Amit Kumar & Mamta Devi & Benidhar Deshmukh

Received: 19 September 2013 /Accepted: 15 May 2014 /Published online: 27 May 2014# Springer Science+Business Media Dordrecht 2014

Abstract Soil loss due to water erosion was estimated in Kangra region of western Himalayausing revised universal soil loss equation modelling (RUSLE) in conjunction with RemoteSensing (RS) and Geographic Information Systems (GIS). The various parameters such asrainfall erosivity (R), soil erodibility (K), topographic factor (LS), crop management factor (C)and support practice factor (P) were derived using standard techniques. The study revealed thatforest cover, crop land and scrub/grass land constitute 87.4 % of soil erosion susceptible area.The rate of depletion of soil was estimated at 25.63 t/ha/yr. It was highest in stony/barren land(60.3 t/ha/yr) and lowest in case of tea garden (16.09 t/ha/yr). It was felt that there is a need ofimplementation of soil and water conservation measures in the region to curb the soil loss. Theundulating nature of terrain was observed as the main contributing factor for soil erosion. Itwas concluded that RS and GIS based RUSLE model can be efficiently used in mountainousregions to determine the status and extent of soil erosion.

Keywords Conservation practices . DEM . Length of slope . NDVI . Rainfall erosivity . Soilerodibility . Support parameter . Soil loss

1 Introduction

Soil erosion is deterioration of soil by the physical movement of its particles from a given site.It is second biggest problem the world faces next to population growth (Pimentel 2006).Worldwide the damage from soil erosions is estimated to be~$400 billion per year (Fletcher2006). About 30 % of the worlds arable land has become unproductive as 60 % of its soil has

Water Resour Manage (2014) 28:33073317DOI 10.1007/s11269-014-0680-5

A. Kumar (*)RS-GIS Laboratory, CSIR-Institute of Himalayan Bioresource Technology, Council of Scientific &Industrial Research, Palampur, Himachal Pradesh, Indiae-mail: amitkr@ihbt.res.in

M. DeviRemote Sensing and GIS Division, University of Allahabad, Allahabad, Uttar Pradesh, India

B. DeshmukhSchool of Sciences, Indira Gandhi National Open University, New Delhi, India

Integrated Remote Sensing and GeographicInformation System Based RUSLE Modellingfor Estimation of Soil Loss in Western Himalaya, India

been washed away and deposited in rivers, streams and lakes, that has made these water bodiesprone to flooding and contamination with pesticides present in soil (Lang 2006). An estimated53 % of total land of India is under soil erosion (Narayana and Babu 1983). Approximate5,334 million tonnes of soil is eroded every year at the rate of 16.4 t/ha/yr due to water erosiondepleting 800 ha/yr of arable land in India (Kothyari 1996). In recent years, seriousness of soilerosion has been felt in mountainous areas. The entire Himalayan region is prone to soil loss(Jain et al. 2001; Rawat et al. 2013) and in Himachal Pradesh (H.P.) of western Himalaya itself,~280 million tonnes of soil is lost annually (Singh 2008). About 49 % of catchments of itsperennial rivers are degraded resulting in declining productivity, loss of biodiversity, increasedsedimentation of reservoirs, drying up of water resources, recurring flash floods and deterio-rating environment (Sharma et al. 2008). It is estimated that 54 % area of the H.P. is degradedand 98 % of such erosion is only due to water. The diverse nature of forest, soil, climate anduneven complex terrain has resulted in a varied topography, which makes the region an idealterrain for soil erosion due to flowing water (Hurni et al. 1996; Liniger and Thomas 1998). Inthis situation, quantification of soil loss and delineation of degraded areas is necessary foreffective conservation planning (Yadav and Sidhu 2010).

