Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires

Peat fires differ from other wildfires in their duration, carbon losses, emissions of greenhouse gases and highly hazardous products of combustion and other environmental impacts. Moreover, it is difficult to identify peat fires using ground-based methods and to distinguish peat fires from forest fires and other wildfires by remote sensing. Using the example of catastrophic fires in July–August 2010 in the Moscow region (the center of European Russia), in the present study, we consider the results of peat-fire detection using Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) hotspots, peat maps, and analysis of land cover pre- and post-fire according to Landsat-5 TM data. A comparison of specific (for detecting fires) and non-specific vegetation indices showed the difference index ΔNDMI (pre- and post-fire normalized difference moisture Index) to be the most effective for detecting burns in peatlands according to Landsat-5 TM data. In combination with classification (both unsupervised and supervised), this index offered 95% accuracy (by ground verification) in identifying burnt areas in peatlands. At the same time, most peatland fires were not detected by Terra/Aqua MODIS data. A comparison of peatland and other wildfires showed the clearest differences between them in terms of duration and the maximum value of the fire radiation power index. The present results may help in identifying peat (underground) fires and their burnt areas, as well as accounting for carbon losses and greenhouse gas emissions.

Android mobile application for wildfire reporting and monitoring

Peat fires cause major environmental problems in Central Kalimantan Province, Indonesia and threaten human health and effect the social-economic sector. The lack of peat fire detection systems is one factor that causing these reoccurring fires. Therefore, in this study, we develop an Android mobile platform application and a web-based application to support the citizen-volunteers who want to contribute wildfires reports, and the decision-makers who wish to collect, visualize, and evaluate these wildfires reports. In this paper, the global navigation satellite system (GNSS) and a global position system (GPS) sensor from a smartphone’s camera, is a useful tool to show the potential fire and smoke’s close-range location. The exchangeable image (EXIF) file image and GPS metadata captured by a mobile phone can store and supply raw observation to our devices and sent it to the data center through global internet communication. This work’s results are the proposed application easy-to-use to monitoring potential peat fire by location and data activity. This paper focuses on developing an application for the mobile platform for peat fire reporting and a web-based application to collect peat fire location for decision-makers. Our main objective is to detect the potential and spread of fire in peatlands as early as possible by utilizing community reports using smartphones.

Loss and Recovery of Carbon in Repeatedly Burned Degraded Peatlands of Kalimantan, Indonesia

Although accurate estimates of biomass loss during peat fires, and recovery over time, are critical in understanding net peat ecosystem carbon balance, empirical data to inform carbon models are scarce. During the 2019 dry season, fires burned through 133,631 ha of degraded peatlands of Central Kalimantan. This study reports carbon loss from surface fuels and the top peat layer of 18.5 Mg C ha−1 (3.5 from surface fuels and 15.0 from root/peat layer), releasing an average of 2.5 Gg (range 1.8–3.1 Gg) carbon in these fires. Peat surface change measurements over one month, as the fires continued to smolder, indicated that about 20 cm of the surface was lost to combustion of peat and fern rhizomes, roots and recently incorporated organic residues that we sampled as the top peat layer. Time series analysis of live green vegetation (NDVI trend), combined with field observations of vegetation recovery two years after the fires, indicated that vegetation recovery equivalent to fire-released carbon is likely to occur around 3 years after fires.

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

Assessing Wood and Soil Carbon Losses from a Forest-Peat Fire in the Boreo-Nemoral Zone

Forest-peat fires are notable for their difficulty in estimating carbon losses. Combined carbon losses from tree biomass and peat soil were estimated at an 8 ha forest-peat fire in the Moscow region after catastrophic fires in 2010. The loss of tree biomass carbon was assessed by reconstructing forest stand structure using the classification of pre-fire high-resolution satellite imagery and after-fire ground survey of the same forest classes in adjacent areas. Soil carbon loss was assessed by using the root collars of stumps to reconstruct the pre-fire soil surface and interpolating the peat characteristics of adjacent non-burned areas. The mean (median) depth of peat losses across the burned area was 15 ± 8 (14) cm, varying from 13 ± 5 (11) to 20 ± 9 (19). Loss of soil carbon was 9.22 ± 3.75–11.0 ± 4.96 (mean) and 8.0–11.0 kg m−2 (median); values exceeding 100 tC ha−1 have also been found in other studies. The estimated soil carbon loss for the entire burned area, 98 (mean) and 92 (median) tC ha−1, significantly exceeds the carbon loss from live (tree) biomass, which averaged 58.8 tC ha−1. The loss of carbon in the forest-peat fire thus equals the release of nearly 400 (soil) and, including the biomass, almost 650 tCO2 ha−1 into the atmosphere, which illustrates the underestimated impact of boreal forest-peat fires on atmospheric gas concentrations and climate.

Kebakaran Hutan dan Lahan di Pulau Rupat, Riau

Forest and land fires in Riau province generally occur on peatlands. This is due to the large area of ​​peatlands and intensive land conversion. Peat fires result in high PM 2.5 pollutant content, as happened on Rupat Island, Meranti Islands. Rupat Island has a history of repeated cases of forest and land fires in 2015 and 2019. This study aims to analyze the background of the causes of forest and land fires on Rupat Island, Riau Province. The research was carried out through several stages, namely data collection, data processing and data analysis. Forest fires on Rupat Island caused PM 2.5 content in February and March to have values ​​above the PM 2.5 content threshold. Based on the results of the study, it is known that the background causes of forest and land fires on Rupat Island, Riau, are caused by poor canal management, damaged peat conditions and the influence of the arrival of El Nino. This is the cause of the widespread forest and land fires on Rupat Island. Keywords: forest fires, peat land, Rupat island

MACHINE LEARNING APPLICATION IN RESPONSE TO DISASTER RISK REDUCTION OF FOREST AND PEATLAND FIRE

Peat forest is a natural swamp ecosystem containing buried biomass from biomass deposits originating from past tropical swamp vegetation that have not been decomposed. In the dry season, this accumulated biomass called peat, which was originally submerged in water, is exposed to the surface and prone to fire. Once it burns, smouldering peat fires consume biomass 15 times larger than open flame. Peat smouldering fires are very difficult to extinguish. These will continuously occur for weeks to months. The most recommended effort by experts and practitioners of peat smouldering fires is to prevent them before they occur with the strategy: 'detect early, locate the fire, deliver the most appropriate technology'. Monitoring methods and early detection of forest and land fires or 'wildfire' have been highly developed and applied in Indonesia, for example monitoring with hotspot data, FWI (Fire Weather Index), and FDRS (Fire Danger Rating System). These 'physical simulator' based methods have some weaknesses (1) the accuracy is very low; (2) having predictive biases in areas with different ecosystem characteristics; (3) it is often difficult to implement because it requires a large and complex number of 'expert rules'; and (4) involving many variables, complex, and heterogeneous data formats. Slowly but surely this method has been replaced by the Machine Learning (ML) method as it is developing in Europe, China, India, Japan, North America and Australia. What about the potential application of ML in the forest and land fires, especially smouldering peat fires in Indonesia? This paper tries to answer this question through the following points: State of the art of machine learning in science and management of forest and land fire Outlook on technology of Disaster Risk Reduction (DRR) in BPPT in the field of forest and land fire and the opportunities of ML implementation Impact based forecasting and machine learning for DRR of forest and land fire This paper recommends a conceptual design: Impact-Based Learning for DRR of Forest, Land Fire and Peat Smouldering