Assessing VIIRS capabilities to improve burned area mapping over the Brazilian Cerrado

Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrad...

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Veröffentlicht in:	International journal of remote sensing Jg. 41; H. 21; S. 8300 - 8327
Hauptverfasser:	Santos, Filippe L.M., Libonati, Renata, Peres, Leonardo F., Pereira, Allan A., Narcizo, Luiza C., Rodrigues, Julia A., Oom, Duarte, Pereira, José M. C., Schroeder, Wilfrid, Setzer, Alberto W.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Taylor & Francis 01.11.2020 Taylor & Francis Ltd
Schlagworte:	Advanced Very High Resolution Radiometer Algorithms cerrado fire season Fires Image enhancement Imagery Imaging radiometers Imaging techniques Infrared imaging Infrared radiometers Landsat Landsat satellites Machine learning Mapping MODIS Radiometers Radiometry Reliability analysis Remote sensing Resolution Scars Sensors Spatial resolution Spectroradiometers Support vector machines
ISSN:	0143-1161, 1366-5901, 1366-5901
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Abstract	Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrades the spatial resolution (375 m) and gives continuity to the Earth long-term monitoring initiated by Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Therefore, aiming to assess VIIRS 375 m imagery capabilities to improve the accuracy and reliability of fire scars mapping over the Brazilian Cerrado, we developed a burned area detection algorithm (VIIRS-SVM) based on machine learning techniques. For this purpose, the (V, W) burnt index adjusted to VIIRS near-infrared and middle-infrared channels and the One-Class Support Vector Machine algorithm were used for burned area identification. The VIIRS-SVM algorithm was applied over the Brazilian Cerrado and evaluated against reference scars from 15 Landsat-8 scenes during the fire season of 2015, covering a large area with substantial variability in terms of fire scars characteristics. We also performed a comparison with the MCD64A1 collection-6 product over the validation sites. Relying on VIIRS 375 m imagery, the VIIRS-SVM algorithm allows an enhancement of 25% in discrimination of small and medium fire scars (25 to 1000 ha), when compared to the MODIS-derived product. Results have demonstrated that the enhancement of medium and small fire scars mapping over the Cerrado is possible using VIIRS sensor capabilities.
AbstractList	Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrades the spatial resolution (375 m) and gives continuity to the Earth long-term monitoring initiated by Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Therefore, aiming to assess VIIRS 375 m imagery capabilities to improve the accuracy and reliability of fire scars mapping over the Brazilian Cerrado, we developed a burned area detection algorithm (VIIRS-SVM) based on machine learning techniques. For this purpose, the (V, W) burnt index adjusted to VIIRS near-infrared and middle-infrared channels and the One-Class Support Vector Machine algorithm were used for burned area identification. The VIIRS-SVM algorithm was applied over the Brazilian Cerrado and evaluated against reference scars from 15 Landsat-8 scenes during the fire season of 2015, covering a large area with substantial variability in terms of fire scars characteristics. We also performed a comparison with the MCD64A1 collection-6 product over the validation sites. Relying on VIIRS 375 m imagery, the VIIRS-SVM algorithm allows an enhancement of 25% in discrimination of small and medium fire scars (25 to 1000 ha), when compared to the MODIS-derived product. Results have demonstrated that the enhancement of medium and small fire scars mapping over the Cerrado is possible using VIIRS sensor capabilities. Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in particular in areas characterized by small and sparse fire scars. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor launched in 2011 upgrades the spatial resolution (375 m) and gives continuity to the Earth long-term monitoring initiated by Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Therefore, aiming to assess VIIRS 375 m imagery capabilities to improve the accuracy and reliability of fire scars mapping over the Brazilian Cerrado, we developed a burned area detection algorithm (VIIRS-SVM) based on machine learning techniques. For this purpose, the (V, W) burnt index adjusted to VIIRS near-infrared and middle-infrared channels and the One-Class Support Vector Machine algorithm were used for burned area identification. The VIIRS-SVM algorithm was applied over the Brazilian Cerrado and evaluated against reference scars from 15 Landsat-8 scenes during the fire season of 2015, covering a large area with substantial variability in terms of fire scars characteristics. We also performed a comparison with the MCD64A1 collection-6 product over the validation sites. Relying on VIIRS 375 m imagery, the VIIRS-SVM algorithm allows an enhancement of 25% in discrimination of small and medium fire scars (25 to 1000 ha), when compared to the MODIS-derived product. Results have demonstrated that the enhancement of medium and small fire scars mapping over the Cerrado is possible using VIIRS sensor capabilities.
Author	Rodrigues, Julia A. Santos, Filippe L.M. Narcizo, Luiza C. Peres, Leonardo F. Oom, Duarte Pereira, José M. C. Schroeder, Wilfrid Pereira, Allan A. Setzer, Alberto W. Libonati, Renata
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Snippet	Coarse spatial resolution of remote sensing imagery still hampers a comprehensive representation of long-term fire patterns at the regional level, in...
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SubjectTerms	Advanced Very High Resolution Radiometer Algorithms cerrado fire season Fires Image enhancement Imagery Imaging radiometers Imaging techniques Infrared imaging Infrared radiometers Landsat Landsat satellites Machine learning Mapping MODIS Radiometers Radiometry Reliability analysis Remote sensing Resolution Scars Sensors Spatial resolution Spectroradiometers Support vector machines
Title	Assessing VIIRS capabilities to improve burned area mapping over the Brazilian Cerrado
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