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.

Keywords

spatial heterogeneity; geographically weighted logistic regression; fire risk; wildfire management

Published in

Forests
2017, Volume: 8, number: 6, article number: 180
Publisher: MDPI AG

SLU Authors

• Tigabu, Mulualem

  • Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences
    

Sustainable Development Goals

SDG15 Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss
SDG11 Make cities and human settlements inclusive, safe, resilient and sustainable


UKÄ Subject classification

Forest Science


Publication identifier

DOI: https://doi.org/10.3390/f8060180

Permanent link to this page (URI)

https://res.slu.se/id/publ/92521