CHAPTER - 3. SOIL EROSION ASSESSMENT AND IDENTIFICATION OF ...shodhganga.inflibnet.ac.in/bitstream/10603/25954/13/13_chapter 3.pdf · SOIL EROSION ASSESSMENT AND IDENTIFICATION OF

Geospatial characterization and conservation potential for ABR

68 Subin K Jose (2012)

CHAPTER - 3.

SOIL EROSION ASSESSMENT AND

IDENTIFICATION OF EROSION

PRONE AREAS

3.1. Introduction

Soil erosion is the process of detachment, transportation and deposition of soil particles

from land surface .Agencies or the energy sources involved in the process of soil erosion are

mainly water, wind, sea waves, human beings and animals (Judson, 1965; Merritt et al.,

2003). Soil erosion as "soil cancer" is a complex process and its multiple obvious and hidden

social and environmental impacts are an increasing threat for the human existence

(Ownegh, 2003). Soil is naturally removed by the action of water or wind and is called

background soil erosion. Natural soil erosion has been occurring since the early period of

earth. But accelerated soil erosion is relatively a recent problem. It is always the result of

mankind's unwise actions which leave the land vulnerable during times of erosive rainfall

or windstorms.



Soil erosion is one of the most serious environmental problems in the world today and with

decreased soil fertility causes the destruction of our natural ecosystems like pastures,

forests and agricultural ecosystems (Bayramin et al., 2003). Soil erosion is a wide spread

problem in both developing and developed countries. The problem has far reaching

economic, political, social and environmental implication due to both on site and off site

damages (Thampapillai and Anderson, 1994; Grepperud, 1995; Pandey et al., 2007). In an

overview of global erosion and sedimentation, Pimental et al., (1995) stated that more than

50% of the world's forestland and about 80% of agricultural land suffer from significant

erosion.

In India, about 53% of the total land area is prone to erosion and has been estimated that

about 5,334 metric tons of soil is being detached annually due to various reasons (Narayana

and Babu, 1983). Unprecedented increasein soil loss and its economic and environmental

impacts have made erosion one of the most serious global problems of the day (Bewket and

Teferi, 2009; Wang et al., 2009; Zhang et al., 2009). Soil erosion is one of the most widespread

forms of land degradation resulting from such changes in land use. The soil erosion process

affects 11.4% of the national territory and has significant consequences for the forest

ecosystem (Maass et al., 1988). Soil erosion responds both to the total amount of rainfall and

to differences in rainfall intensity, however, the dominant variable appears to be rainfall

intensity and energy rather than rainfall amount alone. Every 1% increase in total rainfall,

erosion rate would increase only by 0.85% if there were no correspondent increase in

rainfall intensity. However if both rainfall amount and intensity were to change together in

a statistically representative manner predicted erosion rate increased by 1.7% for every 1%

increase in total rainfall (Pruski and Nearing, 2002).

Soil erosion is broadly of two categories i) the natural erosion or the geologic erosion or the

normal erosion; ii) accelerated erosion. Soil erosion begins with detachment, which is

caused by breakdown of aggregates by raindrop impact, sheering or drag force of water

and wind. Detached particles are transported by flowing water or wind and deposited

when the velocity of water or wind decreases by the effect of slope or ground cover (Ismail

et al., 2008). There are different types of soil erosion like rainwater erosion (splash erosion,

sheet erosion, hill erosion and gully erosion), Landslide erosion (Earthquakes, heavy



rainfall), stream bank erosion (torrential rains in hilly areas causes flooding of rivers and

streams causing large scale erosion throughout the stream banks), seashore erosion (due to

turbulent waves in the sea during monsoons), wind erosion (common in low rainfall areas,

mainly due to strong winds) (Onyando et al., 2005).

The main problems caused by soil erosion include risk to food security, decline in esthetic

landscape beauty, increase in the probability of flood in flood plains, reduced quality of

water, and loss of aquatic biodiversity in rivers and lakes by pollution, eutrophication,

decrease in soil fertility and productivity, transformation of land into fallow land not

suitable for reforestation, irreversible reduction in arable soil, increase in flooding events,

diffuse pollution of river networks and turbidity (Sthiannopkao et al., 2007; Zhou et al.,

2008; Bewket and Teferi ,2009; Wang et al., 2009). The factors that influence the rate of soil

erosion include rainfall, runoff, slope, land cover and the presence or absence of

conservation strategies (Solanki and Singh, 1996). Climatic characteristics of the region such

as having a long dry period followed by heavy erosive rainfall along with prolonged

human intervention have made the region very susceptible to soil erosion (Kouli et al.,

2009). Soil erosion is influenced by the spatial heterogeneity in topography, vegetation, soil

properties and land use, among other factors (Morgan, 1998; Le Rouxa et al., 2007; Jain and

Das, 2010).

Soil erosion is more prevalent in the Western Ghats. Mountain sides of Kerala are facing

severe soil erosion problem. High intensity rainfall and steepness of slope have contributed

in general to the higher soil loss in certain pockets of the state (Jose et al., 2011). Studies

showed that the major portion of Kerala (51.98%) falls in 0-5 tones ha/ 1 year / 1 soil loss

categories and less than 5% of the area is subjected to severe form of soil erosion (Jose et

al., 2011). Soil erosion can be divided into as potential erosion and actual erosion. Potential

erosion gives an indication of the likelihood and possible intensity of erosion that could

occur under given physical and climatic conditions in an area. Actual erosion gives the

existing forms and intensity of erosion in an area under the prevailing physical factors and

climatic conditions. Erosion can also be characterized by the rates of the erosion processes,

and the various factors influencing them in time and space (Angima et al., 2003).



Sustainable and effective management strategies are required to assess erosion at local,

regional and national scales under different types of activities. Different countries use

different methods leading to the design of numerous risk assessment methodologies across

different countries (Saha and Pande 1993; Morgani et al., 1998; Al-Quraishi, 2003; Hoyos

,2005; Lim et al., 2005; Xu et al., 2005; Yuksel et al., 2008; Yue-qing et al., 2009). Erosion

models used at regional scales include USLE/RUSLE, WEPP, SEMMED, ANSWERS,

LISEM, EUROSEM, SWAT, SWRRB, AGNPS, etc., each with its own characteristics and

application scopes (Boggs et al., 2001; Lee, 2004; Lu et al., 2004; Lim et al., 2005; Bhattarai and

Dutta, 2007; Dabral et al., 2008; Ismail and Ravichandran, 2008; Tian et al., 2009). The

dominant model applied worldwide to soil loss prediction is USLE/RUSLE. Wischmeier

and Smith (1965, 1978) by collecting soil erosion data of 8,000 communities of 36 regions in

21 states in USA, analyzed and assessed various dominating factors of soil erosion, and

introduced the Universal Soil Loss Equation (USLE) to assess soil erosion by water.

Basically, USLE predicts the long-term average annual rate of erosion on a field slope based

on rainfall pattern, soil type, topography, crop system, and management practices (soil

erosion factors). By including additional data and incorporating recent research results, the

USLE methodology is improved and a revised version of this model (RUSLE) further

enhanced its capability to predict water erosion by integrating new information made

available through research of the past 40 years (Renard et al., 1997; Yoder and Lown, 1995).

