Multi-Sensor, Active Fire-Supervised, One-Class Burned Area Mapping in the Brazilian Savanna

Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 13; číslo 19; s. 4005
Hlavní autoři:	Pereira, Allan A., Libonati, Renata, Rodrigues, Julia A., Nogueira, Joana, Santos, Filippe L. M., Oom, Duarte, Sanches, Waislan, Alvarado, Swanni T., Pereira, José M. C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 06.10.2021
Témata:	Algorithms Archives & records Automation burned area Cerrado Classification Ecosystems fire regime Fires Forest & brush fires Grasslands humans Landsat Landsat satellites Machine learning Mapping one-class classification Remote sensing Satellite imagery Savannahs savannas Scars Seeds Sensors Support vector machines Time series Vegetation VIIRS Brazil United States > US
ISSN:	2072-4292, 2072-4292
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Abstract	Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates.
AbstractList	Increasing efforts are being devoted to understanding fire patterns and changes highlighting the need for a consistent database about the location and extension of burned areas (BA). Satellite-derived BA mapping accuracy in the Brazilian savannas is limited by the underestimation of burn scars from small, fragmented fires and high cloudiness. Moreover, systematic mapping of BA is challenged by the need for human intervention in training sample acquisition, which precludes the development of automatic-generated products over large areas and long periods. Here, we developed a multi-sensor, active fire-supervised, one-class BA mapping algorithm to address several of these limitations. Our main objective is to generate a long-term, detailed BA atlas suitable to improve fire regime characterization and validation of coarse resolution products. We use composite images derived from the Landsat satellite to generate end-of-season maps of fire-affected areas for the entire Cerrado. Validation exercises and intercomparison with BA maps from a semi-automatic algorithm and visual photo interpretation were conducted for the year 2015. Our results improve the BA mapping by reducing omission errors, especially where there is high cloud frequency, few active fires are detected, and burned areas are small and fragmented. Finally, our approach represents at least a 45% increase in BA mapped in the Cerrado, in comparison to the annual extent detected by the current coarse global product from MODIS satellite (MCD64), and thus, it is capable of supporting improved regional emissions estimates.
Author	Rodrigues, Julia A. Santos, Filippe L. M. Oom, Duarte Pereira, José M. C. Alvarado, Swanni T. Sanches, Waislan Pereira, Allan A. Libonati, Renata Nogueira, Joana
Author_xml	– sequence: 1 givenname: Allan A. surname: Pereira fullname: Pereira, Allan A. – sequence: 2 givenname: Renata orcidid: 0000-0001-7570-1993 surname: Libonati fullname: Libonati, Renata – sequence: 3 givenname: Julia A. orcidid: 0000-0003-0525-9516 surname: Rodrigues fullname: Rodrigues, Julia A. – sequence: 4 givenname: Joana orcidid: 0000-0002-1992-8461 surname: Nogueira fullname: Nogueira, Joana – sequence: 5 givenname: Filippe L. M. orcidid: 0000-0001-8680-1244 surname: Santos fullname: Santos, Filippe L. M. – sequence: 6 givenname: Duarte orcidid: 0000-0002-9244-3487 surname: Oom fullname: Oom, Duarte – sequence: 7 givenname: Waislan surname: Sanches fullname: Sanches, Waislan – sequence: 8 givenname: Swanni T. orcidid: 0000-0002-6416-0076 surname: Alvarado fullname: Alvarado, Swanni T. – sequence: 9 givenname: José M. C. orcidid: 0000-0003-2583-3669 surname: Pereira fullname: Pereira, José M. C.
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SubjectTerms	Algorithms Archives & records Automation burned area Cerrado Classification Ecosystems fire regime Fires Forest & brush fires Grasslands humans Landsat Landsat satellites Machine learning Mapping one-class classification Remote sensing Satellite imagery Savannahs savannas Scars Seeds Sensors Support vector machines Time series Vegetation VIIRS
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