable rank and

wightage are assigned. The lineament density contour map was drawn using

spatial analysis in ARC VIEW VERSION (Figure 4.8).

Figure 4.8 Lineament Density map

4.7 DATA BASE CREATION

It involved the creation of digital database for entire Nilgiris district

on 1:250,000. all the thematic maps / layers were digitized in Geographical

information system (GIS) environment.

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4.8 DATA INTERGRATION

The spatial data for entire district integrated using the decision rules

arrived at by incorporating the inputs from various layers / maps.

4.9 GIS ANALYSIS

4.9.1 General

Geographic Information System is used to monitor the degradation

status. The major inputs are the various themes and their attribute table

generated. The degradation status is obtained by associating the relevant

weight and rank of the respective themes. The analysis is done on a polygon

by polygon basis on the composite coverage. The analysis is preformed by

using the basic overlay operations.

4.9.2 Concept of Model

The aim of study is to assess the degradation status in the Nilgiris

district. The study is carried out with considerations of relevant parameters

such as are Landuse / Landcover, geomorphology, slope, geology, drainage

density, and lineament density. Weights and ranks are assigned to each theme

separately for this study the required values, which is the sum of product of

weightage and rank of the scheme is given by

DI = ∑W*R (4.1)

where, DI - Degradation index

W - Weight of each theme

R - Rank of each class present in the theme

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4.9.3 Assigning Ranks and Weightage

It is the primary input for the multi criterion analysis. The weight

and ranks for the themes are assigned based on their influence on degradation.

The criterions, which are considered for the study, are Landuse/ Landcover,

geomorphology, Geology, slope, lineament density, and Drainage density.

4.9.3.1 Weightage for themes

It is determined by their close contribution to the zonation for the

degradation status. The separate weightages are given in the percentage as

given in Table 4.3.

Table 4.3 Weitage for themes

Themes Weights

1) Landuse / Land cover 30

2) Slope 30

3) Geomorphology 20

4) Drainage density 15

5) Lineament density 5

Total 100

4.9.3.2 Assigning ranks to the themes

4.9.3.2.1 Landuse/ Land cover

It is the indicator of present status of the land utilization, for the

degradation status. The suitability ranks are assigned by considering the dense

forest category is less prone for degradation. The next LU category is

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deciduous forest, which is of low rate degradation. The next group is the Tea,

coffee plantation that is moderately prone to degradation. The grasslands are

degraded easily attain higher rank The last LU categories Built up land attains

more erosion and attains higher rank of 5 (Table 4.4).

Table 4.4 Rank for landuse

Classes Category Rank

1 Dense forest 1

2 Deciduous forest 2

3 Tea coffee plantation 3

4 Scrubland 4

5 Grass land 4

6 Crop Land 4

7 Built upland 5

4.9.3.2.2 Slope

It is determined by the Physiography of the region. The slope and

aspect of region are vital parameters in deciding suitable landuse for the area.

In general, the terrain of the Nilgiris district is highly undulating and hilly.

The slope varies from gentle in valley region to very steep in dissected slopes.

Based on the slope classes a ranks are assigned as furnished in Table 4.5.

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Table 4.5 Rank for slope

Slope class (%) Rank

0 – 1 1

1 – 3 1

3 – 5 2

5 – 10 2

10 – 15 3

15 – 35 4

Above 35 5

4.9.3.2.3 Geology

The study area enormously comprises of major rock type of

charnockite which is prone for erosion due to joint and fracture pattern

present in that rock type, and also it is easily weathered by natural agencies so

it is contributing to the erosion.

4.9.3.2.4 Geomorphology

The ranks for the various geomorphic units are given in Table 4.6.

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Table 4.6 Rank for geomorphology

Classes Rank

Structural Hill 1

Denudational Hill 2

Hill Slopes 4

Buried Pediment 2

Bazada 3

Shallow Pediment 2

Buried Pediment (Deep) 1

4.9.3.2.5 Drainage density

The drainage density map is prepared from drainage map. The

ranks are assigned based on the drainage density as given in Table 4.7.

Table 4.7 Rank for drainage density

Drainage density Rank

Low 1

Moderate 2

High 3

Very high 4

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4.9.3.2.6 Lineament density

The lineament density map is prepared from lineament map. The

ranks for different categories are assigned as given in Table 4.8.

Table 4.8 Rank for lineament density

Category Rank

Low 1

Moderate 2

High 3

Very high 4

4.9.4 GIS Based Statistical Analysis and Zonation

The aim of this analysis is to Zonate the degradable areas. Using

the Overlay module of Arc/Info based GIS Software, the above thematic

layers were overlayed with suitable ranks and weights. The final map contains

numerous polygons having the characteristics of all above themes. In order

group them into four classes, a statistical analysis was made assuming the

distribution is normal. Considering the 1- sigma criteria, a final degradation

map has been delineated into four classes namely, highly degraded,

moderately degraded, less degraded and not degraded. The zonation

categories are based on the statistics generated from the composite coverage.

