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Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
by
Sofan, Parwati
, Khomarudin, M. Rokhis
, Jones, Eriita
, Roswintiarti, Orbita
, Bruce, David
in
accuracy
/ Algorithms
/ Carbon
/ Climate change
/ Clouds
/ Combustion
/ detection
/ Environmental monitoring
/ Fire detection
/ Fires
/ Ground truth
/ Indonesia
/ information
/ Infrared detectors
/ land
/ Landsat
/ Landsat satellites
/ Landsat-8
/ Masking
/ monitoring
/ Object recognition
/ peatland fires detection
/ Peatlands
/ Pixels
/ Remote sensing
/ Satellite observation
/ Satellites
/ Sensors
/ Sentinel-2
/ Smoldering
/ Spatial discrimination
/ Spatial resolution
/ SWIR
/ testing
/ TIR
/ Tropical environment
/ Tropical environments
/ water
/ Water purification
2020
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Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
by
Sofan, Parwati
, Khomarudin, M. Rokhis
, Jones, Eriita
, Roswintiarti, Orbita
, Bruce, David
in
accuracy
/ Algorithms
/ Carbon
/ Climate change
/ Clouds
/ Combustion
/ detection
/ Environmental monitoring
/ Fire detection
/ Fires
/ Ground truth
/ Indonesia
/ information
/ Infrared detectors
/ land
/ Landsat
/ Landsat satellites
/ Landsat-8
/ Masking
/ monitoring
/ Object recognition
/ peatland fires detection
/ Peatlands
/ Pixels
/ Remote sensing
/ Satellite observation
/ Satellites
/ Sensors
/ Sentinel-2
/ Smoldering
/ Spatial discrimination
/ Spatial resolution
/ SWIR
/ testing
/ TIR
/ Tropical environment
/ Tropical environments
/ water
/ Water purification
2020
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Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
by
Sofan, Parwati
, Khomarudin, M. Rokhis
, Jones, Eriita
, Roswintiarti, Orbita
, Bruce, David
in
accuracy
/ Algorithms
/ Carbon
/ Climate change
/ Clouds
/ Combustion
/ detection
/ Environmental monitoring
/ Fire detection
/ Fires
/ Ground truth
/ Indonesia
/ information
/ Infrared detectors
/ land
/ Landsat
/ Landsat satellites
/ Landsat-8
/ Masking
/ monitoring
/ Object recognition
/ peatland fires detection
/ Peatlands
/ Pixels
/ Remote sensing
/ Satellite observation
/ Satellites
/ Sensors
/ Sentinel-2
/ Smoldering
/ Spatial discrimination
/ Spatial resolution
/ SWIR
/ testing
/ TIR
/ Tropical environment
/ Tropical environments
/ water
/ Water purification
2020
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Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
Journal Article
Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery
2020
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Overview
This study establishes a new technique for peatland fire detection in tropical environments using Landsat-8 and Sentinel-2. The Tropical Peatland Combustion Algorithm (ToPeCAl) without longwave thermal infrared (TIR) (henceforth known as ToPeCAl-2) was tested on Landsat-8 Operational Land Imager (OLI) data and then applied to Sentinel-2 Multi Spectral Instrument (MSI) data. The research is aimed at establishing peatland fire information at higher spatial resolution and more frequent observation than from Landsat-8 data over Indonesia’s peatlands. ToPeCAl-2 applied to Sentinel-2 was assessed by comparing fires detected from the original ToPeCAl applied to Landsat-8 OLI/Thermal Infrared Sensor (TIRS) verified through comparison with ground truth data. An adjustment of ToPeCAl-2 was applied to minimise false positive errors by implementing pre-process masking for water and permanent bright objects and filtering ToPeCAl-2’s resultant detected fires by implementing contextual testing and cloud masking. Both ToPeCAl-2 with contextual test and ToPeCAl with cloud mask applied to Sentinel-2 provided high detection of unambiguous fire pixels (>95%) at 20 m spatial resolution. Smouldering pixels were less likely to be detected by ToPeCAl-2. The detected smouldering pixels from ToPeCAl-2 applied to Sentinel-2 with contextual testing and with cloud masking were only 35% and 56% correct, respectively; this needs further investigation and validation. These results demonstrate that even in the absence of TIR data, an adjusted ToPeCAl algorithm (ToPeCAl-2) can be applied to detect peatland fires at 20 m resolution with high accuracy especially for flaming. Overall, the implementation of ToPeCAl applied to cost-free and available Landsat-8 and Sentinel-2 data enables regular peatland fire monitoring in tropical environments at higher spatial resolution than other satellite-derived fire products.
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