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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Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 13; H. 19; S. 4005
Hauptverfasser:	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.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 06.10.2021
Schlagworte:	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
Online-Zugang:	Volltext
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