Although USLE is an empirical model, the combined use of remote sensing and

Geographical Information System (GIS) techniques makes soil erosion estimation and its

spatial distribution feasible within reasonable costs and better accuracy in larger areas

(Millward and Mersey, 1999; Lin et al., 2002; Wang et al., 2003; Lu et al., 2004; Jasrotia and

Singh, 2006; Krishna Bahadur, 2009; Chou, 2010). Current developments in GIS make it

possible to model complex spatial information. The combined use of GIS and erosion

models, such as USLE/RUSLE, has been proved to be an effective approach for estimating

the magnitude and spatial distribution of erosion (Cox and Madramootoo, 1998; Erdogan et

al., 2007; Fernandez et al., 2003; Fu et al., 2006).

In the late 1950s, the Universal Soil Loss Eiquation (USLE) was developed by W.H.

Wischmeier, D.D. Smith, and their associates from the U.S. Department of Agriculture



(USDA), Agricultural Research Service (ARS),Soil Conservation Service (SCS) and Purdue

University. Its field use began in the Midwest in the 1960s. In 1965 Agriculture Handbook

282 was published, which served as the main reference manual for USLE until it was

revised in 1978 as Agriculture Handbook 537 (Deore, 2005).

Although the USLE is a powerful tool that is widely used by soil conservationists in the

United States and many other countries, research and experience gained in this field since

1970s have provided insights to develop improved technology that has led to the designing

of modified USLE (Wischmeier and Smith, 1978) and revised USLE (Renard et al., 1991).

The update is based on an extensive review of the USLE and its database, analysis or data

not previously included in the USLE, and theory describing fundamental hydrological and

erosion processes. This update of the USLE is so substantial that the result is referred to as

RUSLE. RUSLE is an attempt to improve the capability of USLE in using dynamic

hydrological and erosional processes and the flexibility of USLE in adjusting process

parameters to account for spatial and temporal changes.

The modified Universal Soil Loss Eiquation follows the structure of the USLE, with the

exception that the rainfall factor is replaced with the runoff factor. The equation calculates

sediment yield for a storm within a watershed that does not exceed 5 square miles. It also

includes numerous improvements, such as monthly factors, incorporation of the influence

of profile convexity / concavity using segmentation of irregular slopes and improved

empirical equations for the computation of LS factor (Foster and Wischmeier, 1974; Renard

et al., 1991). Such limitations are not at all an indication of the overall performance of the

USLE.

As an empirical equation derived from experimental data, the USLE adequately represents

the first-order effects of the factors that influence sheet and rill erosion. In Asia, several soil

erosion studies have been conducted using USLE approach including the soil erosion and

risk maps for highlands (Jusoff and Chew, 1998; Mongkolsawat, 1994; Samad and Patah,

1997). The present study envisages the application of USLE method along with remote

sensing and GIS techniques in the assessment and quantification of the soil loss in the

Neyyar, Peppara and Shendurney wildlife sanctuary. The present study reveals that

Universal Soil Loss Equation along with Geographic Information System and remote



sensing is very powerful tool for quantifying the soil erosion and useful for preparing

sustainable soil erosion management strategies. The study prepares the soil erosion prone

area map and also quantifies the annual soil erosion map of the study area and its extent in

detail.

3.2. Materials and Methods

In the present study qualitative raster analysis was carried out using different factors

influencing soil erosion for the precise identification of erosion proneness area and

quantification of erosion. For the study data utilized include survey of India topographic

maps in 1:25000 and 1:50000 scale, Indian remote sensing satellite data IRS LISS –III image

with a resolution of 23.5 meter, daily rainfall data for the last 30 years from Indian

meteorological department and forest department (1980- 2010), Soil data regarding soil

type, texture, soil depth etc and also field level data using GPS. Land cover map of the area

is prepared by using supervised classification techniques of satellite image and the accuracy

of the classified image is ground checked and verified. Digital elevation model, slope and

aspect were generated from the vectorised contour by using spatial analyst extension in Arc

GIS software.

Among the traditional approaches, the USLE - the Arithmetic Multiplicative System is used

for the quantitative assessment of soil erosion. Among the emerging methodologies,

process involving raster overlay analysis called Multi-Criteria Analysis - the Experts

Systems Model have been used for the assessment of erosion .The approaches were

adopted for evaluation of soil erosion proneness and quantification are Universal Soil Loss

Equation (USLE) and Multi-Criteria Analysis(MCA).

3.2.1. Universal Soil Loss Equation

The USLE developed by Wischmeier and Smith (1978) is used in this study for estimation of

soil loss. Soil loss quantification for the entire sanctuary is calculated by generating various

input factors of USLE in GIS environment. Methodology followed to compute each

contributing factor is as follows:

A = R.K.LS.C.P



Where A is computed soil loss (tons/hectare/year), R is the rainfall-runoff erosivity factor, K

is the soil erodibility factor. L is the slope length factor, S is the slope steepness factor. C is

the cover-management factor, and P is the supporting practices factor. Factor ‘R’ in the

USLE is an erosion index which is the product of total energy (F) of storm and the

maximum intensity of 30 minutes rainfall event (I-30). Computation of erosion index

invariably requires the data of self recording rain gauges. Because of the non-availability of

these data and cumbersome work involved in the analysis of this data, some alternatives

are necessary. In this work daily rainfall data of 30 years (1980 - 2010) were analyzed for

computing minimum erosion index (EI min) using the procedure suggested by Richardson

et al., (1983). This model is useful for quick computation of erosion index from daily rainfall

data for any particular month or season for any river basin. The minimum EI that can result

from a daily rainfall event of amount ‘P’ would occur during a rainfall of uniform intensity

for the full 24hr period as suggested by Richardson et al., (1983).

EI min = P2 (0.00364 log10P - 0.00062)

Where ‘P’ is the daily rainfall in mm. The ‘K’ factor in the USLE is a quantitative description

of the inherent credibility of a particular soil. It indicates erosion index or the erosion from a

standard plot for a particular soil. The soil erodibility monograph can be used to obtain the

soil erodibility factor ‘K’ for soils, for which the ‘K’ factor have not previously been

determined. It is particularly helpful for areas where the ‘K’ factor for subsoil is not known.

As silt content increases, the ‘K ‘factor of soil increases, and thereby the erodibility

(Morgan, 1979). The topographic factors i.e., slope gradient and length of slope significantly

influences soil erosion by surface water movement. The slope of the study area was derived

from the Digital Elevation Model (DEM) based on the toposheet derived contour data.

Slope length in meters (L) is calculated from the slope steepness in percentage (S) following

the relation (Desmet and Covers, 1996) developed for use in GIS for topographical

conditions.

L= 158-2.92 X S



The L.S factor is calculated using the empirical equation (Wischmeier and Smith, 1978) from

slope length and slope percent using Saga 1.2 software.

LS = (L/22.13) m (0.065 + 0.045S-40.0065S2)

Where m is an exponent varying between 0.2 to 0.6 depending on the percent slope. IRS

LISS-III image was used to interpret the land cover of the study are using ERDAS 9.1

software, the subset image was visually interpreted for different land cover classes and also

carried out supervised classification using maximum likelihood algorithm. Unsupervised

classification were also practiced to cross-check the results for better precision. Integration

of all the above classifications and ground truth verification helped to generate the land

use/land cover map of the study area. This layer was converted to ‘C’ layer through

reclassification of each land cover type by giving appropriate ‘C’ value. The ‘C’ factor

values for natural vegetation are followed from USLE lookup table for different cover

types.

