Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data
Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, w...
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| Veröffentlicht in: | Forests 2024-09, Vol.15 (9), p.1523 |
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| creator | Ji, Cuicui Yang, Hengcong Li, Xiaosong Pei, Xiangjun Li, Min Yuan, Hao Cao, Yiming Chen, Boyu Qu, Shiqian Zhang, Na Chun, Li Shi, Lingyi Sun, Fuyang |
| description | Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development. |
| doi_str_mv | 10.3390/f15091523 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_3153809433</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A810660095</galeid><sourcerecordid>A810660095</sourcerecordid><originalsourceid>FETCH-LOGICAL-c254t-be5a3235f87594bb26c3b4f1d70b93e5503b30d2b0267e1ef5a08d0bded631713</originalsourceid><addsrcrecordid>eNpdkVFP3DAMx6uJSUPHPewbRNrLeCg4ddM2j8exg0mgIWDbY5WmDuTWS1jSHoJPv6CbEMJ-sGX__pYtZ9lnDkeIEo4NFyC5KPBDts-llHkpod57k3_K5jGuIZmoG1mU-9nzygeKI_tth97YQOzaxj9sESPFuCE3Mm_Ywjnr7lJnS4H9UsNAT8w6dmP1_aQcuwp-a50mdqIi9cw7dhrs9kWxUnr0IbJHO96zy2kYbX7jp5DQUzWqg-yjUUOk-f84y36uvt0uz_OLH2ffl4uLXBeiHPOOhMIChWlqIcuuKyqNXWl4X0MnkYQA7BD6ooOiqomTEQqaHrqe-gp5zXGWfd3NfQj-75SObTc2ahoG5chPsUUusAFZIib0yzt0nfZ1abtEcRBFJRESdbSj7tRArXXGj0Hp5D1trPaOjE31RcOhqgCkSILDnUAHH2Mg0z4Eu1HhqeXQvnyuff0c_gMOGYmG</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3110526930</pqid></control><display><type>article</type><title>Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Ji, Cuicui ; Yang, Hengcong ; Li, Xiaosong ; Pei, Xiangjun ; Li, Min ; Yuan, Hao ; Cao, Yiming ; Chen, Boyu ; Qu, Shiqian ; Zhang, Na ; Chun, Li ; Shi, Lingyi ; Sun, Fuyang</creator><creatorcontrib>Ji, Cuicui ; Yang, Hengcong ; Li, Xiaosong ; Pei, Xiangjun ; Li, Min ; Yuan, Hao ; Cao, Yiming ; Chen, Boyu ; Qu, Shiqian ; Zhang, Na ; Chun, Li ; Shi, Lingyi ; Sun, Fuyang</creatorcontrib><description>Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development.</description><identifier>ISSN: 1999-4907</identifier><identifier>EISSN: 1999-4907</identifier><identifier>DOI: 10.3390/f15091523</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Analytic hierarchy process ; Biodiversity ; carbon ; Carbon content ; Carbon cycle ; China ; Climate change ; Decision making ; Decision trees ; Driving ability ; Ecological function ; Economic impact ; Ecosystems ; Elevation ; Environmental risk ; Fire prevention ; Flowers & plants ; Forest & brush fires ; Forest fires ; forests ; Geographic information systems ; Geospatial data ; Health aspects ; Health risks ; Hierarchies ; humans ; Humidity ; Land use ; Normalized difference vegetative index ; Photosynthesis ; Plant species ; Rain ; Rain and rainfall ; Rainfall ; Regression analysis ; risk ; Risk assessment ; river valleys ; Rivers ; Soil quality ; Soil temperature ; Soil types ; Soils ; species ; Sustainable development ; Temperature ; Topography ; Valleys ; Vegetation ; vegetation index ; Weather ; Weighting methods ; Wildfires ; Wind ; Wind speed</subject><ispartof>Forests, 2024-09, Vol.15 (9), p.1523</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c254t-be5a3235f87594bb26c3b4f1d70b93e5503b30d2b0267e1ef5a08d0bded631713</cites><orcidid>0009-0000-5640-5099</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Ji, Cuicui</creatorcontrib><creatorcontrib>Yang, Hengcong</creatorcontrib><creatorcontrib>Li, Xiaosong</creatorcontrib><creatorcontrib>Pei, Xiangjun</creatorcontrib><creatorcontrib>Li, Min</creatorcontrib><creatorcontrib>Yuan, Hao</creatorcontrib><creatorcontrib>Cao, Yiming</creatorcontrib><creatorcontrib>Chen, Boyu</creatorcontrib><creatorcontrib>Qu, Shiqian</creatorcontrib><creatorcontrib>Zhang, Na</creatorcontrib><creatorcontrib>Chun, Li</creatorcontrib><creatorcontrib>Shi, Lingyi</creatorcontrib><creatorcontrib>Sun, Fuyang</creatorcontrib><title>Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data</title><title>Forests</title><description>Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analytic hierarchy process</subject><subject>Biodiversity</subject><subject>carbon</subject><subject>Carbon content</subject><subject>Carbon cycle</subject><subject>China</subject><subject>Climate change</subject><subject>Decision making</subject><subject>Decision trees</subject><subject>Driving ability</subject><subject>Ecological function</subject><subject>Economic impact</subject><subject>Ecosystems</subject><subject>Elevation</subject><subject>Environmental risk</subject><subject>Fire prevention</subject><subject>Flowers & plants</subject><subject>Forest & brush fires</subject><subject>Forest fires</subject><subject>forests</subject><subject>Geographic information systems</subject><subject>Geospatial data</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Hierarchies</subject><subject>humans</subject><subject>Humidity</subject><subject>Land use</subject><subject>Normalized difference vegetative index</subject><subject>Photosynthesis</subject><subject>Plant species</subject><subject>Rain</subject><subject>Rain and