A synergetic approach to burned area mapping using maximum entropy modeling trained with hyperspectral data and VIIRS hotspots

Fernández-Manso, A., Quintano, C. (2020) - A synergetic approach to burned area mapping using maximum entropy modeling trained with hyperspectral data and VIIRS hotspots - Remote Sensing

 Southern European countries, particularly Spain, are greatly a ected 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). O cial

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

Di erence Vegetation Index (NDVI750 ), Normalized Di erenceWater 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.

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