{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T20:41:44Z","timestamp":1770324104202,"version":"3.49.0"},"reference-count":90,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T00:00:00Z","timestamp":1573516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19040500"],"award-info":[{"award-number":["XDA19040500"]}]},{"name":"the Key Project for Field Station Alliance, Chinese Academy of Sciences","award":["KFJ-SW-YW026"],"award-info":[{"award-number":["KFJ-SW-YW026"]}]},{"name":"the Program Founding from IGA","award":["Y6H2091001"],"award-info":[{"award-number":["Y6H2091001"]}]},{"name":"the funding from Youth Innovation Promotion Association Chinese Academy of Sciences","award":["2017277, 2012178"],"award-info":[{"award-number":["2017277, 2012178"]}]},{"name":"the funding from Jilin Scientific and Technological Development Program","award":["20170301001NY"],"award-info":[{"award-number":["20170301001NY"]}]},{"name":"the funding from Jilin Scientific and Technological Development Program","award":["20170301001NY"],"award-info":[{"award-number":["20170301001NY"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Grassland coverage, aboveground net primary production (ANPP), and species composition are used as indicators of grassland degradation. However, soil salinization deficiency, which is also a factor of grassland degradation, is rarely used in grassland degradation assessment in semiarid regions. We assessed grassland degradation by its quality, quantity, and spatial pattern over semiarid west Jilin, China. Considering soil salinization in west Jilin, electrical conductivity (EC) is used as an index with ANPP to assess grassland degradation. First, the spatial distribution of the grassland was measured with information mined from multi-temporal remote sensing images using an object-based image analysis combined with classification and decision tree methods. Second, with 166 field samples, we utilized the random forest (RF) algorithm as the variable selection and regression method for predicting EC and ANPP. Finally, we created a new grassland degradation model (GDM) based on ANPP and EC. The results showed the R2 (0.91) and RMSE (0.057 mS\/cm) of the EC model were generally highest and lowest when the ntree was 400; the ANPP model was optimal (R2 = 0.85 and RMSE = 15.81 gC\/m2) when the ntree was 600. Grassland area of west Jilin was 609.67 \u00d7 103 ha in 2017, there were 373.79 \u00d7 103 ha of degraded grassland, with 210.47 \u00d7 103 ha being intensively degraded. This paper surpasses past limitations of excessive reliance on vegetation index to construct a grassland degradation model which considers the characteristics of the study area and soil salinity. The results confirm the positive influence of the ecological conservation projects sponsored by the government. The research outcome could offer supporting data for decision making to help alleviate grassland degradation and promote the rehabilitation of grassland vegetation.<\/jats:p>","DOI":"10.3390\/ijgi8110511","type":"journal-article","created":{"date-parts":[[2019,11,12]],"date-time":"2019-11-12T04:07:07Z","timestamp":1573531627000},"page":"511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Using Landsat OLI and Random Forest to Assess Grassland Degradation with Aboveground Net Primary Production and Electrical Conductivity Data"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4043-2097","authenticated-orcid":false,"given":"Hao","family":"Yu","sequence":"first","affiliation":[{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"},{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1298-4839","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunying","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,12]]},"reference":[{"key":"ref_1","first-page":"491","article-title":"Spatial distribution of grassland biomass in China","volume":"28","author":"Piao","year":"2004","journal-title":"Acta Phyto Sin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1046\/j.1365-2486.2002.00512.x","article-title":"Estimating net primary productivity from grassland biomass dynamics measurements","volume":"8","author":"Scurlock","year":"2002","journal-title":"Glob. 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