Tropical Peatland Fires Research Articles

Elevated methane flux in a tropical peatland post-fire is linked to depth-dependent changes in peat microbiome assembly

Fires in tropical peatlands extend to depth, transforming them from carbon sinks into methane sources and severely limit forest recovery. Peat microbiomes influence carbon transformations and forest recovery, yet our understanding of microbiome shifts post-fire is currently limited. Our previous study highlighted altered relationships between the peat surface, water table, aboveground vegetation, and methane flux after fire in a tropical peatland. Here, we link these changes to post-fire shifts in peat microbiome composition and assembly processes across depth. We report kingdom-specific and depth-dependent shifts in alpha diversity post-fire, with large differences at deeper depths. Conversely, we found shifts in microbiome composition across all depths. Compositional shifts extended to functional groups involved in methane turnover, with methanogens enriched and methanotrophs depleted at mid and deeper depths. Finally, we show that community shifts at deeper depths result from homogeneous selection associated with post-fire changes in hydrology and aboveground vegetation. Collectively, our findings provide a biological basis for previously reported methane fluxes after fire and offer new insights into depth-dependent shifts in microbiome assembly processes, which ultimately underlie ecosystem function predictability and ecosystem recovery.

Reduction of Carbon Emissions from Tropical Peat Land Fire Disasters Using Weather Modification Technology

Reduction of Carbon Emissions from Tropical Peat Land Fire Disasters Using Weather Modification Technology

Building capacity for estimating fire emissions from tropical peatlands; a worked example from Indonesia

Tropical peatlands are globally significant in the terrestrial carbon cycle as they are comprised of a large forest carbon sink and a large peat carbon store—both of which can potentially be exchanged with the atmosphere on decadal time frames. Greenhouse gas emissions from fire-disturbance and development of tropical peatlands over the last few decades, and the potential for ongoing emissions, highlights the need for policy to slow or halt emissions and to activate mechanisms to sequester carbon through restoration of degraded peatlands. The UN REDD + scheme provides a means for developing countries to receive payments for avoided deforestation and forest degradation, but the steps to achieve REDD+ compliance are rigorous and the details required can be a barrier to activating benefits—especially for peatlands where repeated cycles of fire interrupt forest recovery and create a range of recovery classes. Therefore, to improve estimates of peat fire emissions and of carbon balance of tropical peatlands, the biomass and combustion factor parameters need to be developed and applied according to forest recovery stage. In this study we use published activity data from the extensive 1997 fires in the peatlands of Indonesian Borneo to detail a transparent and accountable way to estimate and report emissions from tropical peatland fires. This example for estimating and reporting emissions is provided to assist REDD+ countries to efficiently develop their capacity for improving emissions estimates from fire-impacted tropical peatlands.

Prediction of Peatlands Forest Fires in Malaysia Using Machine Learning

The occurrence of fires in tropical peatlands poses significant threats to their ecosystems. An Internet of Things (IoT) system was developed to measure and collect fire risk factors in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, to address this issue. In this paper, neural networks with different layers were employed to predict peatland forests’ Fire Weather Index (FWI). The neural network models used two sets of input parameters, consisting of four and nine fire factors. The predicted FWI values were compared with actual values obtained from the Malaysian meteorological department. The findings revealed that the five-layer neural network outperformed others in both the four-input and nine-input models. Specifically, the nine-input neural network achieved a mean square error (MSE) of 1.116 and a correlation of 0.890, surpassing the performance of the four-input neural network with the MSE of 1.537 and the correlation of 0.852. These results hold significant research and practical implications for precise peatland fire prevention, control, and the formulation of preventive measures.

