Smoldering peat fires in Indonesia are responsible for large quantities of trace gas and particulate emissions. However, to date no satellite remote sensing technique has been demonstrated for the identification of smoldering peat fires. Fires have two distinct combustion phases: a high temperature flaming and low temperature smoldering phases. The flaming phase temperature is approximately twice that of the smoldering phase. This temperature differential results in a spectral displacement of the primary radiant emissions of the two combustion phases. It it is possible to exploit this spectral displacement using widely separated wavelength ranges. This paper examines active fire features found in short-wave infrared (SWIR) and long-wave infrared (LWIR) nighttime Landsat data collected on peatlands in Sumatra and Kalimantan. Landsat 8's SWIR bands are on the leading edge of flaming phase radiant emissions, with only minor contribution from the smoldering phase. Conversely, Landsat 8's LWIR bands are on the trailing edge of smoldering phase radiant emissions. After examining the LWIR fire features, we conclude that they are the result of smoldering phase combustion. This has been confirmed with field validation. Detection limits for smoldering peat fires in Landsat 8 is in the 40-90 m 2 range. These results could lead to improved management of peatland fires and emission modeling.
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.
The normalized change index and split-based approach methods have been applied in this research to create the semiautomatic unsupervised change-detection areas affected by flood using multi-temporal Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) remotely sensed data. This research is focused to provide information related to the flood inundation event that occurred in March 2010, in Karawang, West Java, Indonesia. The objectives of this research are as follows:(1) to generate a flood inundation map as rapid mapping steps in disaster mitigation effort and (2) to identify and assess the environmental damage caused by flood inundation event in the research area. ALOS PALSAR remotely sensed data with the acquisition pre-flood (March 09, 2010) and post-flood (March 26, 2010) were used for mapping flood inundation event. Flood inundation map and land-use data are used for the identification and assessment of the environmental damage caused by flood inundation event, which is done with GIS environment tools. The flood inundation event is estimated to have an impact of 7,158 ha for settlements; 20,039 ha for paddy fields; 668 ha for plantations; 1,641 ha for farms; 198 ha for agricultural cultivations; 1,161 ha for shrubberies; 1,022 ha for industrials; and 1,019 ha for road areas. The total number of building damages is estimated to be around 16,350 units. In general, this method can be used to assist emergency response efforts, through an inventory of areas affected by floods. In addition, the use of this method can be applied and it is recommended for future research in different locations, which are consistent and reliable to detect areas affected by other disasters such as flash floods, landslide, tsunami, volcano eruptions, and forest fire.
The result of the accuracy by using cross-correlation matrix between the LULC model in 2015 with the LULC reference in 2015 is 75.88 %. The dynamics of LULC changes showed that area-class forest, dry land, paddy fields and shrubbery would be expected to experience an area decreases in the extent from the year 2015 to 2050, with the rate of change in average: 10.52, 13.22, 14.49 and 1.15 ha/year, respectively. Meanwhile, the area-class bare soil, plantation, settlement and water body would be expected to experience an area increases, with the rate of change in average: 6.79, 11.14, 11.49 and 9.7 ha/year, respectively. Furthermore, flood damage assessment can be calculated by estimating LULC area affected by the flood, which is determined based on the overlay between LULC maps from the result of Markov-CA with flood maps from the result of Monte Carlo algorithm. Under current conditions, estimated flood damage exposure to extreme flood events with return periods of 100 years for the water level scenario Hc = 3 m and Hc = 5 m is more than €520 and €958 million, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.