No abstract

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

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

No abstract