Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data
Mar Bisquert A C , Eduardo Caselles A , Juan Manuel Sánchez B and Vicente Caselles AA Earth Physics and Thermodynamics Department, University of Valencia, E-46100 Burjassot, Valencia, Spain.
B Applied Physics Department, University of Castilla-La Mancha, E-13400 Almadén, Spain.
C Corresponding author. Email: maria.mar.bisquert@uv.es
International Journal of Wildland Fire 21(8) 1025-1029 https://doi.org/10.1071/WF11105
Submitted: 29 July 2011 Accepted: 31 May 2012 Published: 30 July 2012
Abstract
Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and fire history data. Remote sensing inputs used were the land surface temperature and the Enhanced Vegetation Index. A classification into three levels of fire danger was established. Fire danger maps based on this classification will facilitate fire prevention and extinction tasks.
Additional keywords: EVI, LST, remote sensing.
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