rainfall</subject><subject>Rainfall</subject><subject>Regression analysis</subject><subject>risk</subject><subject>Risk assessment</subject><subject>river valleys</subject><subject>Rivers</subject><subject>Soil quality</subject><subject>Soil temperature</subject><subject>Soil types</subject><subject>Soils</subject><subject>species</subject><subject>Sustainable development</subject><subject>Temperature</subject><subject>Topography</subject><subject>Valleys</subject><subject>Vegetation</subject><subject>vegetation index</subject><subject>Weather</subject><subject>Weighting methods</subject><subject>Wildfires</subject><subject>Wind</subject><subject>Wind 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Boyu</creator><creator>Qu, Shiqian</creator><creator>Zhang, Na</creator><creator>Chun, Li</creator><creator>Shi, Lingyi</creator><creator>Sun, Fuyang</creator><general>MDPI 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methods</topic><topic>Wildfires</topic><topic>Wind</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ji, Cuicui</creatorcontrib><creatorcontrib>Yang, Hengcong</creatorcontrib><creatorcontrib>Li, Xiaosong</creatorcontrib><creatorcontrib>Pei, Xiangjun</creatorcontrib><creatorcontrib>Li, Min</creatorcontrib><creatorcontrib>Yuan, Hao</creatorcontrib><creatorcontrib>Cao, Yiming</creatorcontrib><creatorcontrib>Chen, Boyu</creatorcontrib><creatorcontrib>Qu, Shiqian</creatorcontrib><creatorcontrib>Zhang, Na</creatorcontrib><creatorcontrib>Chun, Li</creatorcontrib><creatorcontrib>Shi, Lingyi</creatorcontrib><creatorcontrib>Sun, Fuyang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest SciTech 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Shiqian</au><au>Zhang, Na</au><au>Chun, Li</au><au>Shi, Lingyi</au><au>Sun, Fuyang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data</atitle><jtitle>Forests</jtitle><date>2024-09-01</date><risdate>2024</risdate><volume>15</volume><issue>9</issue><spage>1523</spage><pages>1523-</pages><issn>1999-4907</issn><eissn>1999-4907</eissn><abstract>Forest fires can lead to a decline in ecosystem functions, such as biodiversity, soil quality, and carbon cycling, causing economic losses and health threats to human societies. Therefore, it is imperative to map forest-fire risk to mitigate the likelihood of forest-fire occurrence. In this study, we utilized the hierarchical analysis process (AHP), a comprehensive weighting method (CWM), and random forest to map the forest-fire risk in the Anning River Valley of Sichuan Province. We selected non-photosynthetic vegetation (NPV), photosynthetic vegetation (PV), normalized difference vegetation index (NDVI), plant species, land use, soil type, temperature, humidity, rainfall, wind speed, elevation, slope, aspect, distance to road, and distance to residential as forest-fire predisposing factors. We derived the following conclusions. (1) Overlaying historical fire points with mapped forest-fire risk revealed an accuracy that exceeded 86%, indicating the reliability of the results. (2) Forest fires in the Anning River Valley primarily occur in February, March, and April, typically months characterized by very low rainfall and dry conditions. (3) Areas with high and medium forest-fire risk were mainly distributed in Dechang and Xide counties, while low-risk areas were most prevalent in Xichang city and Mianning country. (4) Rainfall, temperature, elevation, and NPV emerged as the main influencing factors, exerting a dominant role in the occurrence of forest fires. Specifically, a higher NPV coverage correlates with an increased risk of forest fire. In conclusion, this study represents a novel approach by incorporating NPV and PV as key factors in triggering forest fires. By mapping forest-fire risk, we have provided a robust scientific foundation and decision-making support for effective fire management strategies. This research significantly contributes to advancing ecological civilization and fostering sustainable development.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/f15091523</doi><orcidid>https://orcid.org/0009-0000-5640-5099</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Analytic hierarchy process Biodiversity carbon Carbon content Carbon cycle China Climate change Decision making Decision trees Driving ability Ecological function Economic impact Ecosystems Elevation Environmental risk Fire prevention Flowers & plants Forest & brush fires Forest fires forests Geographic information systems Geospatial data Health aspects Health risks Hierarchies humans Humidity Land use Normalized difference vegetative index Photosynthesis Plant species Rain Rain and rainfall Rainfall Regression analysis risk Risk assessment river valleys Rivers Soil quality Soil temperature Soil types Soils species Sustainable development Temperature Topography Valleys Vegetation vegetation index Weather Weighting methods Wildfires Wind Wind speed |
| title | Forest Wildfire Risk Assessment of Anning River Valley in Sichuan Province Based on Driving Factors with Multi-Source Data |
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