Next Article in Journal
A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors
Next Article in Special Issue
A New Method for Automated Measurement of Sand Dune Migration Based on Multi-Temporal LiDAR-Derived Digital Elevation Models
Previous Article in Journal
Parts-per-Object Count in Agricultural Images: Solving Phenotyping Problems via a Single Deep Neural Network
Previous Article in Special Issue
Analysis of Spatial and Temporal Changes and Expansion Patterns in Mainland Chinese Urban Land between 1995 and 2015
Article

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

1
School of Earth Sciences, Yunnan University, Kunming 650500, China
2
Institute of International Rivers & Eco-Security, Yunnan University, Kunming 650500, China
3
Department of Geography and the Environment, University of North Texas, Denton, TX 76203, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Lenio Soares Galvao
Remote Sens. 2021, 13(13), 2497; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132497
Received: 7 June 2021 / Revised: 21 June 2021 / Accepted: 22 June 2021 / Published: 26 June 2021
(This article belongs to the Special Issue Advances of Remote Sensing in Environmental Geoscience)
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. View Full-Text
Keywords: rocky desertification; open-access satellite image; information extraction; machine-learning algorithms; southwest China rocky desertification; open-access satellite image; information extraction; machine-learning algorithms; southwest China
Show Figures

Graphical abstract

MDPI and ACS Style

Pu, J.; Zhao, X.; Dong, P.; Wang, Q.; Yue, Q. Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study. Remote Sens. 2021, 13, 2497. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132497

AMA Style

Pu J, Zhao X, Dong P, Wang Q, Yue Q. Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study. Remote Sensing. 2021; 13(13):2497. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132497

Chicago/Turabian Style

Pu, Junwei, Xiaoqing Zhao, Pinliang Dong, Qian Wang, and Qifa Yue. 2021. "Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study" Remote Sensing 13, no. 13: 2497. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132497

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop