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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.

The first International Peat Congress (IPC) held in the tropics – in Kuching (Malaysia) – brought together over 1000 international peatland scientists and industrial partners from across the world (“International Peat Congress with over 1000 participants!,” 2016). The congress covered all aspects of peatland ecosystems and their management, with a strong focus on the environmental, societal and economic challenges associated with contemporary large-scale agricultural conversion of tropical peat.

The quantification of tropical forest carbon stocks is a key challenge in creating a basic methodology for REDD (reducing emissions from deforestation and degradation in developing countries) projects. Small-footprint LiDAR (light detection and ranging) systems have proven to successfully correlate to above ground biomass (AGB) estimates in boreal and temperate forests. Their applicability to two different tropical rainforest types (lowland dipterocarp and peat swamp forest) in Central Kalimantan, Indonesia, was tested by developing multiple regression models at plot level using full waveform LiDAR point cloud characteristics. Forest inventory data is barely available for Central Kalimantan's forests. In order to sample a high number of field plots the angle count method was applied which allows fast sampling. More laborious fixed-area plots (three nests of circular shape) were used as a control and approved the use of the angle count method. AGB values, calculated by using existing allometric models, were in the range of 15–547 Mg ha −1 depending on forest type, degradation level and the model used for calculation. As expected, logging resulted in significant AGB losses in all forest types. AGB-prediction models were established for each forest type using statistical values of the LiDAR point clouds and the forest inventory plots. These regression models were then applied to six LiDAR tracks (altogether with a size of 5241 ha) covering unlogged, logged and burned lowland dipterocarp and peat swamp forest. The regression analysis showed that the 45th and 65th percentiles and the standard error of the mean explain 83% of the variation in lowland dipterocarp forest plots ( RMSE = 21.37%). The best model for peat swamp forest could only explain 32% of the AGB variation ( RMSE = 41.02%). Taking both forest types together explained 71% ( RMSE = 33.85%). Calculating AGB for whole LiDAR tracks demonstrated the ability of this approach to quantify not only deforestation but also especially forest degradation and its spatial variability in terms of biomass change in different forest ecosystems using LiDAR transects. Concluding it can be stated that the combined approach of extensive field sampling and LiDAR point cloud analysis have high potential to significantly improve current estimates of carbon stocks across different forest types and degradation levels and its spatial variation in highly inaccessible tropical rainforests in the framework of REDD.

During the 1997/98 El Niño-induced drought peatland fires in Indonesia may have released 13–40% of the mean annual global carbon emissions from fossil fuels. One major unknown in current peatland emission estimations is how much peat is combusted by fire. Using a light detection and ranging data set acquired in Central Kalimantan, Borneo, in 2007, one year after the severe peatland fires of 2006, we determined an average burn scar depth of 0.33 ± 0.18 m. Based on this result and the burned area determined from satellite imagery, we estimate that within the 2.79 million hectare study area 49.15 ± 26.81 megatons of carbon were released during the 2006 El Niño episode. This represents 10–33% of all carbon emissions from transport for the European Community in the year 2006. These emissions, originating from a comparatively small area (approximately 13% of the Indonesian peatland area), underline the importance of peat fires in the context of green house gas emissions and global warming. In the past decade severe peat fires occurred during El Niño-induced droughts in 1997, 2002, 2004, 2006, and 2009. Currently, this important source of carbon emissions is not included in IPCC carbon accounting or in regional and global carbon emission models. Precise spatial measurements of peat combusted and potential avoided emissions in tropical peat swamp forests will also be required for future emission trading schemes in the framework of Reduced Emissions from Deforestation and Degradation in developing countries.

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 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 58-7% 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.

We estimated forest Above Ground Biomass (AGB) of tropical peat swamp forests in the Indonesian province of Central Kalimantan through correlating airborne Light Detection And Ranging (LiDAR) data to forest inventory data. Two LiDAR point cloud metrics, the Quadratic Mean Canopy profile Height (QMCH) and the Centroid Height (CH), were correlated to the field derived AGB estimates. The regression models could be improved through the use of the LiDAR point densities as input. The highest coefficient of determination was achieved for CH (R2= 0.88; n= 52). Surveying with a LiDAR point density between 2-4 points per square meter (pt/m2) resulted in the best cost-benefit ratio. It was also shown that impact from logging and the associated AGB losses dating back more than 10 years could still be assessed by LiDAR but not by multispectral satellite imagery. Comparison with a Landsat based AGB estimate showed an overestimation of 60.8% in a 3.0 million ha study area.