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 LiDAR (Light Detection and Ranging) dataset was used to develop a pre-fire peat surface modeling 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 206 t ha -1 for initial fires, reducing to 115 t ha -1 for second, 69 t ha -1 for third and 23 t ha -1 for successive fires, which are 58% to 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 13 t C ha -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.

Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height

Quantification of tropical forest above-ground biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne light detection and ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed, and regression models could be improved through the use of LiDAR point densities as input (R² = 0.88; n = 52). Surveying with a LiDAR point density per square metre of about 4 resulted in the best cost / benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site-specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 43%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong greenhouse gas (GHG) emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments

Indonesian peatlands are one of the largest near-surface pools of terrestrial organic carbon. Persistent logging, drainage and recurrent fires lead to huge emission of carbon each year. Since tropical peatlands are highly inaccessible, few measurements on peat depth and forest biomass are available. We assessed the applicability of quality filtered ICESat/GLAS (a spaceborne LiDAR system) data to measure peatland topography as a proxy for peat volume and to estimate peat swamp forest Above Ground Biomass (AGB) in a thoroughly investigated study site in Central Kalimantan, Indonesia. Mean Shuttle Radar Topography Mission (SRTM) elevation was correlated to the corresponding ICESat/GLAS elevation. The best results were obtained from the waveform centroid (R 2 = 0.92; n = 4,186). ICESat/GLAS terrain elevation was correlated to three 3D peatland elevation models derived from SRTM data (R 2 = 0.90; overall difference = −1.0 m, ±3.2 m; n = 4,045). Based on the correlation of in situ peat swamp forest AGB and airborne LiDAR data (R 2 = 0.75, n = 36) an ICESat/GLAS AGB prediction model was developed (R 2 = 0.61, n = 35). These results demonstrate that ICESat/GLAS data can be used to measure peat topography and to collect large numbers of forest biomass samples in remote and highly inaccessible peatland forests. OPEN ACCESSRemote Sens. 2011Sens. , 3 1958

Quantification of tropical forest Above Ground Biomass (AGB) over large areas as input for Reduced Emissions from Deforestation and forest Degradation (REDD+) projects and climate change models is challenging. This is the first study which attempts to estimate AGB and its variability across large areas of tropical lowland forests in Central Kalimantan (Indonesia) through correlating airborne Light Detection and Ranging (LiDAR) to forest inventory data. Two LiDAR height metrics were analysed and regression models could be improved through the use of LiDAR point densities as input (<i>R</i>² = 0.88; <i>n</i> = 52). Surveying with a LiDAR point density per square meter of 2–4 resulted in the best cost-benefit ratio. We estimated AGB for 600 km of LiDAR tracks and showed that there exists a considerable variability of up to 140% within the same forest type due to varying environmental conditions. Impact from logging operations and the associated AGB losses dating back more than 10 yr could be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat classification for a 1 million ha study area where AGB values were based on site specific field inventory data, regional literature estimates, and default values by the Intergovernmental Panel on Climate Change (IPCC) showed an overestimation of 46%, 102%, and 137%, respectively. The results show that AGB overestimation may lead to wrong GHG emission estimates due to deforestation in climate models. For REDD+ projects this leads to inaccurate carbon stock estimates and consequently to significantly wrong REDD+ based compensation payments