4.7 Article

Human Activity Affects Forest Fires: The Impact of Anthropogenic Factors on the Density of Forest Fires in Poland

期刊

FORESTS
卷 12, 期 6, 页码 -

出版社

MDPI
DOI: 10.3390/f12060728

关键词

forest fires; anthropopressure; socioeconomic factors; spatial non-stationarity; geographically weighted regression; Poland

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资金

  1. NATIONAL SCIENCE CENTER [2019/35/N/ST10/00279]

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This study aimed to quantify the impact of anthropogenic factors on forest fires using Poland as an example. It found that variables such as the length of border between forests and built-up areas and road density significantly influenced forest fire density. Additionally, the impact of most analyzed variables varied across the study area, with geographically weighted regression models showing better fit compared to global models.
Forest fires, due to climate change, are a growing threat to human life, health, and property, especially in temperate climates. Unfortunately, the impact of individual factors on forest fires varies, depending on the geographical region and its natural and socio-economic conditions. The latter are rarely introduced into fire warning systems, which significantly reduces their effectiveness. Therefore, the main goal of this study was to quantify the impact of a wide range of anthropogenic factors on forest fires, using Poland as a representative example of a Central European country. Data were analyzed in relation to districts for the period 2007-2017, using correlation analysis and regression modeling applying global and local/mixed regression methods. It was found that almost all of the 28 variables taken for analysis significantly determined the density of forest fires, but the greatest role was played by the length of the border between forests and built-up areas, and road density. In addition, the impact of most of the analyzed variables on forest fires varied over the study area, so implementing non-stationarity in geographically weighted regression models significantly improved the goodness-of-fit compared to global models.

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