Desertification Information Extraction Along the China-Mongolia Railway Supported by Multisource Feature Space and Geographical Zoning Modeling

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 13; pp. 392 - 402
• Main Authors: Wei, Haishuo, Wang, Juanle, Han, Baomin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Albedo; Biological system modeling; Classification; Climate change; Desertification; Earth; Economics; Environmental risk; Feature extraction; Foundation construction; Global warming; Grain size; Grasslands; Indexes; Information processing; Information retrieval; Land cover; Land use; Modelling; Monitoring; Normalized difference vegetative index; Plant cover; Rail transportation; Railroads; Railway construction; Regions; Remote sensing; Soil; Soil measurements; Topsoil; Vegetation; Vegetation cover; Vegetation index; Vegetation mapping; Zoning; Mongolia; China
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2019.2962830

The China-Mongolia railway is the core foundation for the construction of traffic connection in the China-Mongolia-Russia economic corridor. Long-term desertification has brought significant ecological risks to the railway area. Owing to the large variety of vegetation cover in this region, desertification information is easily confused with other weak vegetation cover information. This article proposes a refined desertification information extraction method based on multisource feature spaces and geographical zoning modeling. First, based on the geographical zoning, land cover, and vegetation coverage data for Mongolia, the railway area is divided into the Central provinces and their northern region, the Eastern Mongolian Plateau, and the Southern Gobi region. According to the vegetation coverage characteristics and the applicability of various feature space models to different geographical regions, Albedo-normalized difference vegetation index, Albedo-modified soil adjusted vegetation index, and Albedo-topsoil grain size index feature space models were constructed for three geographical regions. Faced with new challenges presented by global warming and the impact of monsoons on the classification and extraction of desertification information, we established a desertification classification system with six levels (severe desertification, high desertification, medium desertification, low desertification, withered grassland, and nondesertification) and complete desertification information extraction. The results show that the overall classification accuracy of the method selected in this article is 85.21%. We further analyzed the mechanism of this method, compared it with previous studies, and thereby proved that this method is feasible to extract the fine information of desertification in large areas and complex geographical environments.