‘P’ factor map was prepared from land use/land cover map. For all vegetation types, no

erosion controls were found and are assigned the value 0.8. The value assigned was based

on the research findings of Central Soil and Water Conservation Research and Training

Institute, Dehradun (Rinos et al., 2001) and the studies conducted by Omakupt, (1989),

Mongkolsawat et al., (1994). The computed soil loss (tons/hectare/year) was the calculated

by integrating different factors such as the rainfall-runoff erosivity factor, the soil

erodibility factor, the slope length factor, the slope steepness factor, the cover-management

factor and the supporting practices factor using raster calculator option available in Arc GIS

software. The methodology flowchart for the estimation of USLE was given in the Fig.3.1.

In Neyyar wildlife sanctuary spatial distribution factors such as ‘C’ factor (Fig.3.2),Spatial

distribution of ‘R’ factor (Fig.3.3), spatial distribution of ‘K’ factor (Fig.3.4) and spatial

distribution of ‘LS’ factor ( Fig. 3.5) were used for analysis. In Peppara wildlife sanctuary

(Fig.3.6, Fig.3.7, Fig.3.8 Fig. 3.9) and Shendurney wildlife sanctuary (Fig.3.10,

Fig.3.11,Fig.3.12 Fig. 3.13) the above mentioned factors were used for analysis



Fig.3.1 Flow chart for the estimation of soil erosion using USLE method.

SATELLITE

IMAGE

LANDCOVER

MAP

C – FACTOR

PREPARATION

RAINFALL DATA

GEOSTATISTICAL

ANALYSIS

RAINFALL MAP

R- FACTOR

PREPARATION

SOIL MAP

PREPARATIO

N OF K -

FACTOR

TOPOSHEET

DIGITAL ELEVATION

MODEL

CALCULATION OF LS -

FACTOR

C – FACTOR MAP

R- FACTOR MAP

K – FACTOR

MAP

LS – FACTOR MAP

RASTER CALCULATION

SOIL EROSION

QUANTIFICATION MAP



Fig.3.2 Spatial distribution map of C- factor (Neyyar wildlife sanctuary).

Fig.3.3 Spatial distribution map of R- factor (Neyyar wildlife sanctuary).



Fig.3.4 Spatial distribution map of K- factor (Neyyar wildlife sanctuary).

Fig.3.5 Spatial distribution map of LS- factor (Neyyar wildlife sanctuary).



Fig.3.6 Spatial distribution map of K- factor (Peppara wildlife sanctuary).

Fig.3.7 Spatial distribution map of C- factor (Peppara wildlife sanctuary).



Fig.3.8 Spatial distribution map of R- factor (Peppara wildlife sanctuary).

Fig.3.9 Spatial distribution map of LS- factor (Peppara wildlife sanctuary).



Fig.3.10 Spatial distribution map of K- factor (Shendurney wildlife sanctuary).

Fig.3.11 Spatial distribution map of C- factor (Shendurney wildlife sanctuary).



Fig.3.12 Spatial distribution map of R- factor (Shendurney wildlife sanctuary).

Fig.3.13 Spatial distribution map of LS- factor (Shendurney wildlife sanctuary).



3.2.2. Multi-Criteria analysis

The second approach is the Multi-Criteria Analysis using Analytical Hierarchy Process

(AMP) applied for the prioritization of erosion proneness areas. The criteria were

topographic (slope, aspect, elevation), morphometric (drainage density, gullies), climatic

(rainfall), pedological (soil thickness) and biographic (land use/land cover). All the thematic

layers converted into raster form and each theme is reclassified in to 10 equal classes in a

scale of 0 to 10 based on their importance to soil erosion. Weightage is given in the

percentage scale according to their importance in erosion proneness. Layers thus obtained

were then multiplied by the respective weightage and then added by linear combination

using Boolean logic .Table.3.1 illustrates the weightage given to each layer for the analysis.

The final output of Composite Erosion Index (CEI) map was generated and it was classified

into the categories of erosion intensity by using the mean and standard deviation. Different

thematic layers developed for multi - criteria analysis are as follows:

3.2.2.1. Aspect

Aspect is the direction at which mountain faces. N aspect means the side of the mountain

facing North .South aspect means the side of the mountain facing south. Aspect is one of

the factors, which still scarcely considered in soil erosion modeling, despite its

acknowledge importance (Torri, 1996). All the factors affecting soil moisture content may

influence erosion. Direct radiation received by given slope depends on slope aspect (e.g., N

and S oriented slopes), which indirectly influence the soil moisture content. Aspect is the

directional measure of slope. One common method is to classify aspect into eight

principal directions ( N , NE , E , SE , S , SW , W and NW ) and to treat aspects as categorical

data. Difference in incident solar radiation in mountains, depends on aspect and slope

angle. It directly or indirectly has effect on soil erosion. Fig, 3.14, Fig. 3.15, Fig.3.16

illustrates the aspect map of the study area.



Fig.3.14 Aspect map of Neyyar wildlife sanctuary

Fig.3.15 Aspect map of Peppara wildlife sanctuary.



Fig.3.16 Aspect map of Shendurney wildlife sanctuary.

3.2.2.2. Drainage density

Drainage density ( Dd ) ( km / km2 ) as defined by Horton ( 1932 ) is the total length of

streams in km ( L ) within a basin divided by the drainage area in km2 ( A ). Length of

streams, area and perimeter for the entire study area were obtained using ArcGIS 9.3. The

range of drainage density varying from 0.01 to 53 km2 .The drainage density map of the

study area is shown in the Fig.3.17, Fig. 3.18, Fig.3.19.Mathematically drainage density is

expressed as ,Drainage Density (Dd) = Stream length / Basin Area.

Fig.3.17 Drainage density

map of Neyyar wildlife

sanctuary.



Fig.3.18 Drainage density map of Peppara wildlife sanctuary.

Fig.3.19 Drainage density map of Shendurney wildlife sanctuary.



3.2.2.3. DEM (Digital Elevation Model)

Elevation data are derived from Digital Elevation Model (DEM) based on the contour

developed from SOI toposheets. The study area is found to be characterized with an

elevation ranging from the 100-1740m above from Mean Sea Level .The eastern and north

eastern part of the sanctuary are found to be highly elevated compared to other regions.

The DEM maps are shown in the Fig.3.20, Fig.3.21, and Fig.3.22.

Fig.3.20 DEM map of

Neyyar wildlife sanctuary

Fig.3.21 DEM map of

Peppara wildlife sanctuary



Fig.3.22 DEM map of Shendurney wildlife sanctuary.

3.2.2.4. Gullies

A gully is a land form created by running water eroding sharply into soil, typically on a

hillside. Gully erosion is the most spectacular and prevalent type of erosion as the damage

caused by it is relatively permanent. It dissects the fields, impedes the tillage operations,

damages forest, agricultural, and recreational land and causes environmental pollution. The

damage caused by the gullies is significant compared to other forms of erosion as the

sedimentation production from the gullies is to the tune of 147% of that from other types of

erosion (Grissinger and Murphy, 1989). In the present study, the first and second order

streams alienated from the drainage network were used for developing the gully map layer.

