Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires
AbstractWe used the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution) to automatically extract multispectral samples and train a One-Class Support Vector Machine for burned area mapping, and applied the resulting classification algorithm to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V). The active fire data were screened to prevent extraction of unrepresentative burned area samples and combined with surface reflectance bi-weekly composites to produce burned area maps. The procedure was applied over the Brazilian Cerrado savanna, validated with reference maps obtained from Landsat images and compared with the Collection 6 Moderate Resolution Imaging Spectrometer (MODIS) Burned Area product (MCD64A1) Results show that the algorithm developed improved the detection of small-sized scars and displayed results more similar to the reference data than MCD64A1. Unlike active fire-based region growing algorithms, the proposed approach allows for the detection and mapping of burn scars without active fires, thus eliminating a potential source of omission error. The burned area mapping approach presented here should facilitate the development of operational-automated burned area algorithms, and is very straightforward for implementation with other sensors. View Full-Text
Share & Cite This Article
Pereira, A.A.; Pereira, J.M.C.; Libonati, R.; Oom, D.; Setzer, A.W.; Morelli, F.; Machado-Silva, F.; De Carvalho, L.M.T. Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires. Remote Sens. 2017, 9, 1161.
Pereira AA, Pereira JMC, Libonati R, Oom D, Setzer AW, Morelli F, Machado-Silva F, De Carvalho LMT. Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires. Remote Sensing. 2017; 9(11):1161.Chicago/Turabian Style
Pereira, Allan A.; Pereira, José M.C.; Libonati, Renata; Oom, Duarte; Setzer, Alberto W.; Morelli, Fabiano; Machado-Silva, Fausto; De Carvalho, Luis M.T. 2017. "Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires." Remote Sens. 9, no. 11: 1161.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.