Considerable economic losses and ecological damage can be caused by forest fi res,and compared to suppression,prevention is a much smarter strategy.Accordingly,this study focuses on developing a novel framework to ass...Considerable economic losses and ecological damage can be caused by forest fi res,and compared to suppression,prevention is a much smarter strategy.Accordingly,this study focuses on developing a novel framework to assess forest fi re risks and policy decisions on forest fi re management in China.This framework integrated deep learning algorithms,geographic information,and multisource data.Compared to conventional approaches,our framework featured timesaving,easy implementation,and importantly,the use of deep learning that vividly integrates various factors from the environment and human activities.Information on 96,594 forest fi re points from 2001 to 2019 was collected on Moderate Resolution Imaging Spectroradiometer(MODIS)fi re hotspots from 2001 to 2019 from NASA’s Fire Information Resource Management System.The information was classifi ed into factors such as topography,climate,vegetation,and society.The prediction of forest fi re risk was generated using a fully connected network model,and spatial autocorrelation used to analyze the spatial aggregation correlation of active fi re hotspots in the whole area of China.The results show that high accuracy prediction of fi re risks was achieved(accuracy 87.4%,positive predictive value 87.1%,sensitivity 88.9%,area under curve(AUC)94.1%).Based on this,it was found that Chinese forest fi re risk shows signifi cant autocorrelation and agglomeration both in seasons and regions.For example,forest fi re risk usually raises dramatically in spring and winter,and decreases in autumn and summer.Compared to the national average,Yunnan Province,Guangdong Province,and the Greater Hinggan Mountains region of Heilongjiang Province have higher fi re risks.In contrast,a large region in central China has been recognized as having a long-term,low risk of forest fi res.All forest risks in each region were recorded into the database and could contribute to the forest fi re prevention.The successful assessment of forest fi re risks in this study provides a comprehensive knowledge of fi re risks in China over the last 20 years.Deep learning showed its advantage in integrating multiple factors in predicting forest fi re risks.This technical framework is expected to be a feasible evaluation tool for the occurrence of forest fi res in China.展开更多
Turkey has a high potential for wildfires along its Mediterranean coast because of its dense forest cover and mild climate.An average of 250 wildfires occurs every year with more than 10,000 hectares destroyed due to ...Turkey has a high potential for wildfires along its Mediterranean coast because of its dense forest cover and mild climate.An average of 250 wildfires occurs every year with more than 10,000 hectares destroyed due to natural and human-related causes.The study area is sensitive to fires caused by lightning,stubble burning,discarded cigarette butts,electric arcing from power lines,deliberate fire setting,and traffic accidents.However,52%of causes could not be identified due to intense wildfires occurring at the same time and insufficient equipment and personnel.Since wildfires destroy forest cover,ecosystems,biodiversity,and habitats,they should be spatially evaluated by separating them according to their causes,considering environmental,climatic,topographic and forest structure variables that trigger wildfires.In this study,wildfires caused by lightning,the burning of agriculture stubble,discarded cigarette butts and power lines were investigated in the provinces of Aydin,Mugla and Antalya,where 22%of Turkey’s wildfires occurred.The MaxEnt method was used to determine the spatial distribution of wildfires to identify risk zones for each cause.Wildfires were used as the species distribution and the probability of their occurrence estimated.Additionally,since the causes of many wildfires are unknown,determining the causes is important for fire prediction and prevention.The highest wildfire occurrence risks were 9.7%for stubble burning,30.2%for lightning,4.5%for power lines and 16.9%by discarded cigarette butts.In total,1,266 of the 1,714 unknown wildfire causes were identified by the analysis of the cause-based risk zones and these were updated by including cause-as signed unknown wildfire locations for verification.As a result,the Area under the ROC Curve(AUC)values were increased for susceptibility maps.展开更多