The separate statistics are given in the following Table 4.9.

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Table 4.9 Statistical analysis for zonation

Statistics Status (1973) Status (1993)

Mean 163 166

Range 0-100 0-100

Standard deviation 40 45

The mean value of 163 in Table 4.9 is arrived using the ranks and

weightages assigned to each theme for the year 1973 (Figure 4.9). The range

0-100, indicates the minimum and maximum values obtained using Equation

4.1. Similar computations have followed for the year 1993 (Figure 4.10). A

comparative figure showing the land degradation 1973 and 1993 are shown in

Figure 4.11. The areas under different degradation zones are given in Table

4.10.

Figure 4.9 Degradation status map-1973

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Figure 4.10 Degradation status map-1993

Figure 4.11 Degradation status 1973 and 1993

Highly Degraded Area Moderately Degraded Area Less Degraded Area Not Degraded Area

1973

1993

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Table 4.10 Areal extent of the types of degradation (in sq.km) in the

Nilgiris during 1973 and 1993

S.No. Description Areal Extent in Square Kilometer

1973 1993

1. Highly Degraded Area 331 855

2. Moderately Degraded Area 654 327

3. Less Degraded Area 746 938

4. Not Degraded Area 818 429

TOTAL 2549 2549

4.9.5 Conclusion

The degradation has been carried out for the years 1973 and 1992.

The study has been conducted by preparing thematic layers such as landuse,

drainage density, slope, geomorphology and lineament density. These themes

have direct influence on the degradation. Suitable ranks and weights are

assigned to the themes based on their influence on the degradation. Overlying

the thematic layers in the GIS environment, the areas are divided into four

zones based on the intensity of degradation.

From the degradation zonation map it is observed that during 1973

the highly degraded area is 331 sq.km and the areas not affected by

degradation is 818 sq.km. But, in the year 1992 the highly degraded area has

increased to 855 sq.km from 331 sq.km and the areas not affected by

degradation has reduced to 429 sq.km. From the figures we can understand

the intensity of the problem.

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Some of the factors which cause the degradation are increased tea

and annual crop cultivation in all slopes. From the landuse map prepared for

1973 and 1993 it seen that forest and shoals are converted into tea. The data

collected from the department indicate over the years area under tea has been

increased from 19191 ha in 1957 to 48170 in 1993. Contrary to the belief that

lush green tea estates does not cause the degradation, it is observed that

during initial four years of new tea plantation still the lushes came up, the soil

loss is estimated to be around 3 tons/year.

The deforestation and human impact are also major causes for

degradation the 1993 degradation status map show the higher degradation in

and around all the urban centers of Nilgiris district.

4.10 UNDERSTANDING THE STATUS OF SOIL EROSION IN

THE NILGIRIS USING SATELLITE DATA (RADIANCE

PARAMETERS)

4.10.1 General

Based on the fieldwork carried out and based on enquiries with the

local people, it has been realized that erosion in the Nilgiris district, is

prevalent. Watershed management options would include measuring and

quantifying soil erosion and degradation. Since the area is characterized by a

humid climate and mountainous terrain. Field-based methods would be

difficult, time-consuming and expensive. Review of literature on monitoring

of soil erosion suggests that there exist many field-based methods. In the

absence of data on the rates of soil erosion or degradation in an area,

surrogate measures of soil erosion are possible, which are based on

information derived from remote sensing imagery. A few researchers have

attempted to generate albedo images from Landsat satellite scenes, which

represent the spatial distribution, number, and size of soil patches. This

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information, when obtained in a temporal manner, can be directly related to

the extent of degradation an area has undergone. Soil Stability Index,

suggested by Pickup and Nelson (1984) is one such information that can be

generated for the Nilgiris to understand the process of erosion.

4.10.2 Objectives

The objectives of this chapter are listed below:

To generate the Soil Stability indices (SSI) for 1973 and

1992 for the Nilgiris using temporal satellite image data,

To explain the significance of SSI in relation to the status of

erosion/degradation or sediment deposition, and

To relate the imagery derived SSI values and the process of

degradation in the Nilgiris watershed from 1973 to 1992.

4.10.3 Previous Works

Robinove et al (1981) monitored the direct changes in the temporal

and spatial dynamics of soil albedo patches in arid lands by examining the

difference albedo image that results from subtracting albedo images from two

different time periods. The Landsat albedo, or percentage of incoming

radiation reflected from the ground in the wavelength range of 0.5 µm to

1.1 µm was calculated from an equation using the Landsat digital brightness

values and solar irradiance values, and correcting for atmospheric scattering,

multispectral scanner calibration, and sun angle. The albedo calculated for

each pixel was used to create an albedo image, whose grey scale is

proportional to the albedo. Differencing sequential registered images and

mapping selected values of the difference was used to create quantitative

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maps of increased or decreased albedo values of the terrain. The authors

conclude that decreases of albedo may indicate improvement of land quality;

increases may indicate degradation. This approach can be directly adopted

and tested for the Nilgiris watershed to understand the process of degradation.

