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Remote Sens. 2017, 9(11), 1161;

Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires

Instituto Federal de Ciência e Tecnologia do Sul de Minas Gerais, 37713-100 Poços de Caldas, Brazil
Centro de Estudos Florestais, Instituto Superior de Agronomia, Universidade de Lisboa, 1349-017 Lisboa, Portugal
Departamento de Meteorologia, Universidade Federal do Rio de Janeiro, 21941-916 Rio de Janeiro, Brazil
Centro de Previsão do Tempo e Estudos Climáticos, Instituto Nacional de Pesquisas Espaciais, 12227-010 São José dos Campos, Brazil
Departamento de Engenharia Florestal, Universidade Federal de Lavras, 37200-000 Lavras, Brazil
Author to whom correspondence should be addressed.
These authors equally contributed to this work.
Received: 7 September 2017 / Revised: 23 October 2017 / Accepted: 7 November 2017 / Published: 14 November 2017
(This article belongs to the Section Forest Remote Sensing)
PDF [6241 KB, uploaded 14 November 2017]


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. View Full-Text
Keywords: support vector machine one class; burned area; active fire; Cerrado; PROBA-V; VIIRS support vector machine one class; burned area; active fire; Cerrado; PROBA-V; VIIRS

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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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.

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