{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T07:31:10Z","timestamp":1761291070219,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T00:00:00Z","timestamp":1547769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFB0503603"],"award-info":[{"award-number":["2017YFB0503603"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41631179,41171332, 41771443"],"award-info":[{"award-number":["41631179,41171332, 41771443"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012336","name":"Major State Basic Research Development Program of China","doi-asserted-by":"publisher","award":["2015CB954101"],"award-info":[{"award-number":["2015CB954101"]}],"id":[{"id":"10.13039\/501100012336","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Surveying and Mapping Geoinformation Nonprofit Specific Project","award":["201512033"],"award-info":[{"award-number":["201512033"]}]},{"name":"National Science Technology Basic Work Special Project","award":["2011FY110400-2"],"award-info":[{"award-number":["2011FY110400-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as \u2018permafrost probable\u2019 (p &gt; 0.7), \u2018permafrost possible\u2019 (0.7 \u2265 p \u2265 0.3) and \u2018permafrost improbable\u2019 (p &lt; 0.3). From the 1990s to the 2040s, the \u2018permafrost probable\u2019 type mainly degrades to that of \u2018permafrost possible\u2019, with the total area degenerating from 73.5 \u00d7 103 km2 to 66.5 \u00d7 103 km2. The \u2018permafrost possible\u2019 type mainly degrades to that of \u2018permafrost impossible\u2019, with a degradation area of 6.5 \u00d7 103 km2, which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions.<\/jats:p>","DOI":"10.3390\/rs11020183","type":"journal-article","created":{"date-parts":[[2019,1,18]],"date-time":"2019-01-18T11:26:55Z","timestamp":1547810815000},"page":"183","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Model Simulation and Prediction of Decadal Mountain Permafrost Distribution Based on Remote Sensing Data in the Qilian Mountains from the 1990s to the 2040s"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5197-0192","authenticated-orcid":false,"given":"Shangmin","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Surveying and Mapping, College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}]},{"given":"Shifang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Surveying and Mapping, College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1580-4979","authenticated-orcid":false,"given":"Weiming","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Chenghu","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,18]]},"reference":[{"key":"ref_1","unstructured":"Muller, S.W. 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