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Open AccessArticle

Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau

1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255049, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Visiting professor at the School of Engineering and Applied Sciences, National University of Mongolia, Ulaanbaatar City 14201, Mongolia
5
University of Chinese Academy of Sciences, Beijing 100049, China
6
Department of Environment and Forest Engineering, National University of Mongolia, Ulaanbaatar City 210646, Mongolia
7
Department of Geography, National University of Mongolia, Ulaanbaatar City 14201, Mongolia
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1614; https://doi.org/10.3390/rs10101614
Received: 22 August 2018 / Revised: 29 September 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau. View Full-Text
Keywords: desertification; feature space; Albedo-NDVI; Albedo-MSAVI; Albedo-TGSI; Mongolia desertification; feature space; Albedo-NDVI; Albedo-MSAVI; Albedo-TGSI; Mongolia
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Wei, H.; Wang, J.; Cheng, K.; Li, G.; Ochir, A.; Davaasuren, D.; Chonokhuu, S. Desertification Information Extraction Based on Feature Space Combinations on the Mongolian Plateau. Remote Sens. 2018, 10, 1614.

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