Autor(es): Pereira, Allan A. ; Cardoso Pereira, José Miguel ; Libonati, Renata ; Oom, Duarte ; Setzer, Alberto W. ; Morelli, Fabiano ; Machado-Silva, Fausto ; Carvalho, Luís Marcelo Tavares de
Data: 2017
Identificador Persistente: http://hdl.handle.net/10400.5/14620
Origem: Repositório da Universidade de Lisboa
Assunto(s): support vector machine one class; burned area; active fire; Cerrado; PROBA-V; VIIRS
We 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
