Abstract
Southern European countries, particularly Spain, are greatly affected by forest fires each year. Quantification of burned area is essential to assess wildfire consequences (both ecological and socioeconomic) and to support decision making in land management. Our study proposed a new synergetic approach based on hotspots and reflectance data to map burned areas from remote sensing data in Mediterranean countries. It was based on a widely used species distribution modeling algorithm, in particular the Maximum Entropy (MaxEnt) one-class classifier. Additionally, MaxEnt identifies variables with the highest contribution to the final model. MaxEnt was trained with hyperspectral indexes (from Earth-Observing One (EO-1) Hyperion data) and hotspot information (from Visible Infrared Imaging Radiometer Suite Near Real-Time 375 m active fire product). Official fire perimeter measurements by Global Positioning System acted as a ground reference. A highly accurate burned area estimation (overall accuracy = 0.99%) was obtained, and the indexes which most contributed to identifying burned areas included Simple Ratio (SR), Red Edge Normalized Difference Vegetation Index (NDVI750), Normalized Difference Water Index (NDWI), Plant Senescence Reflectance Index (PSRI), and Normalized Burn Ratio (NBR). We concluded that the presented methodology enables accurate burned area mapping in Mediterranean ecosystems and may easily be automated and generalized to other ecosystems and satellite sensors.
Highlights
Wildfires are natural disturbances in many ecosystems [1], Mediterranean ones
We present a new synergetic approach for mapping burned areas based on a combination of Hyperion EO-1 hyperspectral indexes and hotspots from the Near Real-Time (NRT) Suomi National Polar-orbiting Partnership (S-NPP) / Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMGTDL_NRT) [22]
Our study evaluated the use of a synergetic approach based on a distribution-modeling algorithm trained by hyperspectral indexes and hotspots to model and map burned areas
Summary
Wildfires are natural disturbances in many ecosystems [1], Mediterranean ones. An adequate post-fire management policy that prevents soil losses and promotes vegetation regeneration can only be based on accurate fire damage maps (burned area / burn severity) [4]. Reliable burned area estimates at all scales are obtained using remote sensing data and techniques [5]. Hyperspectral remote sensing of fire damage enables the accurate discrimination and quantification of burned areas, burn severity, and vegetation recovery [5]. Hyperspectral imagery has been successfully used in different fire studies [6,7]; the Hyperion sensor onboard the Earth-Observing One (EO-1) platform provided data that have been successfully utilized for fire detection [8,9] and burn severity mapping [10,11,12]
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