As the first and second order streams are more susceptible for gully formation, a 10 and

20m buffering were carried out respectively. The map prepared for the gully formation was

shown in the Fig. 3.23, Fig. 3.24, Fig. 3.25.



Fig.3.23 Gullies map of Neyyar wildlife sanctuary.

Fig.3.24 Gullies map of Peppara wildlife sanctuary.



Fig.3.25 Gullies map of Shendurney wildlife sanctuary.

3.2.2.5. Land use/Land cover

One of the important thematic maps required for any sort of terrain evaluation studies is

the land use/land cover map. The land use/land cover classes have been selected and

depicted in the map (Fig.2.5, Fig.2.13, and Fig.2.21) to represent different categories of land

utilization.

3.2.2.6. Rainfall

Among the various parameters that affect erosion of soil, precipitation plays a vital role.

More the amount of rainfall, more is the amount of soil detached from the earth surface and

carried away by the runoff. Erosivity has been defined as the potential ability of rain to

cause erosion. It is a function of the physical characteristics of rainfall (Hudson, 1995).

Rainfall erosion - the interaction between rain and soil have been responsible for creating

gullies and rendering millions of hectares of productive land into unproductive wastelands.

Fig.3.26,Fig. 3.27,fig 3.28.shows the distribution of average annual rainfall in the study area

and it was found that the rainfall varied from a minimum of 200 cm adjacent to reservoir to

a maximum of 370 cm in the highlands. For the spatial distribution of rainfall geostatistical

analyst in Arc GIS is used.



Fig.3.26 Spatial distribution of average rainfall map of Neyyar wildlife sanctuary.

Fig.3.27 Spatial distribution of average rainfall map of Peppara wildlife sanctuary.



Fig. 3.28 Spatial distribution of average rainfall map of Shendurney wildlife sanctuary.

3.2.2.7. Slope

Erosion increases along slopes because of the accumulation of runoff along the slope.

Erosion is also related to the steepness of the uniform slope. As slope steepness increases,

the increase in erosion is linear with the increase in steepness. More of the slope steepness

effect comes from erosion by surface runoff than by raindrop impact because deepness has

a greater effect on erosion by flow than by raindrop impact (Foster, 1982). Thus, erosion at a

location on a slope is a function of the distance from the surface runoff origin and the

steepness at that location (Foster et al., 1977). If the location is far down the slope where

much runoff has accumulated, the erosion rate will be high. For a given location, erosion

will be proportional to the steepness at that location (Toy et al., 2002) .Soil erosion has direct

link with soil slope. The slope map of the study area is shown in the Fig.3.29, Fig.3.30, Fig.

3.31 respectively.



Fig.3.29 Slope map of Neyyar wildlife sanctuary.

Fig.3.30 Slope map of Peppara wildlife sanctuary.



Fig. 3.31 Slope map of Shendurney wildlife sanctuary.

3.2.2.8. Soil depth/ thickness

On erosional topography, the depth of the soil is inversely related to the long-term rate of

erosion that has acted on it. Soil thickness has an indirect relationship with soil loss, as there

is a direct relationship between soil depth and plant growth. Shallow soils with marginal

rooting depth are more vulnerable for erosion and landslides. Within a river basin soil

thickness can vary as a function of many different and sometimes interplaying parameters

among which we can count, vegetation cover, underlying lithology, climate, gradient, hill

slope curvature, upslope contributing area and land use. In the present study, soil thickness

was considered as a major factor to ascertain the erosion proneness area. Each sub-class was

ranked according to their influence in soil erosion .Fig. 3.32, Fig. 3.33, Fig.3.34. shows the

soil depth of the study area. The detailed methodology flow chart is shown in the Fig.3.35.



Fig. 3.32 Soil thickness map of Neyyar wildlife sanctuary .

Fig. 3.33 Soil thickness map of Peppara wildlife sanctuary.



Fig. 3.34 Soil thickness map of Shendurney wildlife sanctuary.

Thematic layers Weightage in %

Aspect 7.5

Drainage Density 10

Elevation 12.5

Gullies 10

Land cover 20

Rainfall 20

Slope 15

Soil thickness 5

Table 3.1 weightage given to each thematic layers in soil erosion prone area identification.



Fig.3.35 flow chart of soil erosion prone area identification

3.3. Results

The present study was aimed at estimating the current soil loss and identifying the

susceptible erosion proneness area in the study area. Two approaches were applied in the

study in order to identify the soil erosion prone area and quantifying the soil erosion. The

soil erosion map resulting from the spatial overlay of USLE factors in the Neyyar Wildlife

Sanctuary is shown in the Fig.3.36. The soil erosion map resulting from the spatial overlay

of USLE factors in the Peppara Wildlife Sanctuary is shown in the Fig.3.37 .The soil erosion

map resulting from the spatial overlay of USLE factors in the Shendurney Wildlife

Sanctuary is detailed in the Fig.3.38. The study provides an overall insight into causes of

soil erosion resulting from interaction of the USLE factors spatially and quantitatively.

DIGITAL

ELEVATION

DISTANCE

ANALYSIS

GEOSTATISTICA

L ANALYSIS ASPECT SLOPE

MULTICRITERIA ANALYSIS

SOIL EROSION PRONE AREA MAP

SATELLITE

IMAGE

TOPOSHEETS

LANDCOVER

MAP

DRAINAGE

CONTOUR

MATERIOLOGICAL

DATA

RAINFALL DRAINAGE

DENSITY

GULLIES

SOIL DEPTH



Based on standard deviation, the USLE map is reclassified into four different class viz. low,

moderate, high and severe soil erosion areas. Table 3.2, 3.3, 3.4 presents corresponding

quantitative soil loss, in addition to the spatial information.

Fig. 3.36 Annual soil erosion map of Neyyar wildlife sanctuary.

Quantitative soil loss Neyyar wildlife sanctuary

Erosion class Rate of soil

erosion(tons/ha/Yrs

Area (km2) Percentage

Low 0 - 10 63.5 49.60 %

Moderate 10 - 30 34.1 26.64%

High 30 - 60 17.7 13.82%

Severe 60 – 209.086 12.7 9.94%

Table 3.2 Quantitative soil loss of Neyyar wildlife sanctuary.



In Neyyar wildlife sanctuary low soil erosion area constitutes 57.8% of total sanctuary area,

followed by moderate (22.5%), high (14.3%) and severe (5.4%) soil erosion class.

Fig. 3.37 Annual soil erosion map of Peppara wildlife sanctuary.

Quantitative soil loss Peppara wildlife sanctuary


erosion(tons/ha/Yrs


Low 0 - 10 26.1 49.24

Moderate 10 - 30 12.7 23.96

High 30 - 60 10.4 19.62

Severe 60 – 270.776 3.8 7.18

Table 3.3 Quantitative soil loss Peppara wildlife sanctuary



In Peppara wildlife sanctuary low soil erosion potential area covers 49.7% followed by

moderate (29.5%), high (9.6%) and severe (11.2%) soil erosion areas.

