Evolution of forest fires in Portugal: from spatio-temporal point events to smoothed density maps

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• 作者：Marj Tonini ; Mário Gonzalez Pereira ; Joana Parente ; Carmen Vega Orozco
• 关键词：Forest fires ; Portugal ; Spatio ; temporal statistics ; Ripley’s K ; function ; 3D ; Kernel density
• 刊名：Natural Hazards
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：85
• 期：3
• 页码：1489-1510
• 全文大小：
• 刊物类别：Earth and Environmental Science
• 刊物主题：Natural Hazards; Hydrogeology; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Civil Engineering; Environmental Management;
• 出版者：Springer Netherlands
• ISSN：1573-0840
• 卷排序：85

文摘

The spatial and temporal distribution of forest fires displays a complex pattern which strongly influences the forest landscape and the neighbouring anthropogenic development. Statistical methods developed for spatio-temporal stochastic point processes can be employed to find a structure, detect over-densities and trends in forest fire risk and address towards prevention and forecasting measures. The present study considers the Portuguese mapped burnt areas official geodatabase resulting from interpreted satellite measurements, covering the period 1990–2013. The main goal is to detect whether space and time act independently or whether, conversely, neighbouring events are also closer in time, interacting to generate clusters. To this purpose, the following statistical methods were applied: (1) the geographically weighted summary statistics, to explore how the average burned area vary locally through the investigated region; (2) the bivariate K-function, to test the space–time interaction and the spatial attraction/independency between fires of different size; and (3) the space–time kernel density, allowing elaborating smoothed density surfaces and representing over-densities of large versus medium versus small fires and on north versus south region. The proposed approach successfully allowed finding and mapping spatio-temporal patterns within this large data series. Specifically, medium fires tend to aggregate around small fires, while large fires aggregate at a larger distance and longer times, indicating that the return time following these events is longer than for small and medium fires. The density maps shows that hot spots are present almost each year in the northern region, with a higher concentration in the northern areas, while the southern half of the country counts lower surface densities of fires, which are mainly concentrated in the central period (2000–2007).