Forest structure, roads and soil moisture provide realistic predictions of fire spread in modern Swedish landscape

作者：Sara Sharon Jones, Maksym Matsala, Emily Delin, Narayanan Subramanian, Urban Nilsson, Emma Holmström, Igor Drobyshev · 发表于：Ecological Modelling · 年份：2024 · DOI：10.1016/j.ecolmodel.2024.110942 · 被引用次数：8 · 研究领域：Fire effects on ecosystems、Landslides and related hazards、Plant Water Relations and Carbon Dynamics

• Vegetation properties can be used to predict forest fire susceptibility in Sweden. • High Scots pine stock increases fire susceptibility; deciduous species’ decrease it. • Cellular automata model can realistically predict fire risks in Swedish forests. Recent increases in fire activity in Sweden call for the quantification of forest fire susceptibility, in order to develop management strategies to mitigate fire risk. Using the data from 100 large Swedish forest fires (>10 ha), mapped from sentinel-2 images from 2016 to 2022, we explored the predictive power of vegetation properties in estimating relative likelihood of fires within a landscape using logistic regression. To model spatially explicit fire susceptibility within a given landscape, we used the outcome of logistic regression as an input into a cellular automata model (CA model), which simulates fire spread in a 2D grid. The CA was model calibrated on three fires that occurred between 2016 and 2022, then verified on six 2023 fires and featured a mean sensitivity of 0.74 and specificity of 0.79. The logistic regression model had an accuracy of 54 %, showing increased fire susceptibility from high Scots pine volume ( p -value = 0.02), and decreased fire susceptibility from high volumes of deciduous trees and wet soil. Realistic outcomes of the CA model and reliance of our approach on publicly available data with nation-wide coverage of vegetation cover in Sweden allows for the development of an automated protocol of f...