{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T14:00:26Z","timestamp":1770904826474,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T00:00:00Z","timestamp":1576454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41271230"],"award-info":[{"award-number":["41271230"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41961060"],"award-info":[{"award-number":["41961060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Middle-aged Academic and Technology Leaders of Yunnan Province","award":["2008PY056"],"award-info":[{"award-number":["2008PY056"]}]},{"name":"Graduate Research Innovation Foundation of Yunnan Normal University","award":["2017058"],"award-info":[{"award-number":["2017058"]}]},{"name":"Graduate Research Innovation Foundation of Yunnan Normal University","award":["yjs2018109"],"award-info":[{"award-number":["yjs2018109"]}]},{"name":"Erasmus+ Capacity Building in Higher Education of the Education, Audio visual and Culture Executive Agency (EACEA) for the \u201cInnovation on Remote Sensing Education and Learning\u201d","award":["586037-EPP-1-2017-1-HU-EPPKA2-CBHE-JP"],"award-info":[{"award-number":["586037-EPP-1-2017-1-HU-EPPKA2-CBHE-JP"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Grassland resources are important land resources. However, grassland degradation has become evident in recent years, which has reduced the function of soil and water conservation and restricted the development of animal husbandry. Timely and accurate monitoring of grassland changes and understanding the degree of degradation are the foundation for the scientific use of grasslands. The grassland degradation index of ground comprehensive evaluation (grassland degradation index, GDIg) is a digital expression of grassland growth that can accurately indicate the degradation of grasslands. In this research, the accuracy of GDIg in evaluating grassland degradation is discussed; the typical areas of grassland degradation in Shangri-La City, i.e., the towns of Jiantang and Xiaozhongdian, are selected as the research area. Through a field survey and spectroscopy combined with Huanjing-1 (HJ-1) satellite image data, grassland degradation was monitored in the study area from 2008 to 2017. The results show that: (1) GDIg based on six indicators, namely, above-ground biomass, cover level, height, biomass of edible herbage, biomass of toxic weeds, and species richness, can effectively indicate grassland degradation, with the accuracy of the degradation grade assessment reaching 98.6%. (2) The correlation between the GDIg and the grey values of 4 wavebands and 7 types of vegetation indexes derived from the HJ-1 is analysed, and the degraded grassland inversion model was built and revised based on HJ-1 data. The grassland degradation evaluation index of remote sensing (GDIrs) model indicates that grassland degradation is proportional to the ratio vegetation index (RVI). (3) The grassland area was 405.40 km2 in the initial monitoring period, accounting for 17.26% of the study area, while at the end of the monitoring period, the area was 338.87 km2, with a loss of 66.53 km2. From 2008 to 2017, the area of non-degraded and slightly degraded grassland in the study area presented a downward trend, with decreases of 59.87 km2 and 49.93 km2, respectively. In contrast, the area of moderately degraded grassland increased by 41.17 km2 from 91.58 km2 in 2008 to 132.74 km2 in 2017, accounting for 39.17% of the grassland. The area of severely degraded grassland was 78.32 km2, accounting for 23.11% of the grassland in 2017. (4) The degraded grasslands in the study area mainly transformed into the degradation-enhanced (deterioration) type. As the transformation rate gradually slows down, the current situation of grassland degradation is not hopeful.<\/jats:p>","DOI":"10.3390\/rs11243030","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T02:59:01Z","timestamp":1576551541000},"page":"3030","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Remote-Sensing Monitoring of Grassland Degradation Based on the GDI in Shangri-La, China"],"prefix":"10.3390","volume":"11","author":[{"given":"Yanlin","family":"Yang","sequence":"first","affiliation":[{"name":"College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7202-646X","authenticated-orcid":false,"given":"Jinliang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1712-4555","authenticated-orcid":false,"given":"Yun","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Feng","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9968-688X","authenticated-orcid":false,"given":"Guangjie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Zenghong","family":"He","sequence":"additional","affiliation":[{"name":"College of Tourism and Geographic Sciences, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Remote Sensing Research Laboratory, Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3867","DOI":"10.1080\/01431161.2012.762696","article-title":"MODIS-based remote-sensing monitoring of the spatiotemporal patterns of China\u2019s grassland vegetation growth","volume":"34","author":"Xu","year":"2013","journal-title":"Int. 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