Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in th...Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.展开更多
Vegetation cover is the main factor of soil loss prevention.The C-factor of the RUSLE(Revised Universal Soil Loss Equation) was predicted with NDVI,ground data and exponential regression equation for mountain rangelan...Vegetation cover is the main factor of soil loss prevention.The C-factor of the RUSLE(Revised Universal Soil Loss Equation) was predicted with NDVI,ground data and exponential regression equation for mountain rangelands of Kyrgyzstan.Time series of C-factor,precipitation and temperature were decomposed into seasonal and trend components with STL(seasonal decomposition by loess) to assess their interrelations.C-factor,precipitation and temperature trend components indicated significant lagged correlation,whereas seasonal components indicated more complex relations with climate factors which can be promoting as well as limiting factors for vegetation development,depending on the season.Rainy springs and hot summers may increase soil loss dramatically,whereas warm and dry springs with rainy summers can decrease it.Steep slopes indicated higher soil loss ratio,whereas flat areas were better protected by vegetation.展开更多
基金a part of a joint Kyrgyz-German research project “The Impact of the Transformation Process on Human-Environment Interactions in Southern Kyrgyzstan”, funded by the Volkswagen Foundation, Germany, which had no impact on research or result dissemination
文摘Soil erosion in mountain rangelands in Kyrgyzstan is an emerging problem due to vegetation loss caused by overgrazing. It is further exacerbated by mountain terrain and high precipitation values in Fergana range in the south of Kyrgyzstan. The main objective of this study was to map soil erodibility in the mountainous rangelands of Kyrgyzstan. The results of this effort are expected to contribute to the development of soil erodibility modelling approaches for mountainous areas. In this study, we mapped soil erodibility at two sites, both representing grazing rangelands in the mountains of Kyrgyzstan and having potentially different levels of grazing pressure. We collected a total of 232 soil samples evenly distributed in geographical space and feature space. Then we analyzed the samples in laboratory for grain size distribution and calculated soil erodibility values from these data using the Revised Universal Soil Loss Equation (RUSLE) K-factor formula. After that, we derived different terrain indices and ratios of frequency bands from ASTER GDEM and LANDSAT images to use as auxiliary data because they are among the main soil forming factors and widely used for prediction of various soil properties. Soil erodibility was significantly correlated with channel network base level (geographically extrapolated altitude of water channels), remotely sensed indices of short-wave infrared spectral bands, exposition, and slope degree. We applied multiple regression analysis to predict soil erodibility from spatially explicit terrain and remotely sensed indices. The final soil erodibility model was developed using the spatially explicit predictors and the regression equation and then improved by adding the residuals. The spatial resolution of the model was 30 m, and the estimated mean adjusted coefficient of determination was 0.47. The two sites indicated different estimated and predicted means of soil erodibility values (0.035 and 0.039) with a 0.05 significance level, which is attributed mainly to the considerable difference in elevation.
基金project “The Impact of the Transformation Process on Human-Environment Interactions in Southern Kyrgyzstan”funded by the Volkswagen Foundation,Hannover,Germany
文摘Vegetation cover is the main factor of soil loss prevention.The C-factor of the RUSLE(Revised Universal Soil Loss Equation) was predicted with NDVI,ground data and exponential regression equation for mountain rangelands of Kyrgyzstan.Time series of C-factor,precipitation and temperature were decomposed into seasonal and trend components with STL(seasonal decomposition by loess) to assess their interrelations.C-factor,precipitation and temperature trend components indicated significant lagged correlation,whereas seasonal components indicated more complex relations with climate factors which can be promoting as well as limiting factors for vegetation development,depending on the season.Rainy springs and hot summers may increase soil loss dramatically,whereas warm and dry springs with rainy summers can decrease it.Steep slopes indicated higher soil loss ratio,whereas flat areas were better protected by vegetation.