There are many more studies that have directly related the

reflectance of soils to soil properties which affect reflectance, particularly

surface roughness, structure, moisture content, particle size distribution

(texture), and the content of organic matter, carbonates, clay, minerals, and

iron oxides (Myers and Allen 1968; Stoner and Baumgardner 1981; Wessman

1991). Frank (1984) examined the residual image that results from regression

analysis of different bands of Landsat imagery to quantify soil degradation,

while Kauth and Thomas (1977) examined the temporal trend of a soil

brightness index (SBI) generated using the Kauth-Thomas tasseled-cap

transformation.

In relation to soil erosion studies, it has been shown by Stoner and

Baumgardner (1981) and Wessman (1991) that increases in organic matter

content lead to a darkening of soils and increased reddening of soils is a

function of an increase in iron oxides. This increase in iron oxide is seen to be

consistent with the stage of erosion. Stoner and Baumgardner (1981) showed

that differing amounts of organic matter and iron oxide in soils could be

detected by the change in their spectral response curves and demonstrated that

iron oxide content increased with the degree of erosion. Frazier and Cheng

(1989) found that plots of the ratioed data could be clustered, subdivided, and

discretely mapped to the landscape to show various levels of organic matter

and iron oxides. Consequently, the authors mapped the amount of soil erosion

as these factors related to a change in soil quality at specific locations.

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Pickup and Nelson (1984) carried out a study on the red-yellow

soils within Australia's central arid region by stratifying a landscape's

land-forms into four categories of erosional status: stable, transitional,

depositional, and erosional. Reflectance in the four Landsat Multispectral

Scanner (MSS) bands was measured using an airborne Exotech radiometer for

groups of surfaces in each category and the response plotted against each

other to determine which band combinations (10 nonreciprocal combinations)

best separated the erosion categories in Cartesian space. The authors found

that the ratio of the MSS green (band 1, 0.5 - 0.6 µm) divided by the near

infra-red (NIR, band 3, 0.7 - 0.8 µm) or the 1/3 ratio, plotted against the ratio

of the red (band 2, 0.6 - 0.7 µm) divided by band 3 or the 2/3 ratio best

discriminated the surfaces in spectral space (Figure 4.12).

Figure 4.12 Landsat MSS bands 1/3 vs 2/3 and TM bands 2/4 vs 3/4 and

the discrimination of landform erosion states by the Soil

Stability Index (SSI) (Source: Pickup and Nelson 1984)

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Although MSS data are only available in four bands, a wide range

of derived parameters may be calculated. Some of these parameters may be

sensitive to different types of ground condition but others may be functionally

equivalent (Perry and Lautenschlager 1984). In this investigation, ten radiance

parameters were used, namely, the raw radiance values in each band and the

various band ratios.

4.10.4 Band Ratios

Band ratios were incorporated for several reasons. Firstly, they

have a tendency to partially normalize data for factors such as variations in

sun angle, haze, topography, and system-generated noise. This is important if

the derived soil stability index is to be used for multi-temporal comparisons to

indicate rangeland degradation or recovery. Secondly, band ratios minimize

albedo differences between soil or rock surfaces but enhance the subtle

differences between reflectance between bands that are diagnostic of

variations in surface material. Thirdly, ratios of visible to infrared bands

provide a good distinction between green vegetation and dry vegetation and

soil. This is particularly useful in distinguishing deposition areas, as these

usually have a more dense green cover than other soil stability types.

In the context of the present study in the Nilgiris, it must be

mentioned here that band ratios have been incorporated for the following

reasons:

1. The Nilgiris being a hilly terrain with typical undulating

topography, band ratios are best suited, as the process of

ratioing implifies the effect of undulations in the terrain and

also the effect of shadow caused due to undulation. This fact

has been well illustrated by Lillesand and Kiefer (2000).

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2. A multi-temporal comparison of land degradation has been

attempted here and hence, use of band ratios to compute SSI

is imperative.

3. Green vegetation, dry vegetation and soil are present in

various proportions in the watersheds of the Nilgiris in the

various years taken up for study. According to Pickup and

Nelson (1984) such a situation warrants the use to band

ratios to distinguish areas of deposition from the areas

characterized by erosion.

The derivation of an index of soil stability from the band 1 / band 3

and band 2 / band 3 values requires only simple trigonometry. The data space

consists of a lower line that appears to represent extensive deposition and an

upper line representing extreme erosion that is the opposite condition.

