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Forests 2017, 8(6), 180; doi:10.3390/f8060180

Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China

1
College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Box 49, SE-230 52 Alnarp, Sweden
3
Faculty of Forestry, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Christopher J. Fettig and Timothy A. Martin
Received: 17 March 2017 / Revised: 15 May 2017 / Accepted: 20 May 2017 / Published: 24 May 2017
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Abstract

Fires in urban-forest ecosystems (UFEs) are frequent with complex causes, posing a serious hazard to human lives and infrastructure. Thus, quantifying wildfire risks in UFEs and their spatial pattern is quintessential to develop appropriate fire management strategies. The aim of this study was to explore spatial (geographically weighted logistic regression, GWLR) versus non-spatial (logistic regression, LR) modelling approaches to determine the relationship between forest fire occurrence and driving factors in Yichun, a typical urban-forest ecosystem in China. As drivers of fire, 13 factors related to topographic, vegetation, infrastructure, meteorological and socio-economy were considered and regressed against fire occurrence data from 1980 to 2010. Results demonstrate the superiority of GWLR models over LR in terms of prediction accuracy, goodness of fit and model residuals. The GWLR model further captured the spatial variability of driving factors over a broad study area, and the fire likelihood maps identified areas with different zones of fire risk in the study area. In conclusion, the study demonstrates quantitatively and spatially the importance of accounting for local variation in drivers of fires, thereby improving fire management and prevention strategies. The findings also contribute to the emerged field of fire management and fire risk assessment in UFEs. View Full-Text
Keywords: spatial heterogeneity; geographically weighted logistic regression; fire risk; wildfire management spatial heterogeneity; geographically weighted logistic regression; fire risk; wildfire management
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guo, F.; Su, Z.; Tigabu, M.; Yang, X.; Lin, F.; Liang, H.; Wang, G. Spatial Modelling of Fire Drivers in Urban-Forest Ecosystems in China. Forests 2017, 8, 180.

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