پديد آورندگان :
رضايي ، پيمان نويسنده rezaee, peyman , فريدي، پروانه نويسنده كارشناس ارشد رسوبشناسي و سنگشناسي رسوبي، دانشكده علوم پايه، دانشگاه هرمزگان , , قرباني، منصور نويسنده دانشكده علوم زمين- دانشگاه شهيد بهشتي تهران , , كاظمي، محمد نويسنده ,
چكيده لاتين :
Extended Abstract
Introduction
Soil erosion is considered as a major environmental problem since it seriously threatens natural resources, agriculture and the environment. Spatial and quantitative information on soil erosion on a watershed scale contributes significantly to the planning for soil conservation, erosion control, and management of the watershed environment. The new version of the USLE model, called the Revised Universal Soil Loss Equation (RUSLE), is developed by modifying the USLE to more accurately estimate various factors of soil loss equation and soil erosion losses. The use of remote sensing (RS) and geographical information system (GIS) techniques makes soil erosion estimation and its spatial distribution feasible with reasonable costs and better accuracy in larger areas. The goal of this research is to estimate the soil using RUSLE model and the identification of its most important factor in the study area.
Methodology
The Gabric watershed is the study area located in the southeast part of Hormozgan Province in the southern Iran. The elevations at the highest and lowest points are 2190 and 37 m above mean sea level, respectively. The RUSLE method was used in this study in a GIS framework to estimate soil erosion in the Gabric watershed. The factors of RUSLE represent the effect of topography, soil, precipitation, land cover, and support practices on soil erosion. The factors used in RUSLE were produced or obtained from the meteorological station, soil surveys, topographic maps, and satellite images.
In this study, in order to estimate the R factor, the annual and monthly rainfalls are obtained from the records of twenty-two stations for eleven years. Then, with use of the equations, Fournier index is estimated for all the stations. In order to calculate the K-factor, data from 21 soil samples collected in the study area. After performing relevant laboratory tasks, this factor was calculated for each point using Shirazi’s and Boersma’s formula. Soil texture was acquired from the soil texture’s new triangle. LS factor was computed from the DEM with the ArcGIS Spatial Analyst extension. The first of topographic maps in the scale of 1:250,000 digitized in Arc map environment of subset GIS, then was extracted DEM 50 meters. Moore and Burch were used to calculate the combined LS factor for the watershed. The NDVI resulted from the Landsat ETM+ satellite image (path 152 and row 42) on 19 January 2010 in ENVI4.8 software. The NDVI is used in order to construct the C factor image of the study area in GIS environment. Since there are no conservation practices in the study area, the P was set to 1. After completing data input procedure and preparation of R, K, C, and LS maps as data layers, they were multiplied in ArcGIS10.0 environment to draw up the erosion risk map showing the spatial distribution of soil loss in the study area.
After providing the required layers regression was done. In this model of regression the annual soil loss was taken into account as the dependent variable and the rainfall erosivity, soil erodibility, topographic and plant coverage factors were considered as independent variables.
Results and Discussion
R values of each station were calculated. Then the input maps of R factor of the whole watershed were interpolated using an ordinary kriging interpolation through GIS. R factor varies from 35 to 683 MJ mm ha-1 h-1 y-1. There is more rainfall erosivity in the northwest of the watershed that coincides with higher elevation of study area. The soil erodibility map derived using a Simple kriging interpolation method. K factor values ranged from 0.012 to 0.0158
t ha h ha-1 MJ-1 mm-1. The majority of the areas have little erodibility due to the sandy loam and loamy sand textures and the southwest part of the location, has the most erodibility compared to the rest of the areas, due to its loamy texture. The topographic factor (or LS factor) is varies from 1 to 2052 in pixel level. The highest variability in elevation, the steepest slopes and, as a consequence, the greatest LS values. The C factor map is prepared based on NDVI. This factor has completely inverse relationship with NDVI. The C factor varies from 0.65 to 0.78; which indicates a poor area regarding the plant coverage. As there is no erosion control practice in the region, P factor values are assumed as 1.0 for the study area. The average annual soil erosion in the Gabric watershed was determined by computing the four RUSLE factor values in ArcGIS10.0. Annual soil loss ranged from 0.0033 to 32699 t h-1 y-1 at the pixel level, with mean value of 322.9 t h-1 y-1. The SD parameter is 1278.89 t h-1 y-1 in the study area. Based on the estimated annual soil loss rates, the study area was classified into five erosion risk classes. More than half of the study area ( > 80%) was estimated to have very low to low erosion risk. Only 3.25% of the study area was estimated to suffer from high and severe erosion risks which are mostly found in north and south parts of the area.
Statistical correlations and regression relationships between RUSLE factors and the annual soil loss value for the study area are provided. It is clear that the strongest correlation is between the LS factor and annual soil loss value (R2=0.87) while the correlations between remaining factors of RUSLE and annual soil loss are found to be very low.
Conclusion
Soil erosion has an increasing trend especially in the developing countries. Intensification erosion causes agricultural soil loss, poor soil productive capacity and natural water pollution as a result of sedimentation. In this study applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing (RS) technique, and geographic information system (GIS) to map the soil erosion risk in the Gabric Watershed. All the maps of R, K, LS, C, and P are integrated to generate erosion risk map to find out spatial distribution of soil loss within GIS environment in the study area. The soil erosion risk map showed that, the soil loss varies from 0.0033 to 32699
t h-1 y-1 in the watershed. According to the soil erosion risk map prepared, areas with the moderate to severe erosion risks are located mainly in the rugged areas. Our study indicated that the LS factor of the RUSLE model (R2=0.87) were the most effective factors controlling soil erosion in the region. The study indicated that using RS and GIS technologies concurrently with precipitation data resulted to an effective and accurate assessment of soil erosion in considerable short time and low cost for large watersheds.