In the recent past modelling for soil loss assessment using Remote Sensing (RS) andGeographic Information System (GIS) technologies have been carried out by many investi-gators (Benzer 2010; Biswas 2012; Dabral et al. 2008; Pandey et al. 2007; Sheikh et al. 2011).These works have concluded that the ability of RS to generate latest ground information andstrength of GIS to handle voluminous spatial data help in rapid assessment of the potential forsoil erosion in a region. Among soil loss estimation models, Universal Soil Loss Equation(USLE) is considered as the best model and is being used worldwide for estimation of surfaceerosion (Wischmeier and Smith 1978). It is an empirical model developed initially for fieldcrops in 1954 (Wischmeier and Smith 1978). It is designed to model mainly sheet and rillerosion caused by overland flow in agricultural area (Merritt et al. 2003). A revised version ofthis model (RUSLE) has enhanced soil loss predication capabilities and can be applied tonatural environmental conditions (Renard et al. 1997). RUSLE predicts the long term averageannual rate of soil erosion (Dais 2008) in variety of environment such as agriculture, forest,rangeland, mining sites, construction sites, etc. (Stone and Hilborn 2000). The RUSLEequation is written as

A R x K x LS x C x P 1Where,

A = average annual predicted soil loss from sheet and rill erosion (tons/ha/year)R = rainfall/runoff erosivity (MJ mm/ha/hr/year)K = soil erodibility (Mg h/MJ/mm)LS = slope length and steepness/topographic factor (dimensionless)C = crop management (dimensionless)P = support practices (dimensionless)

R factor is rainfall erosivity that depends on the intensity and duration of rainfall (Stoneand Hilborn 2000). K factor is soil erodibility factor and is a measure of susceptibility ofsoil particles to detachment and transport by runoff (Stone and Hilborn 2000). LS factor ortopographic factor is the combined effect of length and steepness of slope. The steeper andlonger the slope the higher is the risk for erosion. Crop management factor (C) depend uponvarious land use/landcover (LULC) prevailing in the region. It is generally used to determinethe relative effectiveness of soil and crop management systems in terms of preventing soil loss(Roose 1976). P factor is support practice factor and can be computed from the slope and

3308 A. Kumar et al.

crop cover conditions. It reflects the effect of practices that reduce the runoff rate and check thesoil loss (Stone and Hilborn 2000).

Among RUSLE factors, the C factor has been considered as most important as it is aconservation related factor that control soil loss at a specific site (Teh 2011). The RUSLEmodelling using literature based C factor may lead to discrepancies in the results as they havebeen computed for some other locations (Bhattarai and Dutta 2007; Khosrowpanah et al.2007). This is the greatest drawback of RUSLE as it is ineffective in applications outside therange of conditions for which it has been developed (Saha 2005). But on the other hand,RUSLE also allows each factor to be independently updated with improved factors withoutchanging the base RUSLE equation (Khosrowpanah et al. 2007). Thus C factor if derived forthe targeted area may enhance the accuracy of the RUSLE model, which can be achievedthrough remote sensing (Song et al. 2011; Suriyaprasita and Shrestha 2008). The C factorderived using satellite data is the true representation of prevailing crop management practicesin the ground and it can be updated more frequently. Keeping these conditions in the backdrop,the present study was carried out to understand nature and extent of soil loss in Kangra regionof H.P. in Indian western Himalaya (Fig. 1).

2 Materials and Methods

The Kangra is located between 3141 and 32 28 N and 7535 to 7704 E. (Fig. 1). It hasgeographical spread of about 5,739 km2 and elevation ranges from 248 to 5,861 m amsl in theregion. The climate varies from sub-tropical in low hills and valleys, to sub-humid in the mid

Fig. 1 Map of the Study area

Integrated RS and GIS RUSLE Modelling 3309

hills and temperate in high hills. River Beas and Ravi constitute the main drainage systems.Soils of the district may be categorized into Low-hill soil zone, Mid-hill soil zone, High-hillsoil zone and Mountainous soil zone (DHDRK 2002).

The analysis for soil loss estimation was done using Arc GIS (10), Arc View (3.3) andErdas Imagine (8.6) softwares. The various steps (Fig. 2) involved in preparation of RUSLEfactors have been described as follows:

2.1 Preparation of R-Factor (Rainfall Erosivity) Map

Tropical Rainfall Measuring Mission (TRMM) monthly rainfall data of 2011 from NASA(http://pmm.nasa.gov/node/158) was used for the calculation of R factor. Prior to that,Fishers test was performed between TRMM data and rainfall data recorded at a fieldobservatory located inside the study area. As the result of the test was statistically non-significant at 5 % level of significance (=0.04; t=1.77; df=52), it suggested that the TRMMdata can be used as a replacement of field observatory data. Therefore, the TRMM rainfall datain ESRI grid format was added and averaged by grid add and grid average functions ofspatial analyst extension of ArcGIS 10 to produce annual average rainfall map of the studyarea. The following equation (Singh et al. 1981) was then applied to annual rainfall map toderive R-factor map (Fig. 3a):

