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The 2015–2016 strong El Niño event has had a dramatic impact on the amount of Indonesian biomass burning, with the El Niño-driven drought further desiccating the already-drier-than-normal landscapes that are the result of decades of peatland draining, widespread deforestation, anthropogenically driven forest degradation and previous large fire events. It is expected that the 2015–2016 Indonesian fires will have emitted globally significant quantities of greenhouse gases (GHGs) to the atmosphere, as did previous El Niño-driven fires in the region. The form which the carbon released from the combustion of the vegetation and peat soils takes has a strong bearing on its atmospheric chemistry and climatological impacts. Typically, burning in tropical forests and especially in peatlands is expected to involve a much higher proportion of smouldering combustion than the more flaming-characterised fires that occur in fine-fuel-dominated environments such as grasslands, consequently producing significantly more CH4 (and CO) per unit of fuel burned. However, currently there have been no aircraft campaigns sampling Indonesian fire plumes, and very few ground-based field campaigns (none during El Niño), so our understanding of the large-scale chemical composition of these extremely significant fire plumes is surprisingly poor compared to, for example, those of southern Africa or the Amazon.Here, for the first time, we use satellite observations of CH4 and CO2 from the Greenhouse gases Observing SATellite (GOSAT) made in large-scale plumes from the 2015 El Niño-driven Indonesian fires to probe aspects of their chemical composition. We demonstrate significant modifications in the concentration of these species in the regional atmosphere around Indonesia, due to the fire emissions.Using CO and fire radiative power (FRP) data from the Copernicus Atmosphere Service, we identify fire-affected GOSAT soundings and show that peaks in fire activity are followed by subsequent large increases in regional greenhouse gas concentrations. CH4 is particularly enhanced, due to the dominance of smouldering combustion in peatland fires, with CH4 total column values typically exceeding 35 ppb above those of background “clean air” soundings. By examining the CH4 and CO2 excess concentrations in the fire-affected GOSAT observations, we determine the CH4 to CO2 (CH4 ∕ CO2) fire emission ratio for the entire 2-month period of the most extreme burning (September–October 2015), and also for individual shorter periods where the fire activity temporarily peaks. We demonstrate that the overall CH4 to CO2 emission ratio (ER) for fires occurring in Indonesia over this time is 6.2 ppb ppm−1. This is higher than that found over both the Amazon (5.1 ppb ppm−1) and southern Africa (4.4 ppb ppm−1), consistent with the Indonesian fires being characterised by an increased amount of smouldering combustion due to the large amount of organic soil (peat) burning involved. We find the range of our satellite-derived Indonesian ERs (6.18–13.6 ppb ppm−1) to be relatively closely matched to that of a series of close-to-source, ground-based sampling measurements made on Kalimantan at the height of the fire event (7.53–19.67 ppb ppm−1), although typically the satellite-derived quantities are slightly lower on average. This seems likely because our field sampling mostly intersected smaller-scale peat-burning plumes, whereas the large-scale plumes intersected by the GOSAT Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) footprints would very likely come from burning that was occurring in a mixture of fuels that included peat, tropical forest and already-cleared areas of forest characterised by more fire-prone vegetation types than the natural rainforest biome (e.g. post-fire areas of ferns and scrubland, along with agricultural vegetation).The ability to determine large-scale ERs from satellite data allows the combustion behaviour of very large regions of burning to be characterised and understood in a way not possible with ground-based studies, and which can be logistically difficult and very costly to consider using aircraft observations. We therefore believe the method demonstrated here provides a further important tool for characterising biomass burning emissions, and that the GHG ERs derived for the first time for these large-scale Indonesian fire plumes during an El Niño event point to more routinely assessing spatiotemporal variations in biomass burning ERs using future satellite missions. These will have more complete spatial sampling than GOSAT and will enable the contributions of these fires to the regional atmospheric chemistry and climate to be better understood.

