Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires
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 resolutio...
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| Vydáno v: | Remote sensing (Basel, Switzerland) Ročník 9; číslo 11; s. 1161 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Basel
MDPI AG
01.11.2017
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| ISSN: | 2072-4292, 2072-4292 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Oom, Duarte Morelli, Fabiano Machado-Silva, Fausto Pereira, José De Carvalho, Luis Libonati, Renata Setzer, Alberto Pereira, Allan |
| Author_xml | – sequence: 1 givenname: Allan surname: Pereira fullname: Pereira, Allan – sequence: 2 givenname: José orcidid: 0000-0003-2583-3669 surname: Pereira fullname: Pereira, José – sequence: 3 givenname: Renata orcidid: 0000-0001-7570-1993 surname: Libonati fullname: Libonati, Renata – sequence: 4 givenname: Duarte surname: Oom fullname: Oom, Duarte – sequence: 5 givenname: Alberto surname: Setzer fullname: Setzer, Alberto – sequence: 6 givenname: Fabiano surname: Morelli fullname: Morelli, Fabiano – sequence: 7 givenname: Fausto surname: Machado-Silva fullname: Machado-Silva, Fausto – sequence: 8 givenname: Luis surname: De Carvalho fullname: De Carvalho, Luis |
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| SubjectTerms | active fire Algorithms Autonomy burned area Cerrado Fires First aid Image classification Infrared imaging Infrared radiometers Landsat Landsat satellites Mapping MODIS PROBA-V Radiometry Reflectance Remote sensing Satellite imagery Savannahs Scars Spatial data Spatial discrimination Spatial resolution support vector machine one class VIIRS |
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| Title | Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires |
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