For this study of long-term spatial patterns and trends of active fires in southern hemispheric Africa and on Madagascar from 2001 to 2020,active fire data from the MODIS FIRMS global fire data products were analyzed....For this study of long-term spatial patterns and trends of active fires in southern hemispheric Africa and on Madagascar from 2001 to 2020,active fire data from the MODIS FIRMS global fire data products were analyzed.The annual center of fire concentration tended to migrate toward the preserved rainforests and nature conservation areas in the Congo Basin and the mountain forests on the northeastern coast of Madagascar.Fire frequency varied seasonally at both study areas.We used geo statistical analysis techniques,such as measures of dispersion and emerging hot spot analysis,to reveal long-term trends in spatial patterns of fire events.In southern hemispheric Africa,the observed active fires tended to drift northward toward the Zambia-DRC border in the Congo basin.This northward migration progressed toward humid rainforests,which were better suited to sustaining repeated fire events.On Madagascar,the observed active fires tended to migrate toward the east coast in protected mountain forests.The spatial patterns of long-term trends showed a concentration of fires in the tropical regions of southern hemispheric Africa.Moreover,smaller clusters of new hot spots were located over eastern South Africa,overlapping with undifferentiated woodlands.On Madagascar,both hot and cold spots were identified and were separated by the highland region in the center of the island.Most of the eastern island was characterized by cold spots that received less precipitation than did the rest of the island.The presence of increasing hots spots in the densely vegetated areas highlights the urgent need for fire prevention and management in this region.展开更多
Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire.Existing residual fire detection equipment is mainly infrared...Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire.Existing residual fire detection equipment is mainly infrared temperature detection and smoke identification.Due to the isolation of ground,temperature and smoke characteristics of medium and large smoldering charcoal in some forest soils are not obvious,making it difficult to identify by detection equipment.CO gas is an important detection index for indoor smoldering fire detection,and an important identification feature of hidden smoldering ground fires.However,there is no research on locating smoldering fires through CO detection.We studied the diffusion law of CO gas directly above covered smoldering charcoal as a criterion to design a detection device equipped with multiple CO sensors.According to the motion decomposition search algorithm,the detection device realizes the function of automatically searching for smoldering charcoal.Experimental data shows that the average CO concentration over the covered smoldering charcoal decreases exponentially with increasing height.The size of the search step is related to the reliability of the search algorithm.The detection success corresponding to the small step length is high but the search time is lengthy which can lead to search failure.The introduction of step and rotation factors in search algorithm improves the search efficiency.This study reveals that the average ground CO concentration directly above smoldering charcoal in forests changes with height.Based on this law,a CO gas sensor detection device for hidden smoldering fires has been designed,which enriches the technique of residual fire detection.展开更多
基金funded by the Key R&D Projects in Hainan Province (ZDYF2021SHFZ256)Natural Science Foundation of Hainan University,grant numbers KYQD (ZR)21,115
文摘Considerable economic losses and ecological damage can be caused by forest fi res,and compared to suppression,prevention is a much smarter strategy.Accordingly,this study focuses on developing a novel framework to assess forest fi re risks and policy decisions on forest fi re management in China.This framework integrated deep learning algorithms,geographic information,and multisource data.Compared to conventional approaches,our framework featured timesaving,easy implementation,and importantly,the use of deep learning that vividly integrates various factors from the environment and human activities.Information on 96,594 forest fi re points from 2001 to 2019 was collected on Moderate Resolution Imaging Spectroradiometer(MODIS)fi re hotspots from 2001 to 2019 from NASA’s Fire Information Resource Management System.The information was classifi ed into factors such as topography,climate,vegetation,and society.The prediction of forest fi re risk was generated using a fully connected network model,and spatial autocorrelation used to analyze the spatial aggregation correlation of active fi re hotspots in the whole area of China.The results show that high accuracy prediction of fi re risks was achieved(accuracy 87.4%,positive predictive value 87.1%,sensitivity 88.9%,area under curve(AUC)94.1%).Based on this,it was found that Chinese forest fi re risk shows signifi cant autocorrelation and agglomeration both in seasons and regions.For example,forest fi re risk usually raises dramatically in spring and winter,and decreases in autumn and summer.Compared to the national average,Yunnan Province,Guangdong Province,and the Greater Hinggan Mountains region of Heilongjiang Province have higher fi re risks.In contrast,a large region in central China has been recognized as having a long-term,low risk of forest fi res.All forest risks in each region were recorded into the database and could contribute to the forest fi re prevention.The successful assessment of forest fi re risks in this study provides a comprehensive knowledge of fi re risks in China over the last 20 years.Deep learning showed its advantage in integrating multiple factors in predicting forest fi re risks.This technical framework is expected to be a feasible evaluation tool for the occurrence of forest fi res in China.
