New Applications of GIS and Remote Sensing to Monitor and Predict Fire Hazard Risk and Effects

Dear Colleagues,

The development of new geospatial technologies and innovative advanced data analysis is leading to significant progress in the monitoring, mapping, and prediction of fire hazard, risk, and effects. New GIS and remote sensing technologies such as unmanned aerial vehicles (UAV), aerial, terrestrial or satellite LIDAR, new satellite and/or radar sensors, and cloud-based imagery processing tools (e.g., Google Earth Engine) are being applied to monitor and characterize fuel hazard, fuel moisture, fire behavior, burned area, burn severity, and fire effects (e.g., fire emissions, fuel consumption, tree mortality and regeneration). Improved predictions of fire occurrence, spread, intensity, and risk are being developed by integrating GIS and remote sensing information, using innovative statistical and simulation approaches. This Special Issue that will focus on the development of innovative applications of GIS, spatiotemporal modeling, and remote sensing to monitor and predict fire hazard, behavior, risk, and effects. Contributions are welcome on the following topics:

1. Monitoring of Fire Hazard, Behavior, Risk, and Effects with GIS and Remote Sensing:

- New approaches for fuel and hazard mapping with new technologies (e.g., aerial, terrestrial or satellite LIDAR, UAV, satellite or radar);

- Advances in fuel moisture monitoring with remote sensing for improved fire danger prediction;

- Monitoring of fire behavior (e.g., FRP/intensity, rate of spread) and/or fire effects (e.g., fuel consumption, smoke) with new technologies (e.g., UAV, LIDAR, active fires, satellite imagery), including multisensor approaches;

- Methodological advances for burned area, burn severity, forest mortality and regeneration monitoring with new technologies (UAV, LIDAR, satellite, radar), including the use of cloud-based imagery processing tools (e.g., Google Earth Engine).

2. Prediction of Fire Hazard, Behavior, Risk, and Effects with GIS and Remote Sensing:

- Prediction of fire behavior (e.g., fire spread, size, intensity, crown fire susceptibility) and fuel hazard and risk mapping at a landscape scale using GIS and remote sensing, including fire behavior simulation approaches, for optimizing fuel treatments and mapping values at risk;

- Advances in the use of modern data science, machine learning (ML) and artificial intelligence techniques to predict spatial and temporal variations in wildland fire occurrence and intensity;

- Modeling and mapping of spatial fire hazard and risk from human factors (e.g., wildland-urban interface, agricultural interface), together with fuel, topography, and climate;

- Analysis of land cover and fuel effects, including effects of previous burns on subsequent fire frequency, intensity, and size;

- Prediction of spatiotemporal fire danger and risk from daily fuel moisture, weather, topography, and human factors using GIS and remote sensing technologies;

- Prediction of fire intensity and severity from weather, climate, fuel, and topography;

- Prediction of fire effects, including fuel consumption, emissions, mortality, and regeneration;

- Modeling the effect of climate change and associated vegetation changes on fire occurrence and fire regimes, including fire frequency, seasonality, fire size, intensity, and severity.

Prof. Dr. Daniel J. Vega-Nieva
Prof. Dr. Ernesto Alvarado
Dr. William Matthew Jolly
Dr. Adrián Jiménez-Ruano
Prof. Dr. Pablito Marcelo López-Serrano
Guest Editors

Dr. Carlos Ivan Briones-Herrera
Co-Guest Editor

Manuscript Submission Information

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