Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands

A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICIρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8.

[1]  Lawrence Ong,et al.  Landsat-8 Operational Land Imager (OLI) Radiometric Performance On-Orbit , 2015, Remote. Sens..

[2]  E. Chuvieco,et al.  Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence , 2013 .

[3]  F. Siegert,et al.  Detection and Characterization of Low Temperature Peat Fires during the 2015 Fire Catastrophe in Indonesia Using a New High-Sensitivity Fire Monitoring Satellite Sensor (FireBird) , 2016, PloS one.

[4]  M. Casaioli,et al.  Forecast verification: A summary of common approaches, and examples of application , 2008 .

[5]  François Petitcolin,et al.  Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data , 2002 .

[6]  Louis Giglio,et al.  Application of the Dozier retrieval to wildfire characterization: a sensitivity analysis , 2001 .

[7]  K. Moffett,et al.  Remote Sens , 2015 .

[8]  Florian Siegert,et al.  Peat fires detected by the BIRD satellite , 2004 .

[9]  A. Getis.  The Analysis of Spatial Association by Use of Distance Statistics , 2010 .

[10]  D. Fuller.  Satellite remote sensing of biomass burning with optical and thermal sensors , 2000 .

[11]  T. Eck,et al.  A review of biomass burning emissions part III: intensive optical properties of biomass burning particles , 2004 .

[12]  C. Justice,et al.  The collection 6 MODIS active fire detection algorithm and fire products , 2016, Remote sensing of environment.

[13]  C. Justice,et al.  Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: Product status and first evaluation results , 2014 .

[15]  R. Wright,et al.  HOTMAP: Global hot target detection at moderate spatial resolution , 2016 .

[16]  Simon D. Jones,et al.  Next Generation Fire Detection from Geostationary Satellites , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[17]  Christopher J. Banks,et al.  Global and regional importance of the tropical peatland carbon pool , 2011 .

[18]  C. O. Justicea,et al.  The MODIS fire products , 2002 .

[19]  Luke Wallace,et al.  ASSESSMENT OF THE UTILITY OF THE ADVANCED HIMAWARI IMAGER TO DETECT ACTIVE FIRE OVER AUSTRALIA , 2016 .

[20]  F. Siegert,et al.  Spatiotemporal fire occurrence in Borneo over a period of 10 years , 2009 .

[21]  Magnus O. Ulfarsson,et al.  New Insights for Detecting and Deriving Thermal Properties of Lava Flow Using Infrared Satellite during 2014-2015 Effusive Eruption at Holuhraun, Iceland , 2018, Remote. Sens..

[22]  T. Nightingale,et al.  Sentinel-3 SLSTR active fire detection and FRP product: Pre-launch algorithm development and performance evaluation using MODIS and ASTER datasets , 2012 .

[23]  G. Rein,et al.  Smouldering fire signatures in peat and their implications for palaeoenvironmental reconstructions , 2014 .

[24]  J. Reid,et al.  A review of biomass burning emissions, part I: gaseous emissions of carbon monoxide, methane, volatile organic compounds, and nitrogen containing compounds , 2005 .

[25]  N. Pergola,et al.  RST-FIRES, an exportable algorithm for early-fire detection and monitoring: description, implementation, and field validation in the case of the MSG-SEVIRI sensor , 2016 .

[26]  Susan Page,et al.  Tropical Peatland of the World , 2016 .

[27]  C. Justice,et al.  Validation of the MODIS active fire product over Southern Africa with ASTER data , 2005 .

[28]  W. Schroeder,et al.  Active fire detection using Landsat-8/OLI data , 2016 .

[29]  D. Roberts,et al.  Estimating subpixel fire sizes and temperatures from ASTER using multiple endmember spectral mixture analysis , 2009 .

[30]  Antonio J. Plaza,et al.  Land Surface Emissivity Retrieval From Different VNIR and TIR Sensors , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Jun Wang,et al.  Improving Nocturnal Fire Detection With the VIIRS Day–Night Band , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[32]  D. Morton,et al.  Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data , 2008 .

[33]  C. Justice,et al.  Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER) , 2008 .

[34]  D. Marchetti.  A physics exhibit to show the effect of the aerosol in the atmosphere on electromagnetic wave propagation , 2014, 1412.6800.

[35]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[36]  W. Schroeder,et al.  The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment , 2014 .

[37]  Simon D. Jones,et al.  Assessing the utility of the TET-1 hotspot detection and characterization algorithm for determining wildfire size and temperature , 2016 .

[38]  Herry Purnomo,et al.  Fire economy and actor network of forest and land fires in Indonesia , 2017 .

[39]  E. Jones,et al.  Detecting peatland combustion using shortwave and thermal infrared landsat-8 data , 2018 .

[40]  Guillermo Rein,et al.  Smouldering Fires and Natural Fuels , 2013 .

[41]  D. Roberts,et al.  Daytime fire detection using airborne hyperspectral data , 2009 .

[42]  G. Roberts,et al.  New GOES imager algorithms for cloud and active fire detection and fire radiative power assessment across North, South and Central America , 2010 .

[43]  undefined Cifor.  Fires in Indonesia: causes, costs and policy implications , 2015 .

[44]  市男 浅沼,et al.  Visible Infrared Imaging Radiometer Suits（VIIRS）による漁火観測 , 2013 .

[45]  David P. Roy,et al.  Global operational land imager Landsat-8 reflectance-based active fire detection algorithm , 2018, Int. J. Digit. Earth.

[46]  Muhammad Noor,et al.  Peatland in Indonesia , 2016 .

[47]  Vaibhav Garg,et al.  Retrieval of land surface temperature from Landsat 8 TIRS for the command area of Mula irrigation project , 2016, Environmental Earth Sciences.

[48]  A. Mitchell.  The ESRI guide to GIS analysis , 1999 .

[49]  J. F. Oliveira‐Júnior,et al.  Overview of fire foci causes and locations in Brazil based on meteorological satellite data from 1998 to 2011 , 2015, Environmental Earth Sciences.

[50]  Susan I. Stewart,et al.  Detection rates of the MODIS active fire product in the United States , 2008 .

[51]  Suwarsono,et al.  Long-wave infrared identification of smoldering peat fires in Indonesia with nighttime Landsat data , 2015 .

[52]  T. Loboda,et al.  Reconstruction of fire spread within wildland fire events in Northern Eurasia from the MODIS active fire product , 2004 .

[53]  J. San Miguel-Ayanz,et al.  Combining AVHRR and ATSR satellite sensor data for operational boreal forest fire detection , 2003 .

[54]  C. Belcher.  Fire phenomena and the earth system : an interdisciplinary guide to fire science , 2013 .