Fig.3.38 Annual soil erosion map of Shendurney wildlife sanctuary

Quantitative soil loss Shendurney wildlife sanctuary


erosion(tons/ha/Yrs


Low 0 - 10 92.3 53.97

Moderate 10 - 30 43.5 25.43

High 30 - 60 24.7 14.44

Severe 60 – 199.882 10.5 6.16

Table 3.4 Quantitative soil loss Shendurney wildlife sanctuary.

In Shendurney wildlife sanctuary low soil erosion potential area is 53.97% followed by

moderate (25.43%), high (14.44%) and severe ( 6.16%) areas.



In multicriteria analysis layers of weighted aspect, drainage density, elevation, gullies. land

use/land cover, rainfall, slope and soil thickness were generated and used in multi-criteria

analysis. Integration of the above criteria was carried out and the final output map indicates

composite erosion index that relates to the erosion proneness of the unit area under the

relative contribution of the given criteria. Based on the results, the study area was grouped

into four zones such as low, moderate, high and severe. Fig.3.39 illustrates the erosion

proneness areas in Neyyar wildlife sanctuary. Table 3.5 presents the area wise distribution

of the vulnerable erosion proneness regions. Fig.3.40 illustrates the erosion proneness areas

the Peppara wildlife sanctuary. Table 3.6 depicts the area wise distribution of the

vulnerable erosion proneness regions. Fig.3.41 shows the erosion proneness areas of the

Shendurney wildlife sanctuary. Table 3.7 presents the area wise distribution of the

vulnerable erosion proneness regions.

Fig.3.39 Soil erosion proneness map Neyyar wildlife sanctuary



Erosion proneness area – Neyyar wildlife sanctuary

Erosion class Area (Km2) Percentage

Low 52.5 41.01

Moderate 53.9 42.12

High 17.6 13.75

Severe 4 3.12

Table 3.5 Erosion proneness area – Neyyar wildlife sanctuary

In Neyyar wildlife sanctuary 41.01 % of area comes under low soil erosion proneness

followed by moderate (42.12%), high proneness area under (13.75%) and severe (3.12 %)

erosion prone areas.

Fig.3.40 Soil erosion proneness map Peppara wildlife sanctuary



Erosion proneness area – Peppara wildlife sanctuary


Low 19.1 36.10

Moderate 21.7 40.88

High 9.4 17.73

Severe 2.8 5.28

Table 3.6 Erosion proneness area –Peppara wildlife sanctuary.

In Peppara wildlife sanctuary 36.10 % of area is under low soil erosion proneness followed

by moderate (40.88%), high proneness area (17.73%) and only 5.28 % is under severe

erosion proneness.

Fig.3.41 Soil erosion proneness areas map Shendurney wildlife sanctuary.



Erosion proneness area – Shendurney wildlife sanctuary


Low 52.3 30.58

Moderate 87.8 51.44

High 23.1 13.52

Severe 7.8 4.56

Table 3.7 Erosion proneness area –Shendurney wildlife sanctuary.

In Shendurney wildlife sanctuary 30.58 % of the area is under low soil erosion proneness,

followed by moderate (51.44%), high (13.52%) and only 4.56 % of total sanctuary area is

under severe erosion proneness.

3.4. Discussion

Soil erosion is one of the main causes of soil fertility decline, sedimentation in canals and

rivers, decrease in the storage capacity of the dams, increase of flood frequency,

environmental pollution, all affecting sustainable development.

The present study reveals that in low soil erosion prone areas with low slope, high soil

thickness (> 1.99m) and low drainage density, soil erosion is low despite the land use type.

Low rainfall may also be a contributing factor for low erosion. Comparatively low slope

gradient may be the major factor lessening the vulnerability in this area. From the study, it

was found that the land use/land cover has some effect on the erosion proneness in the

study area.

In moderate soil erosion prone area the major factors governing the soil erosion may be the

slope, drainage density and the soil thickness. Land use/land cover in this area is mainly of

Southern moist mixed deciduous forest, West coast semi evergreen forest, West coast

tropical evergreen forest and grasslands. Even though the area experiences heavy rainfall

and moderate to high slope of (15 -45 degree), thick vegetation of West coast semi

evergreen forest, West coast tropical evergreen forest in this region check the erosion

prominently. Deep rooted plants and naturally structured contours of the West coast

evergreen and semi- evergreen forests prevents the direct impact of rainfall erosivity in this



area. Further, the buttressed roots, stilt roots and stilted peg roots of the trees and shrubs

found in this area effectively reduces the effect of slope.

Highly erosive prone areas are mainly concentrated in higher altitudes of the study area.

This area experiences high drainage density, low soil thickness and high slope, which may

be the contributing factor for the high erosion proneness. Besides, most of this area

experiences pockets of grasslands. Annual fire incidence in this region may probably

become a factor to accelerate high soil erosion.

Severe soil erosion proneness area encountered poor ground cover, high rainfall, low soil

thickness and high slope. This might be the reason for high erosion occurred in this region.

Based on the USLE model, this area shows severe erosion rate. Trek paths and annual forest

fires may increase the susceptibility of this area for high erosion. High drainage density in

this area may also be a prime factor for erosion. Erosion-control activities are most effective

during low stress conditions and become relatively less effective as the magnitude of stress

increases. The natural condition often represents the minimum erosion rate, and attempts

to further reduce erosion are generally ineffective. But there are exceptions. During low

stress, effective erosion control activities can reduce or even eliminate erosion. For example,

control structures in stream channels can reduce sediment transport to below the natural

rate until their storage capacity is exceeded; hillside buttresses and check dams on recent

natural landslides can reduce slope movement and surface erosion. Usually, however,

erosion-control activities are used to bring an accelerated erosion rate down near the

natural rate.

The present study shows that in Neyyar wildlife sanctuary erosion range is 0 - 209.08 tons

ha/yr compared to 1 -201 tons ha/yr reported by Suersh et al. (2000) in Neyyar wildlife

sanctuary. It is also found that the erosion rate in Peppara wildlife sanctuary is 0- 199.882

tons ha/yr and in Shendurney wildlife sanctuary 0-270.776 tons/ha/yr .The study infers that

inclinations of 10 to 30° will be affected by gullies, rills and mass movements. Morgan

(1994) also described that areas with lower inclination are less affected by gully erosion and

mass movement. Areas with inclinations of more than 30° are less affected in the study area

due to the fact that these sites still are covered with forest vegetation. Soil erosion decreases

with increasing vegetation cover (Wischmeier and Smith, 1978; Morgan, 1994).



Deforestation in this area brought about changes in the water balance and consequently a

lowering of the water table. A lowered groundwater table leads to increased groundwater

velocity and, therefore, it eats away and erodes the soil cover ( Li and Wang ,1990) The

recurrence of wildfires causes physical changes in soils which enhance their susceptibility

to erosion (Hibbert et al., 2009; Coehlo et al., 1990; Batjes, 2008).The occurrence of forest fire

will increase the soil erosion proneness.

