TY  - JOUR
PY  - 2023//
TI  - Forest fire driving factors and fire risk zoning based on an optimal parameter logistic regression model: a case study of the Liangshan Yi Autonomous Prefecture, China
JO  - Fire (Basel, Switzerland)
A1  - Zhang, Fuhuan
A1  - Zhang, Bin
A1  - Luo, Jun
A1  - Liu, Hui
A1  - Deng, Qingchun
A1  - Wang, Lei
A1  - Zuo, Ziquan
SP  - e336
EP  - e336
VL  - 6
IS  - 9
N2  - Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes an optimal parameter logistic regression (OPLR) model, conducts forest fire risk zoning research under the optimal spatial analysis scale and model parameters, and establishes a forest fire risk prediction model. The results showed that the spatial unit of the optimal spatial analysis scale in the study area was 5 km and that the prediction accuracy of the OPLR was about 81%. The climate was the main driving factor of forest fires, while temperature had the greatest influence on the probability of forest fires. According to the forest fire prediction model, mapping the fire risk zoning, in which the medium- and high-risk area was 6021.13 km2, accounted for 9.99% of the study area. The results contribute to a better understanding of forest fire management based on the local environmental characteristics of the Liangshan Yi Autonomous Prefecture and provide a reference for related forest fire prevention and control management.<p />  <p>Language: en</p>
LA  - en
SN  - 2571-6255
UR  - http://dx.doi.org/10.3390/fire6090336
ID  - ref1
ER  -