RT info:eu-repo/semantics/article
T1 A synergetic approach to burned area mapping using maximum entropy modeling trained with hyperspectral data and VIIRS hotspots
A1 Fernández Manso, Alfonso
A1 Quintano Pastor, María del Carmen
K1 Mediterranean ecosystems
K1 Ecosistemas mediterráneos
K1 33 Ciencias Tecnológicas
AB 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.
PB MDPI
SN 2072-4292
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/52583
UL https://uvadoc.uva.es/handle/10324/52583
LA eng
NO Remote Sensing, 2020, vol. 12, n. 5, 858
NO Producción Científica
DS UVaDOC
RD 22-dic-2024