There is also ample evidence for higher soil erosion at several sites with high human

populations and intensive land uses (Beach et al., 2002). High intensity rainfall accelerate

the soil erosion of an area where the soil texture is sandy, and contains water repellent

material eroded from hillslopes (Shakesby et al., 2002). The major forest disturbance in an

area accelerate the rate of soil erosion in an area( De-Bano et al., 2005) . Fuchs (2007) proved

that there is a significant correlation between sedimentation rates and settlement history,

and that soil erosion was triggered by human activity and then amplified by enhanced

precipitation.

Disturbance by land-management activities, such as logging, road construction, and forest

fire, generally increases erosion rates. The location and magnitude of the effects of land

management on erosion rates, however, cannot be predicted accurately. Human activities

have their greatest relative effect on erosion rates during periods of low stress. To control

soil erosion, conservation measures need to be implemented at the field, hillslope or

watershed level (Vrieling, 2006), yet neglect of regional differences can cause resources to

be wasted. Judicious allocation of limited conservation resources and development of

policies and regulations require a process of prioritizing conservation areas (Shrimali et al.,

2001; Mehlman et al., 2004). For soil erosion, vegetation cover, slope, soil and landuse/ land

cover are very important impact factors and are often used in assessment and prioritization

(Le Bissonnais et al., 2001; Şahin and Kurum, 2002; Kheir et al., 2006). The proposed USLE

and soil erosion prone area identification methods contribute to soil erosion research and

facilitates conservation planning in the future, showing good potential for successful

application in other areas for controlling soil erosion.

In general, preventing erosion is more effective than controlling it. Also, the potential for

increasing erosion rates by land-management activities is greater than that for reducing



erosion by using erosion-control techniques. If control activities reduce erosion during low

stress periods, under some conditions, stored or controlled material may be available to be

eroded during high stress periods. For example, when small check dams in steep streams

are effective in controlling sediment transport during small runoff events, sediment

accumulates in the channel. During large runoff events, the material deposited in the

unstable check dams get accumulated, leading to a debris torrent - a much larger and more

destructive erosion event than if that material had been transported during less stressful

events. Providing provision for small, sustained transport of debris during normal events,

even if there is check dams, is believed to lessen the probability of a major debris torrent.

3.5. Suggestions for conservation

The conservation priority levels identified indicate conservation measures to address soil

erosion, and to facilitate the planning of future erosion conservation actions. Erosion

control regions should be identified for future projects based on conservation priorities.

Human activities aggravate soil erosion, which in turn impacts human quality of life.

Higher priorities for conservation are located in the regions with susceptibility to erosion

and human activities, especially in the gentle slopes at the base of hills and in gully regions

which are often cultivated without any conservation measures. To ensure the long-term

maintenance, we must also consider the potential conflict between conservation interests

and human activities (e.g., agriculture) when controlling soil erosion. Sustainable forest

conservation with the participation of tribal people and traditional knowledge will help to

prevent soil erosion. Encroachments and agricultural expansion near to the settlement area

should be avoided for the soil erosion prevention.

3.6. Chapter summary

Soil erosion quantification of the present study reveals that the study area is under the

threat of soil erosion. The multicriteria based soil erosion prone area identification will be

helpful for the future soil erosion control and to evolve soil erosion management strategies

in the study area. The result of the study reveals that soil erosion is accelerated by the

action of forest fire, forest degradation and human interference. The extreme sensitivity of

soil erosion was mainly caused not only the strong rainfall and large topography

differences, but also the intensive human activities. A qualitative assessment method can be



used to prioritize conservation areas without the need for more complex quantitative

methods. Information generated in this study may be useful in future. The GIS based USLE

methodadopted in this study is a significant tool to soil erosion research and facilitates

conservation planning in future, showing good potential for successful application in other

areas for controlling soil erosion. A coordinated forest management strategy is needed for

better management of soil erosion and forest conservation.



3.7. References

Al-Quraishi ,A.M.F. (2003). Soil erosion risk prediction with RS and GIS for the

northwestern part of Hebei Province, China. Pakistan journal of applied science,3(10–

12):659–666.

Angima, S.D., Stott, D.E., O’Neill, M.K., Ong, C.K. and Weesies, G.A. (2003). Soil erosion

prediction using RUSLE for central Kenyan highland conditions. Agriculture

ecosystem and environment, 97(1–3):295–308.

Batjes, N.H.(2008). Mapping soil carbon stocks of Central Africa using SOTER. 2008.

Geoderma, 146, 58–65.

Bayramin, I. O., Dengiz, O., Baskan, and Parlak,M.(2003). Soil erosion assessment with

ICON A model: Case study, Beypazari area, Turkish journal of agriculture and

forestry,27: pp. 105-116.

Beach, T., Luzzadder-Beach, S., Dunning, N., Hageman, J. and Lohse, J. (2002). Upland

agriculture in the Maya Lowlands: ancient Maya soil conservation in northwestern

Belize. Geographical Review ,92, 372– 397.

Bewket, W. and Teferi, E. (2009). Assessment of soil erosion hazard and prioritization for

treatment at the watershed level: case study in the Chemoga watershed, Blue Nile

Basin, Ethiopia. Land degradation and development , 20: 198–211.

Bhattarai, R. and Dutta, D. (2007). Estimation of soil erosion and sediment yield using GIS

at catchment scale. Water resource management, 21(10):1635–1647.

Boggs, G., Devonport, C., Evans, K. and Puig, P. (2001). GIS-based rapid assessment of

erosion risk in a small catchment in the wet/dry tropics of Australia. Land

degradation and development, 12:417–434.

Chou, W.C. (2010). Modelling watershed scale soil loss prediction and sediment yield

estimation. Water resource management, 24(10):2075–2090.

Coehlo, C.O., Shakesby, R.A., Walsh, R.D., Terry, J. and Ferreira, A. (1990). Responses of

surface and sub-surface soil water movement and soil erosion to forest fires in

Eucalyptus globulus and Pinus pinaster forest, Agueda basin. Proceedings of

International Conference on Forest Fire Research, Coimbra.



Cox, C., Madramootoo, C. (1998). Application of Geographic Information Systems in

watershed management planning in St. Lucia. Computation Electronics and

Agriculture, 20:229–250.

Dabral, P.P., Baithuri, N. and Pandey, A. (2008). Soil erosion assessment in a hilly

catchment of North Eastern India using USLE, GIS and remote sensing. Water

resource management ,22:1783–1798.

De-Bano, L.F., Neary, D.G. and Ffolliott, P.E. (2005). Soil physical properties. In: Neary, D.G.,

Ryan, K.C., DeBano, L.F. (Eds.),Wildland Fire in Ecosystems: Effects of Fire on Soil

and Water. USDA, Forest Service General Technical Report RMRS-GTR-Vol. 4, pp.

29–51.

Deore, S.L. (2005). Prioritization of Micro-watersheds of Upper Bhama Basin on the Basis of Soil

Erosion Risk Using Remote Sensing and GIS Technology, Ph.D Thesis. University of

Pune.

Desmet, P.J.J. and Govers, G. (1996). A GIS procedure for automatically calculating the

USLE L.S factor on topographically complex landscapes. Journal of Soil and Water

Conservation, 51(5): 427-433.

Erdogan, E.H., Erpul, G. and Bayramin, I. (2007). Use of USLE/GIS methodology for

predicting soil loss in a semiarid agricultural environment. Environment monitoring

and assessment, 131:153–161.

