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Using GIS, RS for soil erosion mapping

The Northeastern area of Thailand has faced natural disasters such as floods, droughts and soil erosion. This results in the loss of life, natural resources, economic foundation, and environmental and ecological systems (Teerawong, 2002). Conserved national resources have been used to help flood victims and to restore the affected areas and structures. As natural resources are more heavily exploited, these incidents happen more frequently and with greater severity. Although these natural catastrophes cannot be avoided, their effects can be reduced by proper measures and management. In the past, the natural disaster was managed by a relief effort and restoration of the affected areas. However, when disasters become more frequent and severe, the relief and restoration budget became larger. Prevention and mitigation required far more efficient ways to manage the problem. In this study, the author used GIS and a well known parametric equation, the Universal Soil Loss Equation (USLE), to evaluate the risk area of soil erosion in the case of Maha Sarakham province in Thailand for the year 2010.

Materials and methods

The study area
Maha Sarakham province, which consists of 13 districts, 133 sub-districts and 1804 villages, was selected as a study location. It is shown in Figure 1. Maha Sarakham borders Kalasin to the north, Surin and Buriram to the south, Roi-Et to the east and Khon Kaen to the west (Teerawong, 2012). For political and administrative structure, areas in Maha Sarakham are divided into 13 districts, 133 sub-districts, and 1804 villages. In 2011 the province had a total population of 940,911, of which 466,552 were male and 474,359 were female (Teerawong and Poramate, 2012).

Figure 1 Maha Sarakham province

The use of USLE
The USLE is the most commonly used estimator of soil loss caused by overland erosion. The equation is based on an extensive set of more than 10,000 plot years of runoff and soil loss data from experimental centres in the eastern USA. It was developed to predict average annual soil loss from sheet and rill erosion, not gully or other forms of erosion. The USLE may properly be used to (Wishmeier, 1978; Okalp, 2005; Ozcan, 2008; Sawet et al., 2011): 1) Predict average annual soil movement from a given field slope under specified land use and management conditions, 2) Guide the selection of conservation practices for specific sites, 3) Estimate the reduction in soil loss that would result from a change in cropping or conservation practices, 4) Determine how conservation practices may be applied or altered to allow more intensive cultivation, 5) Estimate soil losses from land use areas other than agricultural purposes, 6) Provide soil loss estimation for determining conservation needs (Shahram and Leroy, 2001). The USLE, derived empirically, is (Lal, 1994):

A = R × K × LS × C × P                (1)

This equation, detailed in Wishmeier and Smith (1978), describes soil loss (A) as a function of rainfall (R) amount and intensity; soil erodibility (K) related to texture, percentage of organic matter, structure, and permeability of soil; morphology, especially the length of the slope (L) and the slope gradient (S); vegetation cover (C); and erosion control practices (P).

The USLE was designated “universal” because it is free of some of the generalisations and the geographic and climatic restrictions inherent in earlier models. It has been criticised as not being universal because original parameter values were presented for conditions of the eastern two-thirds of the United States. Regardless of whether the name is fully accurate, the USLE identifies the major factors affecting soil loss.

Each of the five factors in the USLE has been formulated by Wishmeier and Smith (1978) in such a way that it is linearly related to soil loss. Each variable can be isolated and quantified into numbers using standard USLE plots or unit plots. When the variables of the USLE are multiplied together, the answer is the amount of soil loss. Owing to its simplicity and the relative ease of evaluating each factor, in most parts of the world, including countries in Asia-Pacific like Thailand, China, and India, its gives acceptable results (Omakupt, 1986, 1989; Ma et al., 1987; Saha and Singh, 1991; Teerawong, 2002). USLE can be used in Thailand to assess soil loss erosion. In this work, the USLE was used to evaluate the risk area of soil erosion.

Assessing soil erosion
Using the above as a background, an assessment of soil erosion using GIS was carried out according to procedures developed by the author. The flowchart of this procedure is illustrated in Figure 2.