Intermediate states occur between the lines and as distance from the lower

line increases, the type of deposition becomes less extensive until a transition

to erosion occur. The erosion becomes progressively more developed as the

upper line is approached.

4.10.5 Generating Soil Stability Index for the Nilgiris

From the Figure 4.12, it may be inferred that the various zones in

the scatter plot are clearly related to the status of erosion in a given area. To

decipher the status of erosion in Nilgiris watershed, a multi-temporal image

data set was used to generate ratio images and then the SSI.

TM bands 2 (green), 3 (red), and 4 (NIR) are similar to the MSS

bands (Table 4.11) and can be directly substituted for them (Crist and Cicone

1984). Thus the TM band ratio 2/4 would be substituted for MSS band ratio

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1/3 and TM band ratio 3/4 for MSS band ratio 2/3. Consequently, Pickup and

Nelson (1984) found that not only could they map soil condition to the

landscape, but they had also produced a continuous measure of soil stability

that integrated vegetation reflectance characteristics by including the MSS

green band. Other workers determined the trend of soil condition using an

MSS time series and this analysis indicated when and where a landscape

changed to different states of stability (Pickup and Chewings 1988; Pickup

1990; Stafford-Smith et al 1992, Dube 1994).

Table 4.11 MSS and TM bands

Band No. MSS Bands μm TM Bands μm

1 0.5 – 0.6 0.45 – 0.52

2 0.6 – 0.7 0.52 – 0.60

3 0.7 – 0.8 0.63 – 0.69

4 0.8 – 1.1 0.76 – 0.90

5 -- 1.55 – 1.75

6 -- 10.40 – 12.5

7 -- 2.08 – 2.35

The procedure is as follows:

(1) Use image algebra to calculate either the MSS or TM ratio

images using the image scene in order to represent the entire

reflectance range of landscape features.

(2) Cartesian plotting of either MSS band ratio 1/3 or TM band

ratio 2/4 on the x-axis versus MSS band ratio 2/3 or TM

band ratio 3/4 on the y-axis (Figure 4.12).

83

(3) Determine the upper and lower bounding line.

(4) Determine the regions in the scatter plot that correspond to

the areas of severe erosion, moderate erosion, no erosion

(stable areas), minor deposition and extensive deposition.

Also determine the corresponding pixels in the image (i.e.

pixels characterized by severe erosion, moderate erosion, no

erosion (stable areas), minor deposition, extensive

deposition.

If the imagery we are assessing is a Landsat TM scene, we can

directly substitute similar TM bands for MSS bands (Crist and Cicone 1984).

Thus the band ratio 2/4 can be substituted for band ratio 1/3 and band

ratio 3/4 for 2/3.

The result of generation of the SSI for the years 1973 and 1992 for

the Nilgiris is shown in Figure 4.13. Figure 4.13 I(a) and II(a) are the two

input multi-spectral images for 1973 (MSS) and 1972 (TM) respectively.

Figure 4.13 I(b) and II(b) are the scatter plot of MSS 1/3 Vs TM 2/4 for the

year 1973 and 1992 respectively. Figure 4.13 I(c) and II(c) depict the images

showing regions of erosion, stable regions and region of deposition in the

Nilgiris. These images are similar to classified images, wherein the training

class. Pixels have been selected from the scatter plots. That is, pixels

representing classes of severe erosion are selected from the data space near

the upper line in the scatter plot, while pixels representing classes of Non-

erosion (i.e., stable classes) are selected from the data space near the central

diagonal line in the scatter plot. Similarly, the pixels representing classes of

deposition are selected from the data space near the bottom line in the scatter

plot.

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4.10.6 Description of the SSI Images

Pixels in yellow colour are those representing fluvial, including

stream terraces, channels, reservoirs, and flood plains. These landforms are all

depositional features. Agriculture and forestlands are also represented as

depositional areas.

Pixels in blue indicate areas in stable to transitional state, a

category which dominates the landscape. Green pixels represent areas that are

moderately exposed and hence, the erosion is moderate in this region. Red

colored pixels represent areas that are exposed and can be considered

erosional features. The 1992 SSI image is dominated by these Red colored

pixels.

I (a) I (b) I (c)

II (a) II (b) II (c)

Figure 4.13 SSI Image for Nilgiris district I) 1973 II) 1992

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It has been shown by Pickup & Nelson (1984) that a radiance

measure based on the 1/3-2/3 MSS data space may be used to categorize

eroding, stable, and depositional surfaces in the arid lands of central Australia.

The measure does depend on having a uniform level of greenness in the area

under study.

4.10.7 Conclusion

To sum up, the soil stability index computed using satellite image

data allows rapid survey of the erosion status of large areas commensurate

with the size of management units. The areas under erosion are more in 1992

compared to 1973 and this compares with the results of the degradation study

carried out using GIS analysis.