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Karst environmental issues have become one of the hot spots in contemporary international geological research. The same problem of water shortage is one of the hot spots of global concern. The peak-cluster depression basins in southwest of Guangxi is an important water connotation and ecological barrier areas in the Pearl River Basin of China. Thus, studying the spatial and temporal variations and the influencing factors of its water yield services is critical to achieve the sustainable development of water resources and ecological environmental protection in this region. As such, this paper uses the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to assess the spatial and temporal variabilities of water yield services and its trends in the peak-cluster depression basins in southwest of Guangxi from 2000 to 2020. This work also integrates precipitation (Pre), reference evapotranspiration (ET), temperature (Tem), digital elevation model (DEM), slope, normalized difference vegetation index (NDVI), land use/land cover (LULC) and soil type to reveal the main factors that influence water yield services with the help of Geodetector. Results show that: 1) in time scale, the total annual water yield in the study area show a fluctuating and increasing trend from 2000 to 2020, with a growth rate of 7.3753 × 108 m3/yr, and its multi-year average water yield was 538.07 mm; 2) in spatial pattern, with high yield areas mainly distributed in the south of the study area (mainly including Shangsi County, Pingxiang City, Ningming County, Longzhou County and Jingxi County), and low yield areas mainly distributed in Baise City and Nanning City; 3) the dominant factor of water yield within karst and non-karst landforms is not necessarily controlled by precipitation, and the explanation degree of DEM factors in karst areas is significantly higher than that in non-karst areas; 4) amongst the climatic factors, Pre, ET and Tem are dominant in the spatial pattern of region water yield capacity. among which Pre has the highest explanatory power for the spatial heterogeneity of annual water production, with q values above 0.8, and each driver showed a significant interaction on the spatial distribution of water yield, with Pre exhibiting the strongest interaction with LULC. [ABSTRACT FROM AUTHOR]

Karst rocky desertification (KRD) has become one of the most serious ecological and environmental problems in karst areas. At present, mapping KRD with a high accuracy and on a large scale is still a difficult problem in the control of KRD. In this study, a random forest (RF) based on maximum information coefficient and correlation coefficient feature selection is proposed to predict KRD. Nine predictors stood out as feature factors to estimate KRD. Rock exposure was the most important predictor, followed by fractional vegetation cover for the prediction of KRD processes. The kappa and classification accuracy indexes were to evaluate the performance of the model. We recorded overall accuracy rate and kappa index values of 94.7% and 0.92 for the testing datasets respectively. The RF model was then used to predict the KRD in 2001, 2011, 2016, and 2020, and it was found that the KRD in the study area has exhibited a positive trend of improvement. Therefore, the use of multisource remote sensing data combined with the RF model can obtain better prediction results of KRD, thereby providing a new idea for large-scale estimation of the KRD in peak-cluster depression. [ABSTRACT FROM AUTHOR]

The optimal allocation of educational resources has been a hot issue, and exploring the accessibility of educational facilities in poor mountainous areas helps to reasonably plan the layout of educational facilities and promote the balanced development of education. Taking the rocky desertification area in Yunnan, Guangxi, and Guizhou (YGGRD) as the study area, based on the POI data of educational facilities in the YGGRD in 2000, 2010 and 2019, this study explored the evolution of the accessibility of educational facilities in the YGGRD through raster accessibility. And the influencing factors were analyzed by the ordinary least square method (OLS) and geographically weighted regression model (GWR), and evaluated the model through cross validation. The results show that the overall accessibility of educational facilities improved significantly from 2000 to 2019. Educational facilities mainly have good accessibility and average accessibility. Poor accessibility areas are concentrated in the interprovincial border regions, and the boundary effect is significant. County accessibility, population density and rural per capita disposable income have a great impact on the accessibility of educational facilities in the YGGRD. It is suggested to strengthen the construction of educational facilities in the interprovincial border regions, relocate and integrate villages, and improve the education quality of township schools to improve the supply of rural educational resources. [ABSTRACT FROM AUTHOR]