Figure 2 Flowchart the study

The five parameters of USLE were evaluated for the study area using remotely sensed ground observation and existing map data. Remotely sensed data was the main source of information for the establishment of land-use/land-cover, geology, geomorphology, and soil map, as well as for deriving a scheme of watershed distribution using both digital image processing and visual interpretation.

Rainfall amount and intensity maps were established based on observation data obtained over several decades. The processing and/or interpretation work involved deriving each factor. The final potential soil erosion and soil erosion hazard maps are described below.

1. Rainfall Factor (R)
R factor (rainfall erosivity) is the principal function of USLE. Maximum rainfall intensity for 30 years expressed as a kinetic energy of rainfall is used to compute the R factor, as it is reported to have the best correlation with the soil loss rather than lower or higher intensity. R is expressed in terms of annual erosivity in ton/ha/yr. The formula is shown in equation 2 (EL-Swaify et al., 1985).

R = 38.5+0.35P (2)

where P = total rainfall amount in mm.

2. Soil erodibility factor (K)
A monogram developed by Wishmeier and Smith (1978) was used to obtain the value of the (K) factor on the basis of the percentages of silt, very fine sand, and organic matter (a); soil structure (b); and permeability (c). The first three physical maps were used, especially in the case of medium and small scales. Field observations are available for deriving permeability, and were gathered in some cases when information on the soil profile was insufficient. The factor (K) can be estimated by using equation 3.

K = 2.1M1.14 (10 -4) (12-a) + 3.25 (b-2) + 2.5 (c -3) (3)

where a = % of organic matter
b = soil structure class
c = soil permeability class
M= (% silt + % very fie sand) or (100- % clay)

3. Slope factor (LS)
An aerial unit, upon which determination of slope and corresponding slope length are based, is identified by slope and aspect classes. Slope and aspect map layers are generated from a digital elevation model. These are, in turn, generated from elevation contours of topographic maps at 1:50,000 scale. Overlay operation on the slope and aspect layers yields a polygonal layer, each polygon of which is an area unit used for determination of slope length. The LS-factor layer is then generated from equation 4 (Wishmeier and Smith, 1978).

LS = (L/22.1) * (0.065 + 0.04SS + 0.0065S2) (4)

where m = 0.5 for S> 5%
0.4 for S 3-5%
0.3 for S 1-3%
0.2 for S < 1%
S = slope (%), L = slope length (m).

4. Vegetation cover factor (C) and erosion control practice factor (P)
The factors (C) and (P) were estimated based on land-use/land-cover maps, which are produced from multi-temporal satellite data (Landsat-TM on 2010) based on the digital image processing method and from research results obtained from field data at soil erosion observation stations situated in Thailand as well as in other countries in Southeast Asia.

GIS for soil erosion
Each of the USLE factors with associated attribute data is digitally encoded in a GIS database to eventually produce five thematic layers. These are then spatially overlaid to produce a resultant polygonal layer. Application of the USLE model to the resultant layer yields a soil erosion map with 3 classes of soil loss.

Results and discussion
The soil erosion mapping resulting from the spatial overlay of USLE factors in the Maha Sarakham province is presented in Table 1. Figure 3 presents corresponding quantitative soil loss, in addition to the spatial information.

Table 1 Illustration soil erosion in Maha Sarakham Province

Note: The area unit “rai” is a Thai unit of measurement in which 1 hectare equals 6.25 rai.

Figure 3 Risk area of soil erosion

The Landsat-TM for land-use/land-cover type classification in the Maha Sarakham province has given satisfactory results. This land-use/land-cover was used for soil erosion mapping in the study area. In addition, this study demonstrates how GIS can provide very detailed information to locate areas that are at risk of soil erosion and areas that are suitable for agriculture. The results obtained from this study project may not be directly applicable in the field without thorough field checking. Nevertheless the model applied in this study demonstrates a potential application of remote sensing and GIS in soil erosion mapping.