Rfactor 79 0:363 R R annual average rainfall in mm 2

2.2 Preparation of K-Factor (Soil Erodibility) Map

Soil map from National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), Nagpuron 1:500,000 scale was used to derive K factor. The hard copy of soil map was scanned andgeo-referenced. The soil boundaries were then digitized over geo-referenced soil map in ArcView 3.3 environment and soil attributes were added to digitized map. The Soil erodibilitymap (Fig. 3b) was finally prepared by assigning K-values (Table 1) to the respective soil types.

Fig. 2 Methodology for computation of RUSLE factors

3310 A. Kumar et al.

2.3 Preparation of Ls-Factor (Topographic Factor) Map

Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) was used tocalculate for LS factor. LS-factor (Fig. 3c) was prepared using a tool originally developed inArc Macro Language (Hickey 2001) and later modified using C++ programming language(Van et al. 2004). The DEM of the study area in ASCII format was given as an input to thisprogram. The program calculates LS factor by applying following equation (Wischmeier andSmith 1978) to each grid cell of the input DEM:

LS =72:6 m 65:41 sin2 4:56 sin 0:065 3Where,

= cumulative slope length in meter = the downhill slope angle

Fig. 3 Maps depicting (a) rainfall erosivity (b) soil erodibility (c) topographic factor, and (d) crop managementfactor in the study area

Table 1 Soil types and their K-Values

(Source: Jain et al. 2010)

Soil type K- values (Mg h/MJ/mm)

Loamy soils 0.020

Loamy skeletal soils 0.023

Coarse loamy soils 0.032

Sandy soils 0.042

Integrated RS and GIS RUSLE Modelling 3311

m=slope contingent variable (0.5 if the slope angle is greater than 2.86, 0.4 on slopes of1.72 to 2.86; 0.3 on slopes of 0.57 to 1.72; 0.2 on slopes less than 0.57).

2.4 Preparation of P-Factor (Support Factor) Map

The P-factor (Fig. 3d) was computed from the slope and LULC. The slope map (%) wasprepared using DEM in Erdas Imagine 8.6 and it was merged with LULC using unionfunction. The P-values (Table 2) were then assigned to the merged classes for the preparationof P-factor map.

2.5 Preparation of C-Factor (Crop Management Factor) Map

For the preparation of C-factor map, LULC classification of LANDSAT satelliteimages (path 147, row 38, dated 31.05.2011 and path 148, row 38, dated07.06.2011) of the study area was carried out using digital image processing tech-niques (Jensen 1996; Lillesand et al. 2008). Prior to that, the LANDSAT images wereatmospherically corrected using FLAASH atmospheric correction module (Kaufmanet al. 1997). The classified map categorized the study area into crop land, alpinepastures, forest cover, scrub land, tea garden, built-up land, sandy area, scree slope,snow covered area, stony/barren area and water bodies. The C values (Table 2)available in the literatures (Wanielista and Yousef 1993) were then assigned to aboveLULC classes for the preparation of C factor map (Fig. 4a).

Table 2 Land use/land cover classes and their C and P values

Land use/land cover classes C-values basedon literatures

C-valuesfrom NDVI

P- values Area (%)

Alpine pasture 0.08 0.38 1 5.52

Built-up land 0.01 0.19 1 1.30

Crop land (38 % slope) 0.08 0.17 0.5 8.36

Crop land (812 % slope) 0.08 0.17 0.6 3.13

Crop land (1216 % slope) 0.08 0.17 0.7 1.70

Crop land (1620 % slope) 0.08 0.17 0.8 1.03

Crop land (2025 % slope) 0.08 0.17 0.9 0.98

Crop land (less than 3 % & more than 25 % slope) 0.08 0.17 1 8.75

Forest cover 0.005 0.13 1 31.86

Sandy area 1 0.29 1 2.22

Scrub land 0.01 0.18 1 15.03

Scree slopes 0 0 0 9.68

Snow covered area 0 0 0 1.48

Stony/barren area 0 0.42 1 0.63

Tea garden 0.01 0.08 1 0.55

Water bodies 0 0 0 7.77

(Wanielista and Yousef 1993)

