Spatial-temporal pattern of desertification in the Selenge River Basin of Mongolia from 1990 to 2020

被引:9
作者
Xu, Shuxing [1 ,2 ]
Wang, Juanle [1 ,2 ,3 ]
Altansukh, Ochir [4 ]
Chuluun, Togtokh [5 ]
机构
[1] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
[2] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
[3] Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China
[4] Natl Univ Mongolia, Dept Environm & Forest Engn, Environm Engn Lab, Ulaanbaatar, Mongolia
[5] Natl Univ Mongolia, Inst Sustainable Dev, Ulaanbaatar, Mongolia
关键词
desertification; land degradation; feature space model; Selenge River Basin; Mongolia plateau; HORQIN SANDY LAND; CLIMATE-CHANGE; SOIL-MOISTURE; TIME-SERIES; NDVI; DEGRADATION; DYNAMICS; PLATEAU; WATER;
D O I
10.3389/fenvs.2023.1125583
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Land degradation is the most serious environmental challenge in the Mongolian Plateau, an important arid and semiarid region east of the Eurasian continent. The Selenge River Basin is not only the main catchment area of Baikal Lake, the largest fresh water lake, but also the main concentration area of agriculture and animal husbandry in Mongolia. Under the common influence of global warming and human activities, desertification has become more prominent in this basin, threatening the ecological security and sustainable development of the Mongolian Plateau. In this study, we selected NDVI, Modified Soil Adjusted Vegetation Index, topsoil grain size index and Albedo as feature space indicators, and retrieved the desertification process from 1990 to 2020 in the Selenge River Basin of Mongolia based on a novel feature space monitoring index. A 30-m resolution desertification map of the Selenge River Basin was retrieved based on optimal feature space models for 1990, 1995, 2000, 2005, 2010, 2015, and 2020. Then, the spatial-temporal dynamic changes and driving mechanism of desertification. The results show that: 1) Compared with the other four feature space models, the point-to-line Albedo-MSAVI feature space model has the highest recognition accuracy of 84.89% for desertification in the basin. 2) The desertification level of the Selenge River basin is mainly low and medium on the whole, the high desertification is mainly located in BULGAN and HOVSGOL provinces in the middle-upper reaches of the basin, and the severe desertification is mainly located in TOV province and Ulaanbaatar in the middle-lower reaches of the basin. 3) From 1990-2020, desertification degree in the Selenge River Basin has further deteriorated, and the area of high and serve desertified land has expanded significantly. Within the stage, 1990-2015 was a period of rapid increase in desertification. However, from 2015-2020, recovery takes the dominant position. The regions with high conversion frequency of desertification degree are mainly concentrated in the central and southeastern of the Selenge River basin. The joint effects of large fluctuations in temperature, overgrazing and population migration aggravate the desertification degree in this region. The research results can provide the desertification retrieving method recommendation and land degradation nutrition measures decision support in the Selenge River Basin and the whole Mongolian Plateau.
引用
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页数:14
相关论文
共 44 条
[1]   Spatial Monitoring of Desertification Extent in Western Iraq using Landsat Images and GIS [J].
Ajaj, Qayssar Mahmood ;
Pradhan, Biswajeet ;
Noori, Abbas Mohammed ;
Jebur, Mustafa Neamah .
LAND DEGRADATION & DEVELOPMENT, 2017, 28 (08) :2418-2431
[2]  
[Anonymous], 2017, Global Land Outlook, P8, DOI DOI 10.21324/DACD.415081
[3]   Spatiotemporal evolution of desertification based on integrated remote sensing indices in Duolun County, Inner Mongolia [J].
Bai Zongfan ;
Han Ling ;
Jiang Xuhai ;
Liu Ming ;
Li Liangzhi ;
Liu Huiqun ;
Lu Jiaxin .
ECOLOGICAL INFORMATICS, 2022, 70
[4]  
Buren G. W., 2011, STUDY DESERTIFICATIO
[5]   Land degradation neutrality: The science-policy interface from the UNCCD to national implementation [J].
Chasek, Pamela ;
Akhtar-Schuster, Mariam ;
Orr, Barron Joseph ;
Luise, Anna ;
Ratsimba, Harifidy Rakoto ;
Safriel, Uriel .
ENVIRONMENTAL SCIENCE & POLICY, 2019, 92 :182-190
[6]  
Collado AD, 2002, J ARID ENVIRON, V52, P121, DOI [10.1016/S0140-1963(01)90980-2, 10.1006/jare.2001.0980]
[7]  
Dawasuren, 2018, RES 25 YEARS LAND DE
[8]   Dynamic monitoring of aeolian desertification based on multiple indicators in Horqin Sandy Land, China [J].
Duan, Hanchen ;
Wang, Tao ;
Xue, Xian ;
Yan, Changzhen .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 650 :2374-2388
[9]   Trend analysis of MODIS NDVI time series for detecting land degradation and regeneration in Mongolia [J].
Eckert, Sandra ;
Huesler, Fabia ;
Liniger, Hanspeter ;
Hodel, Elias .
JOURNAL OF ARID ENVIRONMENTS, 2015, 113 :16-28
[10]   Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data [J].
Gong, Peng ;
Wang, Jie ;
Yu, Le ;
Zhao, Yongchao ;
Zhao, Yuanyuan ;
Liang, Lu ;
Niu, Zhenguo ;
Huang, Xiaomeng ;
Fu, Haohuan ;
Liu, Shuang ;
Li, Congcong ;
Li, Xueyan ;
Fu, Wei ;
Liu, Caixia ;
Xu, Yue ;
Wang, Xiaoyi ;
Cheng, Qu ;
Hu, Luanyun ;
Yao, Wenbo ;
Zhang, Han ;
Zhu, Peng ;
Zhao, Ziying ;
Zhang, Haiying ;
Zheng, Yaomin ;
Ji, Luyan ;
Zhang, Yawen ;
Chen, Han ;
Yan, An ;
Guo, Jianhong ;
Yu, Liang ;
Wang, Lei ;
Liu, Xiaojun ;
Shi, Tingting ;
Zhu, Menghua ;
Chen, Yanlei ;
Yang, Guangwen ;
Tang, Ping ;
Xu, Bing ;
Giri, Chandra ;
Clinton, Nicholas ;
Zhu, Zhiliang ;
Chen, Jin ;
Chen, Jun .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (07) :2607-2654