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• Index, texture, and spectral features can improve the identification accuracy of RDSL segmentation model. • The SegGCN model can effectively assess the RDSL in karst regions using RS imagery. • Different driving factors of RD will have varying degrees of impact on RDSLs. • The phenomenon of RD in Guizhou province is gradually being alleviated from 2015 to 2022. Land rocky desertification (RD) is one of the most serious environmental disasters in karst landforms. Identifying the rocky desertification severity level (RDSL) is a key task in the prevention and control projects of rocky desertification in karst areas. How to efficiently and accurately identify the RDSL is an urgent issue. It requires higher accuracy and more advanced techniques. Currently, machine learning-based remote sensing technology (RST) faces challenges in identifying the RDSL, including insufficient dataset features, low accuracy of identification models, and incomplete exploration of rocky desertification driving factors. To address these issues, this study leverages multi-source remote sensing satellite data and related product data to construct a multidimensional dataset with feature factors. By combining convolutional neural networks (CNN) and graph neural networks (GNN), a graph convolutional network segmentation model based on deep learning image segmentation is proposed for the automatic identification of RDSL. In addition, the study has investigated the spatiotemporal changes of RD in Guizhou Province in recent years and explored the impacts of various natural driving factors on the RDSLs. The experimental results indicate that the multidimensional feature dataset (Dataset-2) contributes to enhancing the identification accuracy of the model. The proposed model has capabilities such as composite representation in non-Euclidean space, deep extraction of image semantics, and multiscale segmentation and fusion. The model achieves an Mean Intersection over Union (MIoU) of 84.724, which outperforms other mainstream image segmentation methods. Although rocky desertification from 2015 to 2022 in Guizhou Province is significantly distributed, there is a trend toward mitigation. This study provides effective technical tools and data support for exploring the evolution process of desertification in subtropical karst areas, as well as for the implementation of projects related to environmental protection, afforestation, soil and water conservation, and land monitoring.
Wang et al. (Sat,) studied this question.