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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.

Intergovernmental Panel on Climate Change (IPCC) guidelines have been standardized and widely used to calculate methane (CH4) emissions from paddy fields. The emission factor (EF) is a key parameter in these guidelines, and it is different for each location globally and regionally. However, limited studies have been conducted to measure locally specific EFs (EFlocal) through on-site assessments and modeling their spatial distribution effectively. This study aims to investigate the potential of multisensor satellite data to develop a spatial model of CH4 emission estimation on rice paddy fields under different water management practices, i.e., continuous flooding (CF) and alternate wetting and drying (AWD) in Subang, West Java, Indonesia. The model employed the national EF (EFnational) and EFlocal using the IPCC guidelines. In this study, we employed the multisensor satellite data to derive the key parameters for estimating CH4 emission, i.e., rice cultivation area, rice age, and EF. Optical high-resolution images were used to delineate the rice cultivation area, Sentinel-1 SAR imagery was used for identifying transplanting and harvesting dates for rice age estimation, and ALOS-2/PALSAR-2 was used to map the water regime for determining the scaling factor of the EF. The closed-chamber method has been used to measure the daily CH4 flux rate on the local sites. The results revealed spatial variability in CH4 emissions, ranging from 1–5 kg/crop/season to 20–30 kg/crop/season, depending on the water regime. Fields under CF exhibited higher CH4 emissions than those under AWD, underscoring the critical role of water management in mitigating CH4 emissions. This study demonstrates the feasibility of combining remote sensing data with the IPCC model to spatially estimate CH4 emissions, providing a robust framework for sustainable rice cultivation and greenhouse gas (GHG) mitigation strategies.

In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire data (VNP14IMGML), which provides attributes of thermal anomalies every month from 2012 to 2020 in Indonesia. The characteristics of thermal anomalies other than biomass burning were explored using fire radiative power (FRP) values, confidence levels of active fire, fire pixel areas, and their allocations to permanent geographical features (i.e., volcano, river, lake, coastal line, road, and industrial/settlement areas). The Tukey test showed that there was a significant difference between the mean FRP values of the other thermal anomalies, type-1 (active volcano), type-2 (other static land sources), and type-3 (detection over water/offshore), at a confidence level of 95%. Most thermal anomalies other than biomass burning were in the nominal confidence level with a fire pixel area of 0.21 km2. High spatial images validated these thermal anomaly types as false positives of biomass burning. A zone map of potential false-positive active fire for biomass burning was established in this study by referring to the allocation of thermal anomalies from permanent geographical features. Implementing the zone map removed approximately 13% of the VIIRS active fires as the false positive of biomass burning. Insights gleaned through this study will support efficient ground validation of actual forest/land fires.

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.

The authors wish to make the following corrections to this paper [...]

In this research, remotely sensed data has been used to estimate the volume of pyroclastic deposits and analyze morphological changes that have resulted from the eruption of Sinabung volcano. Topographic information was obtained from these data and used for rapid mapping to assist in the emergency response. Topographic information and change analyses (pre- and syn- eruption) were conducted using digital elevation models (DEMs) for the period 2010–2015. Advanced spaceborne thermal emission and reflection radiometer (ASTER) global digital elevation model (GDEM) data from 2009 were used to generate the initial DEMs for the condition prior to the eruption of 2010. Satellite pour l’observation de la terre 6 (SPOT 6) stereo images acquired on 21 June 2015 and were used to make a DEM for that time. The results show that the estimated total volume of lava and pyroclastic deposits, produced during the period 2010 to mid-2015 is approximately 2.8 × 10 8  m 3 . This estimated volume of pyroclastic deposits can be used to predict the magnitude of future secondary lahar hazards, which are also related to the capacity of rivers in the area. Morphological changes are illustrated using cross-sectional analysis of the deposits, which are currently deposited to the east, southeast and south of the volcano. Such analyses can also help in forecasting the direction of the future flow hazards. The remote sensing and analysis methods used at Sinabung can also be applied at other volcanoes and to assess the threats of other types of hazards such as landslides and land subsidence.

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 m2 range. These results could lead to improved management of peatland fires and emission modeling.

This research focuses on providing information related to the damaging effects of the 2010 eruption of Merapi volcano in Central Java, Indonesia. This information will be used to help emergency responders to assess losses more timely and efficiently, and to monitor the progress in emergency response and recovery. The objectives of this research are: (a) to generate a map of pyroclastic deposits based on activities pre- and post-volcano eruption of 2010 in the research area, (b) to investigate the impact of volcano eruption on the environment, and (c) to assess the impact of volcano eruption on landuse. ALOS PALSAR remote sensing data pre- and post-disaster were used in this research for mapping the volcano eruption. Topographic and geomorphological maps were analyzed for profiling and field orientation, which were used to investigate the impact of volcano eruption on the environment. SPOT 4 satellite images were used in this research for updating landuse information from the topographic map. The result of the landuse updated data was used for assessment of the volcano eruption’s impact on landuse with the GIS raster environment. The volcanic eruption that occurred in 2010 is estimated to have an impact of 133.31 ha for settlements, 92.32 ha for paddy fields, 235.60 ha for dry farming, 570.98 ha for plantations, 380.86 ha for bare land, and 0.12 ha for forest areas. An estimate of the number of buildings damaged due to the volcano eruption in 2010 was carried out by overlaying a map of pyroclastic deposits and the information point of the building sites from the topographic map. The total number of buildings damaged is estimated to be around 12,276 units.

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.

Background: Peatland wildfires involve flaming vegetation and smouldering peat. The smouldering behaviour strongly depends on peat moisture, which can change significantly and quickly due to weather or human activities.Aims: We simulated wildfire in peatlands at the field scale and, for the first time, included daily variations of peat moisture.Methods: We developed KAPAS II, a cellular automaton that includes flaming and smouldering, and coupled it with PEATCLSM (Catchment Land Surface Model) for peatland hydrology.Key results: Compared with the satellite observations over 90 days of a 2018 wildfire in Borneo, KAPAS II predictions provide good agreement for burn scars (79% accuracy) and for the number of smouldering hotspots (85% accuracy). For the same burn scar, the model predicts that 54 ha of peat would smoulder when considering daily moisture variations, but only 12 ha if moisture was constant. Simulations at the same Borneo location, but in different years from 2000 to 2019, show the importance of seasons and climate events like El Niño.Conclusion: Temporal variations in peat moisture, which are strongly influenced by weather and climate, are important to predict the behaviour and severity of peatland wildfires.Implications: This model improves our understanding of wildfire behaviour in peatlands and can contribute to its mitigation.