Drained organic soils are a significant source of greenhouse gas (GHG) emissions to the atmosphere. Rewetting these soils may reduce GHG emissions and could also create suitable conditions for return of the carbon (C) sink function characteristic of undrained organic soils. In this article we expand on the work relating to rewetted organic soils that was carried out for the 2014 Intergovernmental Panel on Climate Change (IPCC) Wetlands Supplement. We describe the methods and scientific approach used to derive the Tier 1 emission factors (the rate of emission per unit of activity) for the full suite of GHG and waterborne C fluxes associated with rewetting of organic soils. We recorded a total of 352 GHG and waterborne annual flux data points from an extensive literature search and these were disaggregated by flux type (i.e. CO2, CH4, N2O and DOC), climate zone and nutrient status. Our results showed fundamental differences between the GHG dynamics of drained and rewetted organic soils and, based on the 100 year global warming potential of each gas, indicated that rewetting of drained organic soils leads to: net annual removals of CO2 in the majority of organic soil classes; an increase in annual CH4 emissions; a decrease in N2O and DOC losses; and a lowering of net GHG emissions. Data published since the Wetlands Supplement (n = 58) generally support our derivations. Significant data gaps exist, particularly with regard to tropical organic soils, DOC and N2O. We propose that the uncertainty associated with our derivations could be significantly reduced by the development of country specific emission factors that could in turn be disaggregated by factors such as vegetation composition, water table level, time since rewetting and previous land use history.

Indonesia possesses the third largest tropical forests coverage following Brazilian Amazon and Congo Basin regions. This country, however, suffered from the highest deforestation rate surpassing deforestation in the Brazilian Amazon in 2012. National capacity for forest change assessment and monitoring has been well-established in Indonesia and the availability of national forest inventory data could largely assist the country to report their forest carbon stocks and change over more than two decades. This work focuses for refining forest cover change mapping and deforestation estimate at national scale applying over 10,000 scenes of Landsat scenes, acquired in 1990, 1996, 2000, 2003, 2006, 2009, 2011 and 2012. Pre-processing of the data includes, geometric corrections and image mosaicking. The classification of mosaic Landsat data used multi-stage visual observation approaches, verified using ground observations and comparison with other published materials. There are 23 land cover classes identified from land cover data, presenting spatial information of forests, agriculture, plantations, non-vegetated lands and other land use categories. We estimated the magnitude of forest cover change and assessed drivers of forest cover change over time. Forest change trajectories analysis was also conducted to observe dynamics of forest cover across time. This study found that careful interpretations of satellite data can provide reliable information on forest cover and change. Deforestation trend in Indonesia was lower in 2000-2012 compared to 1990-2000 periods. We also found that over 50% of forests loss in 1990 remains unproductive in 2012. Major drivers of forest conversion in Indonesia range from shrubs/open land, subsistence agriculture, oil palm expansion, plantation forest and mining. The results were compared with other available datasets and we obtained that the MOF data yields reliable estimate of deforestation.

Estimation of belowground carbon stocks in tropical wetland forests requires funding for laboratory analyses and suitable facilities, which are often lacking in developing nations where most tropical wetlands are found. It is therefore beneficial to develop simple analytical tools to assist belowground carbon estimation where financial and technical limitations are common. Here we use published and original data to describe soil carbon density (kgC m−3; Cd) as a function of bulk density (gC cm−3; Bd), which can be used to rapidly estimate belowground carbon storage using Bd measurements only. Predicted carbon densities and stocks are compared with those obtained from direct carbon analysis for ten peat swamp forest stands in three national parks of Indonesia. Analysis of soil carbon density and bulk density from the literature indicated a strong linear relationship (Cd = Bd × 495.14 + 5.41, R2 = 0.93, n = 151) for soils with organic C content > 40%. As organic C content decreases, the relationship between Cd and Bd becomes less predictable as soil texture becomes an important determinant of Cd. The equation predicted belowground C stocks to within 0.92% to 9.57% of observed values. Average bulk density of collected peat samples was 0.127 g cm−3, which is in the upper range of previous reports for Southeast Asian peatlands. When original data were included, the revised equation Cd = Bd × 468.76 + 5.82, with R2 = 0.95 and n = 712, was slightly below the lower 95% confidence interval of the original equation, and tended to decrease Cd estimates. We recommend this last equation for a rapid estimation of soil C stocks for well-developed peat soils where C content > 40%.