{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T19:37:39Z","timestamp":1764704259011,"version":"build-2065373602"},"reference-count":92,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T00:00:00Z","timestamp":1686182400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China Strategic International Cooperation in Science and Technology Innovation Program","award":["2018YFE0207800","31971483","2022YFC3003101"],"award-info":[{"award-number":["2018YFE0207800","31971483","2022YFC3003101"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018YFE0207800","31971483","2022YFC3003101"],"award-info":[{"award-number":["2018YFE0207800","31971483","2022YFC3003101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2018YFE0207800","31971483","2022YFC3003101"],"award-info":[{"award-number":["2018YFE0207800","31971483","2022YFC3003101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.<\/jats:p>","DOI":"10.3390\/rs15122999","type":"journal-article","created":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T01:32:34Z","timestamp":1686274354000},"page":"2999","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4419-4882","authenticated-orcid":false,"given":"Chang","family":"Chang","sequence":"first","affiliation":[{"name":"CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2348-8806","authenticated-orcid":false,"given":"Yu","family":"Chang","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"}]},{"given":"Zaiping","family":"Xiong","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"}]},{"given":"Xiaoying","family":"Ping","sequence":"additional","affiliation":[{"name":"School of Public Administration, North China University of Water Resources and Electric Power, Zhengzhou 450045, China"}]},{"given":"Heng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5856-4018","authenticated-orcid":false,"given":"Meng","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Northeast Normal University, Changchun 130024, China"}]},{"given":"Yuanman","family":"Hu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China"},{"name":"E\u2019erguna Wetland Ecosystem National Research Station, Hulunbuir 022250, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Steiner, J.L., Wetter, J., Robertson, S., Teet, S., Wang, J., Wu, X., Zhou, Y., Brown, D., and Xiao, X. 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