Beyond Climatic Variation: Human Disturbances Alter the Effectiveness of a Protected Area to Reduce Fires in a Tropical Peatland

Fire is considered a major threat to biodiversity in many habitats and the occurrence of fire has frequently been used to investigate the effectiveness of protected areas. Yet, despite the known importance of tropical peatlands for biodiversity conservation and serious threat that anthropogenically induced fires pose to this ecosystem, the influence of protected area designation on fire occurrence in tropical peatland has been poorly assessed thus far. Our study addresses this knowledge gap through providing a novel assessment of fire patterns from a tropical peatland protected area and surrounding landscape. We investigated the importance of both climatic factors (top-down mechanism) and human interventions (bottom-up mechanism) on fire occurrence through analyzing 20-years (2001–2020) of LANDSAT and Moderate Resolution Imaging Spectrometer (MODIS) images of the Padang Sugihan Wildlife Reserve and a 10-km buffer area surrounding this in Sumatra, Indonesia. Fire density was assessed in relation to road and canal construction. Monthly and annual precipitation was compared between wet and dry years. The reserve was effective in limiting fire compared to surrounding landscapes only in wet years. We revealed that peat fire occurrence in the protected area and buffer zone was not due to climatic factors alone, with distance from canals and roads also contributing toward fire occurrence. Our results suggest that it is essential to address tropical peatland fire processes at a landscape level, particularly at the surroundings of protected areas, in order to increase the effectiveness of fire protection, improve fire risk classification maps, and conserve threatened tropical peatland wildlife such as the Sumatran elephant.

Carbonaceous aerosol composition in air masses influenced by large-scale biomass burning: a case study in northwestern Vietnam

Abstract. We investigated concentrations of organic carbon (OC), elemental carbon (EC), and a wide range of particle-bound organic compounds in daily sampled PM2.5 at the remote Pha Din (PDI) – Global Atmosphere Watch (GAW) monitoring station in northwestern Vietnam during an intense 3-week sampling campaign from 23 March to 12 April 2015. The site is known to receive trans-regional air masses during large-scale biomass burning (BB) episodes. BB is a globally widespread phenomenon and BB emission characterization is of high scientific and societal relevance. Emissions composition is influenced by multiple factors (e.g., fuel and thereby vegetation type, fuel moisture, fire temperature, available oxygen). Due to regional variations in these parameters, studies in different world regions are needed. OC composition provides valuable information regarding the health- and climate-relevant properties of PM2.5. Yet, OC composition studies from PDI are missing in the scientific literature to date. Therefore, we quantified 51 organic compounds simultaneously by in situ derivatization thermal desorption gas chromatography and time-of-flight mass spectrometry (IDTD-GC-TOFMS). Anhydrosugars, methoxyphenols, n-alkanes, fatty acids, polycyclic aromatic hydrocarbons, oxygenated polycyclic aromatic hydrocarbons, nitrophenols, and OC were used in a hierarchical cluster analysis highlighting distinctive patterns for periods under low, medium, and high BB influence. The highest particle phase concentration of the typical primary organic aerosol (POA) and possible secondary organic aerosol (SOA) constituents, especially nitrophenols, were found on 5 and 6 April. We linked the trace gas mixing ratios of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and ozone (O3) to the statistical classification of BB events based on OA composition and found increased CO and O3 levels during medium and high BB influence. Likewise, a backward trajectory analysis indicates different source regions for the identified periods based on the OA clusters, with cleaner air masses arriving from the northeast, i.e., mainland China and the Yellow Sea. The more polluted periods are characterized by trajectories from the southwest, with more continental recirculation of the medium cluster and more westerly advection for the high cluster. These findings highlight that BB activities in northern Southeast Asia significantly enhance the regional organic aerosol loading and also affect the carbonaceous PM2.5 constituents and the trace gases in northwestern Vietnam. The presented analysis adds valuable data on the carbonaceous and chemical composition of PM2.5, in particular of OC, in a region of scarce data availability, and thus offers a reference dataset from Southeast Asian large-scale BB for future studies. Such a reference dataset may be useful for the evaluation of atmospheric transport simulation models, or for comparison with other world regions and BB types, such as Australian bush fires, African savannah fires, or tropical peatland fires.

Peatland Fire Geotagging Photo for Investigation Tool using Smartphone

One of the ways to control tropical peat fires is to use a combination of the Global Navigation Satellite System (GNSS) and smartphones for monitoring, reporting, and verification of the location of the fires. The latest smartphones have many sensors, such as a compass, accelerometer, GPS, and a camera, to collect forest activity data from a specific target area. The collected data were then transferred to a cloud server through global mobile communication. This paper discusses a mobile and web application-based approach to collect and analyze user-generated geographical information of human activity data. The paper aims is to promote law enforcement agencies, local government, and fire patrol to consider the use of this low-cost, easy-to-use technology in controlling and reducing the risk of tropical peatland fires. Our mobile application has an easy-to-use mapping technology that allows its users to locate addresses quickly and provides cartographic maps augmented with digital information and high-resolution aerial imagery. This study proposes online citizen reporting as a new approach for law enforcement by aiding local government and fire patrols to conduct monitoring, reporting, and verification to reduce the risk of peat forest fires.