(Londhe et al. 2010)

(Wischmeier and Smith 1978)

3312 A. Kumar et al.

In addition, a C factor map was also prepared from satellite data using following equation(Kouli et al. 2009; Zhou et al. 2008), which has already been used and tested for Indianmountainous region (Prasannakumar et al. 2011):

C exph NDVI= NDVI 4

Where,NDVI = Normalized Difference Vegetation Index (Jensen 1996; Lillesand et al. 2008), and

and are constants (=2 and =1; Vander-Knijff et al. 2000)

NDVI LANDSAT BAND 4 LANDSAT BAND 3 LANDSAT BAND 4 LANDSAT BAND 3 5

The atmospherically corrected LANDSAT TM temporal images acquired in the month ofNovember and March were used for the calculation of C factor as images of these monthshave been reported as ideal for deriving C values (Alexandridis et al. 2013). In Novemberand March, deciduous forest shades their leaves and crops are harvested leading to sparsevegetation cover in the region. The C values obtained for these 2 months were averaged andthe resulting values were considered as final C values (Fig. 4b). The LULC map of the studyarea was finally overlaid on it to derive C values for different LULC classes (Table 2) in thestudy area.

2.6 Estimation of Soil Loss

The above derived RUSLE factors (R, K, LS, C and P factors) were multiplied together for theestimation of soil loss in the study area (Fig. 5). The calculated soil loss values were finallycategorized in to seven erosion classes (Table 3) following the classification system proposedfor soil loss in Indian context (Singh et al. 1992).

3 Results and Discussion

The result showed that the Baijnath region has lowest rainfall erosivity (238.72 MJ mm/ha/hr/year) while the highest erosivity was observed in Dharamshala region (1144.04 MJ mm/ha/hr/

Fig. 4 Crop management factors (a) derived from literatures, and (b) computed from NDVI

Integrated RS and GIS RUSLE Modelling 3313

year) as depicted in Fig. 3a. The study area has four types of soils such as loamy, loamyskeletal, coarse loamy and sandy soil. Among them, the areas bearing sandy soil are moreerodible, which cover 12.5 % of the area. Another 77.88 % of the area is occupied with loamysoil which is less erodible in nature as compared to other soil (Fig. 3b). The Length of sloperanged from 1 to 92 in the study area (Fig. 3c). The Dharamshala and Baijnath regions hadhighest average length of slope (15) whereas Kangra, Fatehpur and Jaisinghpur regions hadlowest slope length (9). The largest area was occupied by forest cover (31.86 %) followed by

Fig. 5 Map depicting soil erosion in the study area

Table 3 Soil Loss and their corresponding classes

Erosion class Soil loss (t/ha/yr) Area (%) (C factorbased on published sources)

Area (%) (C factorderived using NDVI)

Negligible 0 1.51 0.88

Slight 0-5 61.88 9.74

Moderate 5-10 8.28 18.73

High 10-20 5.26 22.41

Very high 20-40 2.07 17.42

Severe 40-80 1.11 8.53

Very severe >80 0.96 3.36

No erosion - 18.93 18.93

(Source: Singh et al. 1992)

3314 A. Kumar et al.

23.96 % crop land (Table 2), and 15.2 % under alpine pasture and scree slopes on the higherridges. The 2.85 % area was found to covered with stony/barren land and sandy area.

It was found that the result of soil loss were different (Table 3) when C values from twodifferent sources (published sources and satellite data) were given as inputs to RUSLEmodelling. The reason being the C values provided in published sources pertain to someother locations . Besides the similar C values have been suggested for more than one LULC(0.08 for alpine pasture and crop land; 0.01for built-up land, scrub land and tea garden) inpublished sources. On the other hand C values computed from NDVI are recent, locationspecific and varied for different LULC.

