Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm

Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2...
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Autor*in:	Wenyuan Ma [verfasserIn] Zhongke Feng [verfasserIn] Zhuxin Cheng [verfasserIn] Shilin Chen [verfasserIn] Fengge Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	forest fire driving factors forest fire occurrence random forest forest fire management China

Übergeordnetes Werk:	In: Forests - MDPI AG, 2010, 11(2020), 5, p 507
Übergeordnetes Werk:	volume:11 ; year:2020 ; number:5, p 507

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/f11050507

Katalog-ID:	DOAJ075820528

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520			\|a Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.
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allfields	10.3390/f11050507 doi (DE-627)DOAJ075820528 (DE-599)DOAJbe864297439843b680f010de2ee2a983 DE-627 ger DE-627 rakwb eng QK900-989 Wenyuan Ma verfasserin aut Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards. forest fire driving factors forest fire occurrence random forest forest fire management China Plant ecology Zhongke Feng verfasserin aut Zhuxin Cheng verfasserin aut Shilin Chen verfasserin aut Fengge Wang verfasserin aut In Forests MDPI AG, 2010 11(2020), 5, p 507 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:11 year:2020 number:5, p 507 https://doi.org/10.3390/f11050507 kostenfrei https://doaj.org/article/be864297439843b680f010de2ee2a983 kostenfrei https://www.mdpi.com/1999-4907/11/5/507 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 5, p 507
spelling	10.3390/f11050507 doi (DE-627)DOAJ075820528 (DE-599)DOAJbe864297439843b680f010de2ee2a983 DE-627 ger DE-627 rakwb eng QK900-989 Wenyuan Ma verfasserin aut Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards. forest fire driving factors forest fire occurrence random forest forest fire management China Plant ecology Zhongke Feng verfasserin aut Zhuxin Cheng verfasserin aut Shilin Chen verfasserin aut Fengge Wang verfasserin aut In Forests MDPI AG, 2010 11(2020), 5, p 507 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:11 year:2020 number:5, p 507 https://doi.org/10.3390/f11050507 kostenfrei https://doaj.org/article/be864297439843b680f010de2ee2a983 kostenfrei https://www.mdpi.com/1999-4907/11/5/507 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 5, p 507
allfields_unstemmed	10.3390/f11050507 doi (DE-627)DOAJ075820528 (DE-599)DOAJbe864297439843b680f010de2ee2a983 DE-627 ger DE-627 rakwb eng QK900-989 Wenyuan Ma verfasserin aut Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards. forest fire driving factors forest fire occurrence random forest forest fire management China Plant ecology Zhongke Feng verfasserin aut Zhuxin Cheng verfasserin aut Shilin Chen verfasserin aut Fengge Wang verfasserin aut In Forests MDPI AG, 2010 11(2020), 5, p 507 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:11 year:2020 number:5, p 507 https://doi.org/10.3390/f11050507 kostenfrei https://doaj.org/article/be864297439843b680f010de2ee2a983 kostenfrei https://www.mdpi.com/1999-4907/11/5/507 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 5, p 507
allfieldsGer	10.3390/f11050507 doi (DE-627)DOAJ075820528 (DE-599)DOAJbe864297439843b680f010de2ee2a983 DE-627 ger DE-627 rakwb eng QK900-989 Wenyuan Ma verfasserin aut Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards. forest fire driving factors forest fire occurrence random forest forest fire management China Plant ecology Zhongke Feng verfasserin aut Zhuxin Cheng verfasserin aut Shilin Chen verfasserin aut Fengge Wang verfasserin aut In Forests MDPI AG, 2010 11(2020), 5, p 507 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:11 year:2020 number:5, p 507 https://doi.org/10.3390/f11050507 kostenfrei https://doaj.org/article/be864297439843b680f010de2ee2a983 kostenfrei https://www.mdpi.com/1999-4907/11/5/507 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 5, p 507
allfieldsSound	10.3390/f11050507 doi (DE-627)DOAJ075820528 (DE-599)DOAJbe864297439843b680f010de2ee2a983 DE-627 ger DE-627 rakwb eng QK900-989 Wenyuan Ma verfasserin aut Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards. forest fire driving factors forest fire occurrence random forest forest fire management China Plant ecology Zhongke Feng verfasserin aut Zhuxin Cheng verfasserin aut Shilin Chen verfasserin aut Fengge Wang verfasserin aut In Forests MDPI AG, 2010 11(2020), 5, p 507 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:11 year:2020 number:5, p 507 https://doi.org/10.3390/f11050507 kostenfrei https://doaj.org/article/be864297439843b680f010de2ee2a983 kostenfrei https://www.mdpi.com/1999-4907/11/5/507 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 11 2020 5, p 507
language	English
source	In Forests 11(2020), 5, p 507 volume:11 year:2020 number:5, p 507
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authorswithroles_txt_mv	Wenyuan Ma @@aut@@ Zhongke Feng @@aut@@ Zhuxin Cheng @@aut@@ Shilin Chen @@aut@@ Fengge Wang @@aut@@
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callnumber-first	Q - Science
author	Wenyuan Ma
spellingShingle	Wenyuan Ma misc QK900-989 misc forest fire driving factors misc forest fire occurrence misc random forest misc forest fire management misc China misc Plant ecology Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
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topic_title	QK900-989 Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm forest fire driving factors forest fire occurrence random forest forest fire management China
topic	misc QK900-989 misc forest fire driving factors misc forest fire occurrence misc random forest misc forest fire management misc China misc Plant ecology
topic_unstemmed	misc QK900-989 misc forest fire driving factors misc forest fire occurrence misc random forest misc forest fire management misc China misc Plant ecology
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title	Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
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title_full	Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
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title_sort	identifying forest fire driving factors and related impacts in china using random forest algorithm
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title_auth	Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
abstract	Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.
abstractGer	Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.
abstract_unstemmed	Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management. We used the random forest model and MODIS Global Fire Atlas dataset (2010~2016) to analyse the impacts of climate, topographic, vegetation and socioeconomic variables on forest fire occurrence in six geographical regions in China. The results show clear regional differences in the forest fire driving factors and their impacts in China. Climate variables are the forest fire driving factors in all regions of China, vegetation variable is the forest fire driving factor in all other regions except the Northwest region and topographic variables and socioeconomic variables are only the driving factors of forest fires in a few regions (Northwest and Southwest regions). The model predictive capability is good: the AUC values are between 0.830 and 0.975, and the prediction accuracy is between 70.0% and 91.4%. High fire hazard areas are concentrated in the Northeast region, Southwest region and East China region. This research will aid in providing a national-scale understanding of forest fire driving factors and fire hazard distribution in China and help policymakers to design fire management strategies to reduce potential fire hazards.
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title_short	Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm
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