Yunlong Cai

Karst rocky desertification (KRD) is a type of land degradation especially prominent in southwest China. This article analyzes the anthropogenic driving forces of KRD at two scales: rural locality and its macro socio‐economic circumstances. At the rural locality scale, the intensive human pressure on land because of a large and fast growing population and unsustainable land use are identified to be the reason for KRD. However, more radical driving forces lie in the farmers' disadvantages in social‐economic circumstances, which compel them to overuse rural land. Hukou system, coastal development strategy, and household responsibility system are verified as three important factors in social‐economic circumstances. At last, a two‐scale framework is constructed to explain how anthropogenic driving forces lead to KRD in southwest China. Copyright © 2013 John Wiley & Sons, Ltd.

Knowing the spatial relationships between the normalized difference vegetation index (NDVI) and environmental variables is of great importance for monitoring rocky desertification. This article investigated the spatially non-stationary relationships between NDVI and environmental factors using geographically weighted regression (GWR) at multi-scales. The spatial scale-dependency of the relationships between NDVI and environmental factors was identified by scaling the bandwidth of the GWR model, and the appropriate bandwidth of the GWR model for each variable was determined. All GWR models represented significant improvements of model performance over their corresponding ordinary least squares (OLS) models. GWR models also successfully reduced the spatial autocorrelations of residuals. The spatial relationships between NDVI and environmental factors significantly varied over space, and clear spatial patterns of slope parameters and local coefficient of determination (R ²) were found from the results of the GWR models. The study revealed detailed site information on the different roles of related factors in different parts of the study area, and thus improved the model ability to explain the local situation of NDVI.