Abstract
Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.
Highlights
Karst areas cover approximately 22 million km2 and are an important part of Earth’s surface system, accounting for 15% of the Earth’s land area [1,2]
Rocky desertification is an extreme manifestation of land degradation in karst areas that results in vegetation deterioration, soil erosion, and bedrock exposure, under the dual action of natural processes and human interference [4,5]
This paper presents a comparative analysis of open-access satellite images for extracting information on rocky desertification, in terms of both data and methods, in a typical karst area in southwest China
Summary
Karst areas cover approximately 22 million km and are an important part of Earth’s surface system, accounting for 15% of the Earth’s land area [1,2]. Karst areas are ecologically fragile and susceptible to land degradation [3]. Rocky desertification is an extreme manifestation of land degradation in karst areas that results in vegetation deterioration, soil erosion, and bedrock exposure, under the dual action of natural processes and human interference [4,5]. Accurate mapping of rocky desertification is essential for controlling land degradation in karst areas. While the geomorphological/hydrological characteristics, geological causes, and development processes of karst areas have been well investigated, studies on rocky desertification are relatively new [4,6,7,8,9,10]. Information on the degree and spatial distribution of rocky desertification is essential for creating a baseline for sustainable land management in karst areas.
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