The Effect Of Land Use/Cover Change On Soil Erosion Change By Spatial Regression In Changwu County On The Loess Plateau In China

Changwu County is a typical soil and water loss area on the Loess Plateau. Soil erosion is an important ecological process, and the impact of land use/cover change on soil erosion has received much attention. The present study used remote sensing images of the study area in 1987, 1997, 2007, and 2017 to analyze the land use/cover change (LULCC), and the RUSLE model was applied to estimate the soil erosion in different times. We exploited the Sankey diagram to visualize the spatiotemporal changes in land use/cover and soil erosion. We planned to obtain the most suitable model by comparing the application of different spatial regression models (Geographically weighted regression model, Spatial lag model, Spatial error model) and Ordinary least squares in LULCC and soil erosion changes. The results revealed that land use/cover has significantly changed in the last 30 years. From 1987 to 1997, cropland expansion came mainly from planted land and orchards, which transformed 68.99 km(2) and 64.93 km(2), respectively. In 1997-2007, the planted land increase was mainly through the conversion of cropland. In 2007-2017, the increase in orchard area came mainly from cropland. The forest land increase was mainly from the planted land. Soil erosion in Changwu County was dominated by slight erosion and light erosion, although the area of slight erosion and light erosion continued to decrease. The annual average soil erosion increased, which was estimated at 977.84 ton km(-2) year(-1), 1305.17 ton km(-2) year(-1), 1310.60 ton km(-2) year(-1), and 1891.46 ton km(-2) year(-1) in 1987, 1997, 2007, and 2017, respectively. These amounts of transformation mainly occurred when slight erosion was converted to light erosion, light erosion was converted to moderate erosion, and moderate erosion was converted to light and severe erosion. The Spatial lag model and Spatial error model have higher accuracy than the Geographically weighted regression model and Ordinary least squares when fitting the effect of LULCC and soil erosion change, where the accuracy exceeded 0.62 in different periods.