Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

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This article uses three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) 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).

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Pu, Junwei; Zhao, Xiaoqing; Dong, Pinliang; Wang, Qian & Yue, Qifa June 26, 2021.

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This article is part of the collection entitled: UNT Scholarly Works and was provided by the UNT College of Liberal Arts & Social Sciences to the UNT Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 13 times. More information about this article can be viewed below.

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This article uses three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) 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).

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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.

This article belongs to the Special Issue Advances of Remote Sensing in Environmental Geoscience.

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  • Remote Sensing, 13(13), Multidisciplinary Digital Publishing Institute, June 26 2021, pp. 1-21

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  • Publication Title: Remote Sensing
  • Volume: 13
  • Issue: 13
  • Article Identifier: 2497
  • Peer Reviewed: Yes

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  • June 26, 2021

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  • Oct. 21, 2021, 11:26 a.m.

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  • Nov. 2, 2021, 9:29 a.m.

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Pu, Junwei; Zhao, Xiaoqing; Dong, Pinliang; Wang, Qian & Yue, Qifa. Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study, article, June 26, 2021; (https://digital.library.unt.edu/ark:/67531/metadc1852222/: accessed September 19, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT College of Liberal Arts & Social Sciences.

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