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Application of RS and GIS to Land Degradation
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Assessment of watershed degradation and its socioeconomic impacts using Remote Sensing and GIS:
A case study of Trijuga watershed, Nepal
Bhuwneshwar P. Sah1, Shunji Murai2, Kiyoshi Honda1, Karl E. Weber1, Haja Andrianasolo1
12SAR/SERD, Asian Institute of Technology
PO Box 4, Klongluang, Pathumthani 12120, Thailand
2Institute of Industrial Science, University of Tokyo
7-22-1 Roppongi, Minato-ku, Tokyo 106, Japan
Abstract:
This paper is based on a research to develop and test a methodology for assessing the watershed resources degradation over time and seeking its socioeconomic impacts. Universal Soil Loss Equation in conjunction with Remote Sensing and GIS had been utilized for resources monitoring, while household survey had been conducted for socioeconomic status assessment. The land use change had been exceeded the permissible limit along with 44 percent increment in soil erosion Rae between 1978 to 1991 and. The analysis of sensitivity and socioeconomic status had been found strongly correlated with resource degradation. A multiple linear regression model has been developed from these parameters which can be used to simulate the resource degradation speed under the various socioeconomic conditions. The study concluded that, development activities should be concentrated in valley while conservation activities should be focused to the hills, by considering and formulating land use plan.
1. Introduction:
The utilization of a watershed area beyond its carrying capacity to provide food, fiber, and shelter for the exploding population has resulted in its deterioration in most part of the world (FAO, 1985). However, such deterioration is more severe in developing countries including Nepal (Thapa and Weber, 1990). Being an integral part, the natural resources and socioeconomic status of a watershed should be paid equal attention (Erickon, 1995). Unfortunately until now most of the people are confined only to the resources degradation, keeping the social factors aside.
Watershed degradation is a phenomena by which the potentiality of the watershed is getting reduced over time, which can be confined to the forest loss and the rate of soil erosion increment, if other factors are negligible (Kelly, 1983). These resources can be monitored by using Remote Sensing (RS) and Geographic Information Systems (GIS) in conjunction with Universal Soil Loss Equation (USLE). For the assessment of the socioeconomic conditions, household survey along with other ancillary data can be used. These two aspects can be correlated for the better understanding of the degradation phenomena of the watershed. Under this context, the present study was carried out with the objectives (i) to analyze the locational sensitivity based on resources monitoring. (ii) to establish the relationship between resources monitoring and socioeconomic status and evaluate the applicability of RS and GIS for this purpose (iii) to allocate the suitable zones in the watershed for their specific use and management purpose.
2. The Study Area:
The study area, encompassing 732 sq. km., lies between 26o 42 and 26o 59 N latitude and 86o 33 46 and 86o 59 48 E longitude in the Eastern Region of Nepal (Map. 1). The altitude varies from 75 m to 1942 m. The tropical climate of the low-lying valley gradually passes into the sub-tropical towards higher elevation; north. The average annual temperature is 20oC (WEC, 1982) with 1942 mm rain fall. More than 70 percent rainfall is concentrated from may to October. The forest cover is nearly 58 percent area which is dominated by tropical Sal (Shorea robusta) forest, followed by 24 percent area of agriculture. Agriculture along with livestock are the important source of income and livelihood the of the people. Population per ha. Arable land comes to be 4.53 which is some what lowever as compared to the Tarai region of the Nepal.
Map 1. Location of the Study Area
3. Methodology:
An integrated approach of digital image processing of satellite data and visual interpretation of airphoto combined with GIS and USLE was carried out for resources assessment. Household questionnaire survey was conducted for the socioeconomic status assessment. The ancillary data were used where ever it was relevant.
3.1 Collection of Data and Assessing Resources:
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Socioeconomic survey: out of 18,000 household, 113 were interviewed with well structured questionnaire. The sample size was calculated as described by Cochran (1977).
- Physiographic survey: 35soil samples were taken for soil fertility and texture analysis.
- Satellite dataL Landsat TM of path/row 140/041, acquired on 21 Dec: 1991 and MSS of April 1984.
- Airphotos: Scale 1:50,000, Nov., 1978
- Reconnaissance survey maps: Topographic map: 1:25,000, 1995 and 1:63,360, 1958, Land use map: 1:50,000, 1982, Land system map: 1:50,000, 1984, and Political map: 1:250,000, 1987
By using these data, the general methodology was followed as presented in Fig. 1. The socioeconomic survey was done in three strata, which were described as Hills (High altitude), Midlands (Medium altitude) and Valley (Low latitude). Land use map of 1978 was obtained by using visual interpretation of airphoto, while surpervised digital image processing was adopted for 1984 and 1991 satellite data. After comparison of land use of 198, 1984 and 1991, the change trends had been obtained.
Fig 1 General Methodology
Widely adopted USLE model was taken to estimate the soil loss (Schawab et.al., 1993). The equation is written is written as
E=RKLSCP
Where,
E= Mean annual soil loss (tons/ha/yr.), R= Rainfall erosivity index, K= Soil erodibility, L = Slope length, S = Slope steepness, C= Crop management and vegetation cover, P = Erosion control practice factor,
From the average annual rainfall (1942 mm) and the maximum 30 minute intensity (100 mm) of the year 1991, the R value was calculated (Forster, 1981 and Morgan, 1986). The K factor for 20 soil units and the P factor for different land use were determined by using Schawab et al. (1993). The slope length for different land use were adopted from the DSCWM/HMG, Nepal 1992, while the slope inclination factor was determined by using the Digital Terrain Model (DTM) which was interpolated after digitizing ht 20 meter contour interval lines. The C value was adopted from suggested by Morgan, (1986). The layers of USLE model were created and integrated with the help of GIS (Fig. 2). The soil samples were analyzed for soil nutrients, texture and permeability at RARS, Tarahara, Sunsari, Nepal. As the socioeconomic study involves both qualities and quantitative information, both descriptive as well as analytical statistics measures had been used. Furthermore, weight age index were also formulated where ever essential.
Fig 2 USLE Model in conjuction with RS & GS to Estimate Soil Erosion
4. Result and Discussion:
4.1 Resource Monitoring:
Overall land cover change for the duration of 13 years between 1978 and 1991, is given in Table 1. For the management of natural resources, the land cover change should be less than 0.1 per cent per year (Murai 1993, citied)
in Pahari, 1993) on sustainability basis. The rate of forest degradation of study area was 0.57 per cent per year, and is too high for sustainable use of resources. Although the rate of soil erosion was the highest for shrubs land the contribution was maximum (54% during year 1991) from agriculture land. The temporal variation of soil erosion rate from different land use, may be attributed to the change in spatial location of land (Table 3). (Map 2 and 3)
Table 1: Land Use Change
| Land cover |
1978 % |
1984 % |
1991 % |
Time interval % change |
| 1978-84 |
1984-91 |
1978-91 |
| Shrubs |
2.27 |
4.08 |
3.45 |
1.81 |
-0.63 |
1.18 |
| Degraded forest |
4.19 |
12.49 |
8.90 |
8.30 |
-3.59 |
4.71 |
| Forest |
65.21 |
57.31 |
57.86 |
-7.90 |
0.55 |
-7.35 |
| Agriculture |
22.49 |
21.11 |
23.66 |
-1.38 |
2.55 |
1.17 |
| River |
5.84 |
5.00 |
6.14 |
-0.83 |
1.13 |
0.30 |
| Total |
100.0 |
100.0 |
100.0 |
0.00 |
0.00 |
0.00 |
Map 2. Soil Erosion Map Year 1991
Map 3. Land Use Map Year (Derived from Landsat TM, 1991)
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