The study revealed that the rate of soil loss due to rill and sheet erosion in the Kangradistrict is 25.63 t/ha/yr. The 39.83 % of the study area was observed under high to very higherosion. The 18.73 % of the region experiences moderate soil erosion and 11.89 % is affecteddue to severe and very severe types of soil loss. The 18.93 % of the study area is unaffecteddue to soil erosion, while 10.93 % areas undergo negligible to slight soil erosion. The rate ofsoil erosion was observed highest in Dharamshala region (37.31 t/ha/yr) followed by Kangra(35.56 t/ha/yr), Palampur (29.48 t/ha/yr), Jaisinghpur (25.32 t/ha/yr), Dhira (22.54 t/ha/yr),Khundian (22.53 t/ha/yr), Baijnath (22.42 t/ha/yr), Thural (22.27 t/ha/yr), Baroh (20.23 t/ha/yr), Harchakian (17.47 t/ha/yr), Jaswan (16.63 t/ha/yr), Jawali (16.28 t/ha/yr), Dera Gopipur(15.86 t/ha/yr), Nurpur (13.31 t/ha/yr), Rakkar (13.06 t/ha/yr), Indora (12.96 t/ha/yr) andFatehpur (12.04 t/ha/yr). The rate of soil depletion was observed more in the regions havinghigh length and steepness of slopes. The soil erodibility was also observed higher in suchregions.

Of the total soil erosion in the region, the forest cover contributes to 24. 67 % of the high tovery severe types of erosion followed by crop land (15.3 %) and, scrub/grass land (14.99 %)and the least in the case of tea garden (0.38 %). The 5.46 % of the moderate soil erosion wasfound in case of crop land followed by forest cover (4.51 %) and, scrub/grass land (1.96 %),and least for tea garden (0.03 %). Similarly, 10.12 % of the negligible to slight soil erosion wasrecorded in case of forest cover followed by crop land (8.8 %), and least in built-up land(0.06 %). The rate of soil erosion was highest in case of stony/barren area (60.3 t/ha/yr)followed by alpine pasture (57.29 t/ha/yr), built-up land (37.53 t/ha/yr), sandy area (31.4 t/ha/yr), scrub/grass land (29.88 t/ha/yr), forest cover (23 t/ha/yr), crop land (18.31 t/ha/yr) and teagarden (16.09 t/ha/yr).

It was noticed that though forest cover, crop land and scrub/grass land constitute largest areasusceptible to soil erosion but the rate of erosion in these cases are less as compared to otherLULC classes. The reason being these are covered by vegetations that prevent soil erosion.Therefore the tea gardens, which are properly managed, are least affected by erosion. The areassuch as stony/barren land and alpine pasture are more prone to soil erosion due to their highlength and steepness of slopes.

4 Conclusion

It was observed that the undulating nature of terrains favored soil erosion in the study area.Therefore, the high length and steepness of slopes in the alpine and stony/barren regions ofDharamshala, Baijnath, Kangra and Palampur have made them vulnerable to soil erosion. Itwas found that though a large proportion of forest cover, crop land and scrub/grass landundergo soil erosion but the rate of soil depletion in such cases are less as they are protected byvegetation cover. The susceptibility of alpine regions and forest cover due to soil erosion is aserious issue that needs to be addressed on priority as they abode rich biodiversity and also

Integrated RS and GIS RUSLE Modelling 3315

influence climate regime of the region. The implementation of soil/water conservation mea-sures and adoption of suitable support practices are therefore recommended in order to curb thesoil loss in the study area. The study also concluded that the RUSLE model along withgeospatial technologies are efficient tools for identification of erosion prone areas in moun-tainous regions like western Himalaya. These technologies help in identification of nature andextent of soil erosion, which may be a guiding point for further field based experiments on soilerosion.

Acknowledgments We acknowledge USGS, USA for LANDSAT satellite data, CGIAR-CSI, USA for DEMand NASA, USA for TRMM rainfall data used in this study. The Council of Scientific & Industrial research isacknowledged for financial and infrastructure support. We are thankful to Dr. P. S. Ahuja, director, CSIR-IHBT,Palampur and staff members of Biodiversity division, CSIR-IHBT for their help. The authors are also grateful toanonymous reviewers and editors for their helpful comments. This is IHBT communication number 3548.

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Integrated RS and GIS RUSLE Modelling 3317

Integrated...AbstractIntroductionMaterials and MethodsPreparation of R-Factor (Rainfall Erosivity) MapPreparation of K-Factor (Soil Erodibility) MapPreparation of Ls-Factor (Topographic Factor) MapPreparation of P-Factor (Support Factor) MapPreparation of C-Factor (Crop Management Factor) MapEstimation of Soil Loss

Results and DiscussionConclusionReferences