Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China
Forest city (FC) usually refers to an urban area with high forest coverage. It is a green model of urban development that has been strongly advocated for by governments of many nations. Forest fire is a prominent threat in FC development, but the causes of fires in FCs are usually different and more...
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Online Access: | http://dx.doi.org/10.1080/19475705.2018.1505667 |
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doaj-45408838bb694c2dbedff4631af6c4a82020-11-25T01:11:11ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132018-01-01911207122910.1080/19475705.2018.15056671505667Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, ChinaZhangwen Su0Haiqing Hu1Guangyu Wang2Yuanfan Ma3Xiajie Yang4Futao Guo5Northeast Forestry UniversityNortheast Forestry UniversityUniversity of British ColumbiaFujian Agriculture and Forestry UniversityFujian Agriculture and Forestry UniversityFujian Agriculture and Forestry UniversityForest city (FC) usually refers to an urban area with high forest coverage. It is a green model of urban development that has been strongly advocated for by governments of many nations. Forest fire is a prominent threat in FC development, but the causes of fires in FCs are usually different and more complex than in pure forested areas since more socio-economic factors and human activity are involved in the ignition and spread of fire. The large and increasing number of lives being exposed to wildfire hazard highlights the need to understand the characteristics of these fires so that forest fire prediction and prevention can be efficient. In this study, Ripley's K(d) function and Random Forests (RF) were applied to analyze the drivers, spatial distribution and risk patterns of fires in Yichun, a typical FC in China. The results revealed a clustered distribution of forest fire ignitions in Yichun, as well as identified the driving factors and their dynamic influence on fire occurrence. Fire risk zones were identified based on RF modelling. Improved preventive measures can be implemented in the fire prone areas to reduce the risk of fire in Yichun by considering the factors identified in this study.http://dx.doi.org/10.1080/19475705.2018.1505667Driving factorsRandom Forestsspatial distributionRipley's K function |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhangwen Su Haiqing Hu Guangyu Wang Yuanfan Ma Xiajie Yang Futao Guo |
spellingShingle |
Zhangwen Su Haiqing Hu Guangyu Wang Yuanfan Ma Xiajie Yang Futao Guo Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China Geomatics, Natural Hazards & Risk Driving factors Random Forests spatial distribution Ripley's K function |
author_facet |
Zhangwen Su Haiqing Hu Guangyu Wang Yuanfan Ma Xiajie Yang Futao Guo |
author_sort |
Zhangwen Su |
title |
Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China |
title_short |
Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China |
title_full |
Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China |
title_fullStr |
Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China |
title_full_unstemmed |
Using GIS and Random Forests to identify fire drivers in a forest city, Yichun, China |
title_sort |
using gis and random forests to identify fire drivers in a forest city, yichun, china |
publisher |
Taylor & Francis Group |
series |
Geomatics, Natural Hazards & Risk |
issn |
1947-5705 1947-5713 |
publishDate |
2018-01-01 |
description |
Forest city (FC) usually refers to an urban area with high forest coverage. It is a green model of urban development that has been strongly advocated for by governments of many nations. Forest fire is a prominent threat in FC development, but the causes of fires in FCs are usually different and more complex than in pure forested areas since more socio-economic factors and human activity are involved in the ignition and spread of fire. The large and increasing number of lives being exposed to wildfire hazard highlights the need to understand the characteristics of these fires so that forest fire prediction and prevention can be efficient. In this study, Ripley's K(d) function and Random Forests (RF) were applied to analyze the drivers, spatial distribution and risk patterns of fires in Yichun, a typical FC in China. The results revealed a clustered distribution of forest fire ignitions in Yichun, as well as identified the driving factors and their dynamic influence on fire occurrence. Fire risk zones were identified based on RF modelling. Improved preventive measures can be implemented in the fire prone areas to reduce the risk of fire in Yichun by considering the factors identified in this study. |
topic |
Driving factors Random Forests spatial distribution Ripley's K function |
url |
http://dx.doi.org/10.1080/19475705.2018.1505667 |
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