One of our most important natural resources is soil. The factor which most damages and reduces its productivity is soil erosion. Soil erosion causes serious financial losses in the countries whose economies are dependent on the efficiency and workability of soils. Some developed countries in the world produce various soil erosion models by taking the specifications of their own area into consideration. The most common of these models are USLE, CORINE, ICONA, etc (Fazli et al., 2012) While some of these models assess soil erosion risk, some present current erosion conditions and some of them specify potential soil erosion quantitatively. The Universal Soil Loss Equation (USLE) method, used to calculate soil loss in a work area or basin per year per unit area per unit of time, is also a commonly used and efficient model for determining the precautions against soil erosion in areas under threat of soil erosion.


  • Teerawong Laosuwan. (2002). Integration of remote sensing and geographic information system for assigning risk area of drought in Changwat Mahasarakham, Final report, Department of Physics, Faculty of Science Mahasarakham University.
  • Teerawong Laosuwan. (2012).Online web GIS– based services for spatial data and sharing of leptospirosis epidemiology information; Development of pilot project in Mahasarakham province Thailand. International journal of Geomatics and Geosciences, 4 (1), 121-133.
  • Teerawong Laosuwan and Poramate Chunpang.(2012).Design and Development a Novel Web-based GIS for Surveillance and Monitoring of Diarrhea by Using of Free Open Source Software, Innovative Systems Design and Engineering, 3 (6), 42-53
  • Wischmeir, W.H. and Smith, D.D. (1978). Predicting Rainfall Erosion Losses. A Guide to Conservation planing. USDA Agric. Handboo No. 537
  • Okalp, Kivanç (2005). Soil Erosion Risk Mapping Using Geographic Information Systems: A Case Study on Kocadere Creek Watershed, Izmir. Ph.D thesis, Middle East Technical University, Turkey
  • A. Ugur Ozcan, Gunay Erpul, Mustafa Basaran, H. Emrah Erdogan. (2008). Use of USLE/GIS technology integrated with geostatistics to assess soil erosion risk in different land uses of Indagi Mountain Pass–Çankiri, Turkey, Environmental Geology, 53 , pp. 1731–1741
  • Sawet PlakayrungrassameeWichai Pantanahiran, and Charlie Navanugr. (2011). Soil Erosion Analysis Using Universal Soil Loss Equation (USLE) to Estimate the Loss of Plant Nutrient in Huaimaeprachan Watershed. Journal of Social Science, Srinakharinwirot University, 14 (6).
  • Shahram Khosrowpanah and Leroy F. Heitz. (2001). Rainfall Erosivity Factor (R-Factors) for Selected Islands in the Federtade States of Micronesia (FSM).
  • Lal, R. 1994. Tillage effects on soil degradation, soil resilience, soil quality, and sustainability. Soil Tillage Research, 27, 1–8.
  • Saha, S.K. and Singh, B.M. (1991). Soil erosion assessment and mapping of the Aglar river watershed (Uttar Pradesh) using remote sensing technique Photonirvachak.Jr. Of the Indian Society of Remote Sensing, 19 (2), 67-76.
  • Ma Ai Nai, Zhang Xuding, Liang Zhengya and Yi Qing (1987). Study on the soil erosion information system. Proceedings of the International Workshop on GIS, Beijing, Chaina. Omakupt, M. and Sawattanakoon S. (1986). Evaluation of C-factor (Kenaf) in the Universal Soil Loss Equation Seminar on Research and Technolog Development LDD.
  • Omakupt, M. (1989). Soil Erosioin Mapping Using Remote Sensing Data and GIS. Proceedings of the 10th ACRS Nov. 23-29, 1989. Kuala Lumpur, Malaysia.
  • El-Seaify, S.A.W.C. Moldenhauer and A.Lo. (1985). Soil Erosion and Conservation. Soil Conservation Society of America, Ankeny, lowa.
  • Fazli Engin TOMBUS, Mahmut YÜKSEL, Murat SAHIN, Ibrahim Murat OZULU and Mustafa COSAR. (2012). Assessment Of Soil Erosion Based On The Method USLE; Çorum Province Example, Knowing to manage the territory, protect the environment, evaluate the cultural heritage Rome, Italy, 6-10 May 2012.