Application of Geo-Information Technology in the Study of Soil and Nutrients Loss by Water Erosion for three Different Land Cover

Abstract

This research integrated the Revised Universal Soil Loss Equation (RUSLE) and the soil statistical analysis with RS and GIS techniques to quantify soil and nutrient loss risk. The Letianxi watershed, located in the southwest part of Hubei Province of China was taken as a case. A system was established for rating soil erodibility, slope length/gradient, rainfall erosivity and conservation practices. The rating values serve as inputs into a Revised Universal Soil Loss Equation (RUSLE) to calculate the risk for soil degradation processes, namely soil water erosion. Two Landsat TM senses in 1997 and ETM 2002, respectively, were used to produce land use/cover maps of the study area based on the maximum likelihood classification method. These maps were then used to generate the conservation practice factor in the RUSLE. In order to assess the effect of land cover and landscape position on soil nutrient consisting of Soil Organic Mater (SOM), Total N (TN), Total P (TP) and Total K (TK), soil samples were collected from the top to foot of hill slope in the study area. The three categories consisted of a typical land cover structure for each polygon in the study area: Soil under forest cover (T1), soil under agriculture crops cover (T2) and soil under no vegetation cover (T3). Annual loss of total N (8.24 kg ha^‾1), total P (5.88 kg ha^‾1) and total K (6.98 kg ha^‾1), per unit loss of soil (t ha^‾1), was maximum from the soil under no vegetation cover (T3). The loss of total N ranged between 5.30 and 32.27 kg ha^‾1, total P ranged between 2.14 and 12.42 kg ha^‾1, total K ranged from 2.12 to 10.31 kg ha^-1 whereas organic matter loss varied between 10.65 and 236.16 kg ha^-1, from three different land covers. ERmapper and Arc/Info software were used to manage and manipulate thematic data, to process satellite images and tabular data source. Results showed that 110.72 km2 (27.09%) was exposed to very slight soil loss and 227.01 km² (55.55%) was exposed to slight soil loss. This study demonstrates the effectiveness of Geo-information technology in generating a soil and nutrient loss map.