文摘Turkey has a high potential for wildfires along its Mediterranean coast because of its dense forest cover and mild climate.An average of 250 wildfires occurs every year with more than 10,000 hectares destroyed due to natural and human-related causes.The study area is sensitive to fires caused by lightning,stubble burning,discarded cigarette butts,electric arcing from power lines,deliberate fire setting,and traffic accidents.However,52%of causes could not be identified due to intense wildfires occurring at the same time and insufficient equipment and personnel.Since wildfires destroy forest cover,ecosystems,biodiversity,and habitats,they should be spatially evaluated by separating them according to their causes,considering environmental,climatic,topographic and forest structure variables that trigger wildfires.In this study,wildfires caused by lightning,the burning of agriculture stubble,discarded cigarette butts and power lines were investigated in the provinces of Aydin,Mugla and Antalya,where 22%of Turkey’s wildfires occurred.The MaxEnt method was used to determine the spatial distribution of wildfires to identify risk zones for each cause.Wildfires were used as the species distribution and the probability of their occurrence estimated.Additionally,since the causes of many wildfires are unknown,determining the causes is important for fire prediction and prevention.The highest wildfire occurrence risks were 9.7%for stubble burning,30.2%for lightning,4.5%for power lines and 16.9%by discarded cigarette butts.In total,1,266 of the 1,714 unknown wildfire causes were identified by the analysis of the cause-based risk zones and these were updated by including cause-as signed unknown wildfire locations for verification.As a result,the Area under the ROC Curve(AUC)values were increased for susceptibility maps.
文摘For this study of long-term spatial patterns and trends of active fires in southern hemispheric Africa and on Madagascar from 2001 to 2020,active fire data from the MODIS FIRMS global fire data products were analyzed.The annual center of fire concentration tended to migrate toward the preserved rainforests and nature conservation areas in the Congo Basin and the mountain forests on the northeastern coast of Madagascar.Fire frequency varied seasonally at both study areas.We used geo statistical analysis techniques,such as measures of dispersion and emerging hot spot analysis,to reveal long-term trends in spatial patterns of fire events.In southern hemispheric Africa,the observed active fires tended to drift northward toward the Zambia-DRC border in the Congo basin.This northward migration progressed toward humid rainforests,which were better suited to sustaining repeated fire events.On Madagascar,the observed active fires tended to migrate toward the east coast in protected mountain forests.The spatial patterns of long-term trends showed a concentration of fires in the tropical regions of southern hemispheric Africa.Moreover,smaller clusters of new hot spots were located over eastern South Africa,overlapping with undifferentiated woodlands.On Madagascar,both hot and cold spots were identified and were separated by the highland region in the center of the island.Most of the eastern island was characterized by cold spots that received less precipitation than did the rest of the island.The presence of increasing hots spots in the densely vegetated areas highlights the urgent need for fire prevention and management in this region.
基金funded by Natural Science Foundation of Heilongjiang Province(TD2020C001)National Forestry Science and Technology Promotion Project(2019[10])。
文摘Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire.Existing residual fire detection equipment is mainly infrared temperature detection and smoke identification.Due to the isolation of ground,temperature and smoke characteristics of medium and large smoldering charcoal in some forest soils are not obvious,making it difficult to identify by detection equipment.CO gas is an important detection index for indoor smoldering fire detection,and an important identification feature of hidden smoldering ground fires.However,there is no research on locating smoldering fires through CO detection.We studied the diffusion law of CO gas directly above covered smoldering charcoal as a criterion to design a detection device equipped with multiple CO sensors.According to the motion decomposition search algorithm,the detection device realizes the function of automatically searching for smoldering charcoal.Experimental data shows that the average CO concentration over the covered smoldering charcoal decreases exponentially with increasing height.The size of the search step is related to the reliability of the search algorithm.The detection success corresponding to the small step length is high but the search time is lengthy which can lead to search failure.The introduction of step and rotation factors in search algorithm improves the search efficiency.This study reveals that the average ground CO concentration directly above smoldering charcoal in forests changes with height.Based on this law,a CO gas sensor detection device for hidden smoldering fires has been designed,which enriches the technique of residual fire detection.
