Assessment of Interventions in Fuel Management Zones Using Remote Sensing

Abstract

Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.

Keywords

• remote sensing
• time series
• Sentinel-2
• Sentinel-1
• Fuel Management Zones
• machine learning