Using cellular automata to simulate field-scale flaming and smouldering wildfires in tropical peatlands

Peat wildfires are the largest fires on Earth involving both flaming and smouldering combustion, with one leading to the other. A common ignition source of smouldering fires in tropical peatlands are intentional flaming fires used to clear surface vegetation. To capture the behaviour of these fires, it is necessary to consider the interaction between flaming vegetation and smouldering peat. However, doing so is infeasible with the state-of-the-art wildfire models, as they do not consider the transition from flaming to smouldering and are computationally too expensive at the field-scale hundreds of hectares. In this work, we overcome these limitations and model both flaming and smouldering at the field-scale using cellular automata: that is a discrete mathematical model that uses simple rules to capture complex behaviour while remaining computationally light. The model was calibrated against existing experiments in the literature and used to predict the effect of peat moisture content on the behaviour of peatland wildfires. The model shows how flaming creates smouldering hotspots and how these hotspots merge – flaming spreads rapidly, consuming surface vegetation, leaving behind hotspots of smouldering peat which consumes most of the peat. The model was then applied to study a real prescribed fire of 573 ha peatland in Borneo in 2015, observed by drone footage. The model captured the spread patterns of the fire and predicted that 2.9 ha of peatland burnt after 3 months with 70% peat moisture content (dry-based). This ioutcome could have been reduced to 0.02 ha if the peat moisture content had been above 100%. This work improves the fundamental understanding of how peat wildfires spread at the field scale which has received little attention until now.

New Tropical Peatland Gas and Particulate Emissions Factors Indicate 2015 Indonesian Fires Released Far More Particulate Matter (but Less Methane) than Current Inventories Imply

Deforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September–October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered “extremely hazardous to health” by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO2, CH4 and CO) and PM2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of “pure” sub-surface peat burning and spreading vegetation fires atop burning peat. PM2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg−1, and for spreading vegetation fires atop burning peat 44 to 61 g·kg−1, both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM2.5 emissions factors measured worldwide. Using our peatland CO2, CH4 and CO emissions factors (1779 ± 55 g·kg−1, 238 ± 36 g·kg−1, and 7.8 ± 2.3 g·kg−1 respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg−1) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EFCH4 produces far less (~3×) methane. However, our mean in situ derived EFPM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg−1) is far higher than current emissions inventories assume, resulting in our total PM2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions’ spatio-temporal variations in far greater detail than ever before (hourly, 0.05°), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in particular led to millions of citizens being exposed to extremely poor levels of air quality for substantial periods.

Measuring Emissions from Smoldering Peat Fires

A new study measures emission factors for tropical peatland fires in Malaysia.

In Situ Tropical Peatland Fire Emission Factors and Their Variability, as Determined by Field Measurements in Peninsula Malaysia

AbstractFires in tropical peatlands account for >25% of estimated total greenhouse gas emissions from deforestation and degradation. Despite significant global and regional impacts, our understanding of specific gaseous fire emission factors (EFs) from tropical peat burning is limited to a handful of studies. Furthermore, there is substantial variability in EFs between sampled fires and/or studies. For example, methane EFs vary by 91% between studies. Here we present new fire EFs for the tropical peatland ecosystem; the first EFs measured for Malaysian peatlands, and only the second comprehensive study of EFs in this crucial environment. During August 2015 (under El Niño conditions) and July 2016, we embarked on field campaigns to measure gaseous emissions at multiple peatland fires burning on deforested land in Southeast Pahang (2015) and oil palm plantations in North Selangor (2016), Peninsula Malaysia. Gaseous emissions were measured using open‐path Fourier transform infrared spectroscopy. The IR spectra were used to retrieve mole fractions of 12 different gases present within the smoke (including carbon dioxide and methane), and these measurements used to calculate EFs. Peat samples were taken at each burn site for physicochemical analysis and to explore possible relationships between specific physicochemical properties and fire EFs. Here we present the first evidence to indicate that substrate bulk density affects methane fire EFs reported here. This novel explanation of interplume, within‐biome variability, should be considered by those undertaking greenhouse gas accounting and haze forecasting in this region and is of importance to peatland management, particularly with respect to artificial compaction.

