Using Remote Sensing to Monitor Forest Fire Behavior and Forest Landscape

Dear Colleagues,

Fire behavior is a product of fuel properties, weather conditions and topography during the fire advance. The new generation of fires we are facing requires a deeper knowledge about where, and under which conditions, fires occur, focusing on fuel properties and climate hazards (e.g., drought, heat waves). Remote sensing systems (i.e., LiDAR and satellite images) have proven to be highly accurate in estimating critical fuel continuities and moisture, helping to identify areas with high potential for extreme wildfire events. Additionally, fuel data derived from remote sensing systems, when included in fire behavior simulators, can predict fire spread and intensity adequately. On the other hand, UAV-borne NIR and thermal IR cameras have collected consistent time series of fire rate of spread (RoS) and direction in complex fires, which has improved knowledge about fire–environment interactions. In addition, historical fire scars and fire severity maps derived from remote sensing systems are needed to estimate the accuracy of fire behavior simulators. Automated methods based on change detection algorithms, artificial intelligence and deep learning approaches have been widely used for fire mapping. The integration of optical remotely sensed imagery and LIDAR data provides improved opportunities to understand fire behavior.

This Special Issue of Forests is focused on experimental studies, monitoring and modeling approaches on fire behavior, including characterizing pre-fire vegetation conditions, assessing fire severities and evaluate the post-fire recovery. Research articles may focus on any aspect of remote sensing techniques and/or products applied to modeling fire behavior and methods for fuel characterization and monitoring.

Dr. Olga Viedma
Prof. Dr. Chunying Ren
Guest Editors

Manuscript Submission Information

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