Fernandez, C., Wu, J.Q., McCool, D.K. and Stockle, C.O. (2003) Estimating water erosion

and sediment yield with GIS, RUSLE,and SEDD. Journal of Soil Water Conservation

,58:128–136.

Foster, G. R., Meyer, L.D. and Onstad, C.A. (1977). A runoff erosivity factor and variable

slope length exponents for soil loss estimates. Transactions of the ASAE ,20: 683-687.

Foster, G.R. (1982). Modeling the erosion process. Transactions of the ASAE: 297 -380.

Foster, G.R. and Wischmcier, W.H. (1974). Evaluating irregular slopes for soil loss

prediction. Journal Transactions American Society Agricultural Engineers. 17: pp. 305-

309.

Fu, G., Chen, S.and McCool, K.D. (2006). Modeling the impacts of no-till practice on soil

erosion and sediment yield using RUSLE, SEDD and ArcView GIS. Soil tillage

research, 85:38–49.



Fuchs, M. (2007). An assessment of human versus climatic impacts on Holocene soil erosion

in NE Peloponnese, Greece. Quaternary Research 67 (3), 349–356.

Grepperud, S. (1995). Soil conservation and governmental policies in tropical areas: does

aid worsen the incentives for arresting erosion. Agricultural economics, 12:129–140.

Grissinger, K.M. and Murphy, J.B. (1989). Ephemeral gully erosion in the loess uplands,

Gardwin creek watershed. Northern Missippi, USA. Proceedings of 4th International

River Sedimentation Symposium. Beijing. China. IASH Publication. 236: 251-266.

Hibbert, A.R., Davis, E.A. and Scholl, D.G.(2009). Chaparral conversion: Part I. Water yield

response and effects on other resources. USDA Forest Service, Research Paper ,RM-17.

Hoyos, N. (2005). Spatial modeling of soil erosion potential in a tropical watershed of the

Colombian Andes. Catena ,63(1):85–108.

Hudson, N.W. (1995). Soil Conservation. 3rd Edition, Batsford, London. 392p.

Ismail, J. and Ravichandran, S. (2008). RUSLE2 model application for soil erosion

assessment using remote sensing and GIS. Water resource management, 22(1):83–102.

Jain, M.K. and Das, D. (2010). Estimation of sediment yield and areas of soil erosion and

deposition for watershed prioritization using GIS and remote sensing. Water resource

management, 24(10):2091–2112.

Jasrotia, A.S. and Singh, R. (2006). Modeling runoff and soil erosion in a catchment area,

using the GIS, in the Himalayan region, India. Environment geology, 51:29–37.

Jose ,S.K, Varghese,A., Kumar,S, Madhu, G., Rakhi C. and Sreekala T.K.(2011). Soil erosion

modeling using GIS and Remote sensing. A case study of Shendurney Wildlife

sanctuary Southern Western Ghats, Kerala. Ecology Environment and Conservation,

2011 (4).

Judson, S. (1965). Physical Geology. Prentice Hall. NJ, USA. 3rd Edition, pp. 143-144.

Jusoff, K. and Chew, S. (1998). Soil Erosion Risk Assessment Using Satellite Data in Genting

Highland . Proc. of International Symposium of Arid Region Soil. Izmir,Turkey.

Kheir, R.B., Cerdan, O. and Abdallah, C. (2006). Regional soil erosion risk mapping in

Lebanon. Geomorphology ,82 (3–4), 347–359.

Kouli, M., Soupios, P. and Vallianatos, F. (2009). Soil erosion prediction using the Revised

Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern

Crete, Greece. Environment geology, 57:483–497



Krishna Bahadur, K.C. (2009). Mapping soil erosion susceptibility using remote sensing and

GIS: a case of the Upper Nam Wa Watershed, Nan Province, Thailand. Environment

geology ,57:695–705.

Le Bissonnais, Y., Montier, C., Jamagne, M., Daroussin, J. and King, D. (2001). Mapping

erosion risk for cultivated soil in France. Catena, 46 (2–3), 207–220.

Le Rouxa, J.J., Newbyb, T.S. and Sumner, P.D. (2007). Monitoring soil erosion in South

Africa at a regional scale: review and recommendations. South African journal of

science, 103:329–335.

Lee, S. (2004). Soil erosion assessment and its verification using the Universal Soil Loss

Equation and Geographic Information System: a case study at Boun, Korea.

Environment geology,45:457–465.

Li, Z. and Wang, Z. (1990). The Basis of Engineering Geology. Publishing Company of China

Geoscience University, pp. 176–179.

Lim, K.J., Myung- Sagong, M., Engel, B.A., Tang, Z., Choi, J.and Kim, K.M. (2005). GIS-

based sediment assessment tool. Catena, 64:61–80.

Lin, C.Y., Lin, W.T. and Chou, W.C. (2002). Soil erosion prediction and sediment yield

estimation: the Taiwan experience. Soil tillage research, 68:143–152.

Lu, D., Li, G., Valladares, G.S. and Batistella, M. (2004). Mapping soil erosion risk in

Rondonia, Brazilian Amazonia: using RUSLE, remote sensing and GIS. Land

degradation and development, 15:499–512.

Maass, J.M., Jordan, C.F. and Sarukhan, J. (1988). Soil erosion and nutrient losses in seasonal

tropical agro-ecosystems under various management techniques. Journal of Applied

Ecology ,25, 595–607.

Mehlman, D.W., Rosenberg, K.V., Wells, J.V. and Robertson, B. (2004). A comparison of

North American avian conservation priority ranking systems. Biological

Conservation, 120 (3), 383–390.

Merritt, W.S., Letcher, R.A. and Jakeman, A.J. (2003) A review of erosion and sediment

transport models. Environmental modeling and software, 18:761–799.

Millward, A.A. and Mersey, J.E. (1999). Adapting the RUSLE to model soil erosion potential

in a mountainous tropical watershed. Catena, 38(2):109–129.



Mongkolsawat, P., Thirangoon, and Sriwongsa, S. (1994). Soil erosion mapping with

Universal Soil Loss Equation and GIS. Proceedings of the 15th Asian Conference on

remote sensing. Bangalore, India.

Morgan, R. P. C., Quinton, J. N., Smith, R. E., Govers, G., Poesen, J. W. A., Auerswald, K.,

Cnisci, G. and Torri, D. (1998). ‘The EUROSEM model’, in Boardman, J. B. and

Favis-Mortlock, D. (Eds.) Global Change: modelling soil erosion by water, NATO

ASI series, Series 1: Global environmental change. Springer Verlag, London, 373–38.

Morgan, R. P.C. (1979). Soil erosion. Longman, London, pp. 21 -24.

Morgan, R.P.C. (1994). Soil Erosion and Conservation, Crane field University, Longman.

Morgani, R.P.C., Quintoni, J.N., Smith, R.E., Govers, G., Poesen, J.W.A., Auerswald, K.,

Chisci ,G., Torri, D.and Styczen, M.E. (1998). The European soil erosion model

(EUROSEM): a dynamic approach for predicting sediment transport from fields and

small catchments. Earth surface process and landforms, 23:527–544.