Temperature and burning history affect emissions of greenhouse gases and aerosol particles from tropical peatland fire

AbstractTropical peatland burning in Asia has been intensifying over the last decades, emitting huge amounts of gas species and aerosol particles. Both laboratory and field studies have been conducted to investigate emission from peat burning, yet a significant variability in data still exists. We conducted a series of experiments to characterize the gas and particulate matter emitted during burning of a peat sample from Sumatra in Indonesia. Heating temperature of peat was found to regulate the ratio of CH4 to CO2 in emissions (ΔCH4/ΔCO2) as well as the chemical composition of particulate matter. The ΔCH4/ΔCO2 ratio was larger for higher temperatures, meaning that CH4 emission is more pronounced at these conditions. Mass spectrometric analysis of organic components indicated that aerosol particles emitted at higher temperatures had more unsaturated bonds and ring structures than that emitted from cooler fires. The result was consistently confirmed by nuclear magnetic resonance analysis. In addition, CH4 emitted by burning charcoal, which is derived from previously burned peat, was lower by at least an order of magnitude than that from fresh peat. These results highlight the importance of both fire history and heating temperature for the composition of tropical peat‐fire emissions. They suggest that remote sensing technologies that map fire histories and temperatures could provide improved estimates of emissions.

Tropical Peatland Burn Depth and Combustion Heterogeneity Assessed Using UAV Photogrammetry and Airborne LiDAR

We provide the first assessment of tropical peatland depth of burn (DoB) using structure from motion (SfM) photogrammetry, applied to imagery collected using a low-cost, low-altitude unmanned aerial vehicle (UAV) system operated over a 5.2 ha tropical peatland in Jambi Province on Sumatra, Indonesia. Tropical peat soils are the result of thousands of years of dead biomass accumulation, and when burned are globally significant net sources of carbon emissions. The El Niño year of 2015 saw huge areas of Indonesia affected by tropical peatland fires, more so than any year since 1997. However, the Depth of Burn (DoB) of these 2015 fires has not been assessed, and indeed has only previously been assessed in few tropical peatland burns in Kalimantan. Therefore, DoB remains arguably the largest uncertainty when undertaking fire emissions calculations in these tropical peatland environments. We apply a SfM photogrammetric methodology to map this DoB metric, and also investigate combustion heterogeneity using orthomosaic photography collected using the UAV system. We supplement this information with pre-burn airborne light detection and ranging (LiDAR) data, reducing uncertainty by estimating pre-burn soil height more accurately than from interpolation of adjacent unburned areas alone. Our pre-and post-fire Digital Terrain Models (DTMs) show accuracies of 0.04 and 0.05 m (root-mean-square error, RMSE) respectively, compared to ground-based global navigation satellite system (GNSS) surveys. Our final DoB map of a 5.2 ha degraded peat swamp forest area neighboring Berbak National Park (Sumatra, Indonesia) shows burn depths extending from close to zero to over 1 m, with a mean (±1σ) DoB of 0.23 ± 0.19 m. This lies well within the range found by the few other studies available (on Kalimantan; none are available on Sumatra). Our combustion heterogeneity analysis suggests the deepest burns, which extend to ~1.3 m, occur around tree roots. We use these DoB data within the Intergovernmental Panel on Climate Change (IPCC) default equation for fire emissions to estimate mean carbon emissions as 134 ± 29 t·C∙ha−1 for this peatland fire, which is in an area that had not had a recorded fire previously. This is amongst the highest per unit area fuel consumption anywhere in the world for landscape fires. Our approach provides significant uncertainty reductions in such emissions calculations via the reduction in DoB uncertainty, and by using the UAV SfM approach this is accomplished at a fraction of the cost of airborne LiDAR—albeit over limited sized areas at present. Deploying this approach at locations across Indonesia, sampling a variety of fire-affected landscapes, would provide new and important DoB statistics for producing optimized carbon and greenhouse gas (GHG) emissions estimates from peatland fires.

