Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China
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 oc...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/12/2999 |
| _version_ | 1827735841771880448 |
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| author | Chang Chang Yu Chang Zaiping Xiong Xiaoying Ping Heng Zhang Meng Guo Yuanman Hu |
| author_facet | Chang Chang Yu Chang Zaiping Xiong Xiaoying Ping Heng Zhang Meng Guo Yuanman Hu |
| author_sort | Chang Chang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:59:20Z |
| format | Article |
| id | doaj.art-d3e20ff9653a43bc9928212bf10a88db |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:59:20Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d3e20ff9653a43bc9928212bf10a88db2023-11-18T12:24:55ZengMDPI AGRemote Sensing2072-42922023-06-011512299910.3390/rs15122999Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, ChinaChang Chang0Yu Chang1Zaiping Xiong2Xiaoying Ping3Heng Zhang4Meng Guo5Yuanman Hu6CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaCAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaCAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaSchool of Public Administration, North China University of Water Resources and Electric Power, Zhengzhou 450045, ChinaCollege of Forestry, Inner Mongolia Agricultural University, Hohhot 010019, ChinaSchool of Geographical Sciences, Northeast Normal University, Changchun 130024, ChinaCAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, ChinaFires 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.https://www.mdpi.com/2072-4292/15/12/2999grassland firefire driversfire predictingvegetation indexmeteorological factorsInner Mongolia |
| spellingShingle | Chang Chang Yu Chang Zaiping Xiong Xiaoying Ping Heng Zhang Meng Guo Yuanman Hu Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China Remote Sensing grassland fire fire drivers fire predicting vegetation index meteorological factors Inner Mongolia |
| title | Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China |
| title_full | Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China |
| title_fullStr | Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China |
| title_full_unstemmed | Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China |
| title_short | Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China |
| title_sort | predicting grassland fire occurrence probability in inner mongolia autonomous region china |
| topic | grassland fire fire drivers fire predicting vegetation index meteorological factors Inner Mongolia |
| url | https://www.mdpi.com/2072-4292/15/12/2999 |
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