Narayana, V.V.D. and Babu, R. (1983). Estimation of soil loss in India. Journal of irrigation

and drainage engineering, 109(4):419–433.

Omakupt, M. (1989). Soil Erosion Mapping using remote sensing and GIS. Proceedings of the

10th ACRS. Nov. 23 - 29. 1989, Kuala Lumpur. Malaysia.

Onyando, J.O., Kisoyan, P. and Chemelil, M.C. (2005). Estimation of potential soil erosion

for River Perkerra catchment in Kenya. Water resource management,19:133–143.

Ownegh, M. (2003). Land use planning and integrated management of natural hazards in Golestan

Province. International Seminar on flood hazard prevention and mitigation 15-16

January. (2003). Gorgan, Iran. Abstract, p 9.

Pandey, A., Chowdary, V.M. and Mai, B.C. (2007). Identification of critical erosion prone

areas in the small agricultural watershed using USLE, GIS and remote sensing.

Water Resource Managemnet, 21:729- 746.

Pimentel, D. C., Harvey, P., Resosudarmo, K., Sinclair, D., Kurz, M., McNair, S., Crist, L.,

Sphntz, L., Fitton, R., Saflburi, and Blair, R. (1995). Environmental and Economic

Costs of Soil Erosion and Conservation Benefits. Science, 267: pp. 1117-1123.

Pruski, F.F.and Nearing, M.A. (2002). Climate-induced changes in erosion during the 21st

century for eight U.S. locations. Water Resources Research ,11:1298.



Renard, K.G., Foster, G.R., Weesies, G.A., McCool, D.K. and Yoder, D.C. (1997) .Predicting

soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss

Equation (RUSLE). Agriculture Handbook No. 703, USDA-ARS.

Renard, K.G., G.R. Foster. G.A. Weesies and P.J. Porter, 1991. RUSLE Revised Universal Soil

Loss Equation.J. Soil and Water Conservation. January-February: pp.30-33.

Richardson,C.W., Foster, G.R. and Wright, D.A. (1983). Estimation of erosion index from

daily rainfall amount. Transactions of the ASAE. 26(F): pp. 153-156. 160.

Rinos, M.H.M., Agarwal, S.P. and De Silva, R.P.(2001). Application of Remote sensing and

GIS on soil erosion assessment at Uata River Basin. India. Procedings of 21th Asian

Conference on Remote Sensing. Taipei. Taiwan www.gisdevelopment.net.

Saha, S.K. and Pande, L.M. (1993). Integrated approach towards soil erosion inventory for

environmental conservation using satellite and agrometeorological data. Asia-

Pacific remote sensing journal, 5(2):21–28.

Şahin, S. and Kurum, E. (2002). Erosion risk analysis by GIS in environmental impact

assessments: a case study — Seyhan Kopru Dam construction. Journal of

Environmental Management, 66, 239–247.

Samad, R. and Patah, N.B. (1997). Soil Erosion and Hydrological Study of the Bakun Dam

Catchment Area, Sarawak Using Remote Sensing and Geographic Information

System (GIS). Proc. 18th Asian Conference on Remote Sensing, Malaysia.

www.gisdevelopment.net.

Shakesby, R.A., Coelho, C.O.A., Ferreira, A.J.D. and Walsh, R.P.D. (2002). Ground level

changes after wildfire and pouching in eucalyptus and pine forests, Portugal:

implications for soil microtopographical development and soil longevity. Land

degradation and development, 13, 111–127.

Shrimali, S.S., Aggarwal, S.P. and Samra, J.S. (2001). Prioritizing erosion-prone areas in

hills using remote sensing and GIS — a case study of the Sukhna Lake catchment,

Northern India. International Journal of Applied Earth Observation and Geoinformation ,3

(1), 54–60.

Solanki,R.C. and Singh, A. (1996). Soil erosion research on steep slopes in north-eastern hill

region of India—a review. Indian Journal of Soil Water Conservation, 24(3):187–192.



Sthiannopkao, S., Takizawa, S., Homewong, J., Wirojanagud, W. (2007). Soil erosion and its

impacts on water treatment in the northeastern provinces of Thailand. Environment

international, 33:706–711.

Suresh Babu, D.S., Nandakumar, V., Biju, J., Jayaprasad, B.K. and Pramod, S.V. (2000).

Siltation analysis in the Neyyar reservoir and forest degradation in its catchment: A

study from Kerala state. India. Environment geology, 39(3-4): 390-397.

Thampapillai, D. and Anderson, J.R. (1994) . “A Review of the Socio-Economic Analysis of

Soil Degradation Problems for Developed and Developing Countries.” Review of

Marketing and Agricultural Economics, 62(3):291-315.

Tian, Y.C., Zhou, Y.M., Wu, B.F. and Zhou, W.F. (2009). Risk assessment of water soil

erosion in upper basin of Miyun Reservoir, Beijing, China. Environonment geology,

57:937–942.

Toy,J.T., Foster, G.R. and Renard, K.G.(2002). Soil Erosion: Processes, Prediction, Measurement,

and Control. John Wiley and Sons, New York. 352p.

Vrieling, A. (2006). Satellite remote sensing for water erosion assessment: a review. Catena

,65 (1), 2–18.

Wang, G., Gertner, G., Fang, S. and Anderson, A.B.(2003). Mapping multiple variables for

predicting soil loss by geostatistical methods with TM images and a slope map.

Photogrammetry engineering and Remote sensing, 69:889–898.

Wang, G., Hapuarachchi, P., Ishidaira, H., Kiem, A.S. and Takeuchi, K. (2009). Estimation of

soil erosion and sediment yield during individual rainstorms at catchment scale.

Water Resource Managemnet, 23: 1447–1465.

Wischmeier, W.H. and Smith, D.D. (1978). Predicting rainfall erosion losses—a guide to

conservation planning. Agriculture Handbook No. 537. US Department of Agriculture

Science and Education Administration, Washington, DC, USA, 163 pp.

Xu, Y., Zhou, Q. and Li, S. (2005). An analysis on spatial–temporal distribution of rainfall

erosivity in Guizhou province. Bull soil water conservation, 4:11–14.

Yoder, D. and Lown, J. (1995). The Future of RUSLE: inside the new revised universal soil

loss equation. Journal of soil water conservation, 50(5):484–489



Yue-qing, X., Jian, P. and Xiao-mei, S. (2009). Assessment of soil erosion using RUSLE and

GIS: a case study of the Maotiao River watershed, Guizhou Province, China.

Environment geology, 56:1643–1652.

Yuksel, A., Gundogan, R. and Akay, A.E. (2008). Using the remote sensing and GIS

technology for erosion risk Mapping of Kartalkaya Dam Watershed in

Kahramanmaras, Turkey. Sensors, 8:4851–4865.

Zhang, Y., Degroote, J., Wolter, C. and Sugumaran, R. (2009). Integration of modified

universal soil loss equation (MUSLE) into a GIS framework to assess soil erosion

risk. Land degradation and development ,20: 84–91.

Zhou, W. and Wu, B. (2008). Assessment of soil erosion and sediment delivery ratio using

remote sensing and GIS: a case study of upstream Chaobaihe River catchment, north

China. International journal of sediment research, 23:167–173.

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