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

Haishuo Wei,Davaadorj Davaasuren,Kai Cheng,Sonomdagva Chonokhuu,Altansukh Ochir,Juanle Wang,Ge Li

doi:10.3390/rs10101614

Abstract

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.

Highlights

Desertification is a serious global environmental problem
By constructing the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, this study, with a high resolution (30 m), has obtained the results regarding the extraction of desertification information of Northwestern Mongolia, analyzed the mechanism of three feature space models, and compared these with previous studies on desertification information extraction
This study has proven that it is feasible to extract desertification information using the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI

Summary

Introduction

Desertification is a serious global environmental problem. Under the influence of natural environmental change and the anthropogenic causes of grassland degradation, ecological and environmental deterioration, as well as desertification have become more severe in Mongolia [1]. In 2007, more than 72% of Mongolia’s land was affected by desertification, with the range of desertification still expanding [3]. In 2017, the most current data from the Ministry of Natural Environment and Tourism of Mongolia indicated that 76.8% of the country’s land suffered varying degrees of desertification with desertification continuing to spread at a rapid rate, affecting the country’s renowned grasslands, including those in Dornod and Khentii provinces [4]. The increasing desertification problem on the Mongolian Plateau will have a strong effect on local sustainable development and may become the biggest obstacle to the transboundary cooperation in this area, for example, the China-Mongolia-Russia economic corridor issued by the governments of these three countries [5]

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Results

Discussion

Conclusion