Variable carbon losses from recurrent fires in drained tropical peatlands.

Tropical peatland fires play a significant role in the context of global warming through emissions of substantial amounts of greenhouse gases. However, the state of knowledge on carbon loss from these fires is still poorly developed with few studies reporting the associated mass of peat consumed. Furthermore, spatial and temporal variations in burn depth have not been previously quantified. This study presents the first spatially explicit investigation of fire-driven tropical peat loss and its variability. An extensive airborne Light Detection and Ranging data set was used to develop a prefire peat surface modelling methodology, enabling the spatially differentiated quantification of burned area depth over the entire burned area. We observe a strong interdependence between burned area depth, fire frequency and distance to drainage canals. For the first time, we show that relative burned area depth decreases over the first four fire events and is constant thereafter. Based on our results, we revise existing peat and carbon loss estimates for recurrent fires in drained tropical peatlands. We suggest values for the dry mass of peat fuel consumed that are 206tha(-1) for initial fires, reducing to 115tha(-1) for second, 69tha(-1) for third and 23tha(-1) for successive fires, which are 58-7% of the current IPCC Tier 1 default value for all fires. In our study area, this results in carbon losses of 114, 64, 38 and 13tCha(-1) for first to fourth fires, respectively. Furthermore, we show that with increasing proximity to drainage canals both burned area depth and the probability of recurrent fires increase and present equations explaining burned area depth as a function of distance to drainage canal. This improved knowledge enables a more accurate approach to emissions accounting and will support IPCC Tier 2 reporting of fire emissions.

Characteristics of fire-generated gas emission observed during a large peatland fire in 2009 at Kalimantan, Indonesia

To investigate the characteristics of gas emissions from a tropical peatland fire, ground-level measurement of fire-generated gases was conducted during a large fire event in Kalimantan, Indonesia in 2009. Concentrations of CO and CH4 showed positive linear correlations with that of CO2. The relationship between concentrations of N2O and CO2 were divided into two parts, suggesting the influence of additional N2O generation during sample storage. The CO2-normalized emission ratio was calculated for CO, CH4, and N2O. The molar ratio of these fire-generated gas emissions was summarized as CO2:CO:CH4:N2O = 1.00:0.382:0.0261:0.000156, whereas the emission ratio calculated on the global warming potential (GWP) basis was CO2:CH4:N2O = 1.00:0.237:0.0465. The GWP emission based on this ratio was 87.8–91.2% of a simple evaluation in which all carbon was assumed to be emitted as CO2. This is the first trial to evaluate the emission ratios of major greenhouse gases on the basis of ground-level observation during an actual tropical peatland fire.

Development of the Indonesian and Malaysian Fire Danger Rating Systems

Forest and land fires in Southeast Asia have many social, economic, and environ- mental impacts. Tropical peatland fires affect global carbon dynamics, and haze from peat fires has serious negative impacts on the regional economy and human health. To mitigate these fire-related problems, forest and land management agencies require an early warning system to assist them in implementing fire prevention and management plans before fire prob- lems begin. Fire Danger Rating Systems (FDRS) were developed for Indonesia and Malaysia to provide early warning of the potential for serious fire and haze events. In particular, they identify time periods when fires can readily start and spread to become uncontrolled fires and time periods when smoke from smouldering fires will cause an unacceptably high level of haze. The FDRS were developed by adapting components of the Canadian Forest Fire Danger Rating System, including the Canadian Forest Fire Weather Index (FWI) System and the Canadian Forest Fire Behavior Prediction (FBP) System, to local vegetation, climate, and fire regime conditions. A smoke potential indicator was developed using the Drought Code (DC) of the FWI System. Historical air quality analysis showed that the occurrence of severe haze events increased substantially when DC was above 400. An ignition potential indicator was developed using the Fine Fuel Moisture Code (FFMC) of the FWI System. Historical hot spot analysis, grass moisture, and grass ignition studies showed that fire occurrence and the ability for grass fires to start and spread dramatically increased when FFMC > 82. The Initial Spread Index (ISI) of the FWI System was used to develop a difficulty of control indicator for grassland fires, a fuel type that can exhibit high rates of spread and fire intensity.