中文摘要：

以RS、GIS和RUSLE模型为主要技术,选取典型的土壤侵蚀区福建省长汀县河田盆地区为研究区,通过对模型因子的合理选择,估算了该地区1988年、1998年和2010年的土壤侵蚀量,实现土壤侵蚀状况的定量评价和动态监测。结果表明：在1988年至2010年期间,研究区土壤侵蚀状况得到明显改善,平均土壤侵蚀模数由4259.11 t.km-.2a-1下降为1280.09 t.km-.2a-1,年侵蚀量由252.42万t下降至75.87万t;中度及其以上侵蚀面积由176 km2减少至62.69 km2,微度侵蚀面积由225.85km2增加至358.9 km2。研究结果说明近22年来针对长汀河田盆地区土壤侵蚀的治理所采取的措施是卓有成效的。长汀河田盆地区水土流失进一步重点治理的区域应集中在盆地中心及其西北部等地区的高程低于400 m、植被覆盖度为20%—50%的地区。

英文摘要：

The Revised Universal Soil Loss Equation （RUSLE）, supported by remote sensing and geographical information system techniques, was used to quantitatively estimate the soil erosion amount of the Hetian basin area of County Chanting in Fujian province, southeastern China. The Hefian area is a typical reddish soil erosion area in South China, and thus is selected as a study area. We evaluated the soil erosion intensity of the area in the years of 1988, 1998 and 2010, respectively, using the RUSLE but with the factors more suitable for the study area. The RUSLE is expressed as A = R · K ·LS ·C · P, where A is the annual mean soil erosion modulus, R is the rainfall erosivity factor, K is the soil erodibility factor, LS is the combined slope length and slope steepness factor, C is the vegetation cover management factor, and P is the soil and water conservation support practice factor. The R factor was calculated using the monthly rainfall of 1988, 1998, and 2010, and the results were 332.12, 296.40, and 345.14, respectively. According to the soil type map and the K values assigned to the corresponding soil types, we acquired the K grid surface. For the LS factor, we obtained it from a 30m digital elevation model （DEM） of the study area and created the LS grid surface using the ArcGIS. To obtain the C surface, we firstly calculated the vegetation fraction cover from three Landsat TM images of the study years, and thenevaluated the C value. The P factor was determined based on land use types. Similar to the K factor, we assigned different P values to the corresponding land use types, and obtained the P surface for the three study years. Besides, all the factors were converted from English units to metric units, and to GRID format in the same coordinate system of WGS84. By overlaying all the six RUSLE factors described above, we generated the resultant map of estimated soil loss amount for the study area in each study year. The field verification indicated that the accuracy of the estimation could be as high a