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

Abstract

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.

Spatially resolved horizontal spread in smouldering peat combining infrared and visual diagnostics

“…An important approach to study smouldering in field studies of peatlands is using infrared cameras due to larger scale of the spatial domain. Remote sensing of smouldering wildfires has attracted research interest to develop monitoring system using satellite data [21] or use of drones equipped with IR cameras. Compared to thermocouple measurements, infrared cameras offer the advantage of better spatial resolution and allow for a large burning area to be calculated.…”

Interannual climate variation, land type and village livelihood effects on fires in Kalimantan, Indonesia

“…While relatively high intensity fires can be accurately detected via these datasets, cooler or smouldering fires, especially those commonly occur on degraded peatland may be more difficult to detect . A recent study has shown that VIIRS fire product has the accuracy of detecting the actual smouldering on peatland of 71% (Sofan et al 2019), suggesting that about a third of the actual smouldering events were potentially overlooked. MODIS fire product likely has lower detection accuracy than VIIRS data for smouldering fires due to the lower spatial resolution.…”

Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

“…Landsat-8 OLI & TIRS SWIR, TIR 16 30 Peatland fire detection at nighttime & daytime [26,27] BIRD HSRS MWIR, TIR 2-3 370 Peatland fire detection at daytime [28] TET-1 TET-1 MWIR 2-5 160 Peatland fire detection at daytime [29] Suomi NPP VIIRS SWIR 0.5 750…”

“…However, because fire products are not available for Sentinel-2, there exists an opportunity for the development of a detection algorithm for this sensor. A fire detection algorithm, particularly for peatland fires in tropical environments, e.g., Indonesia, called Tropical Peatland Combustion Algorithm (ToPeCAl), has been established using aerosol, shortwave infrared (SWIR) and longwave thermal infrared (TIR) spectra and applied to Landsat-8 OLI/TIRS daytime [27].…”