ALBERT

Description: 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 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.

Description: 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.

Description: Massive sediment pulses in catchments are a key alpine multi-risk component. Substantial sediment redistribution in alpine catchments frequently causes flooding, river erosion, and landsliding and affects infrastructure such as dam reservoirs as well as aquatic ecosystems and water quality. While systematic rock slope failure inventories have been collected in several countries, the subsequent cascading sediment redistribution is virtually unaccessed. For the first time, this contribution reports the massive sediment redistribution triggered by the multi-stage failure of more than 130 000 m3 from the Hochvogel dolomite peak during the summer of 2016. We applied change detection techniques to seven 3D-coregistered high-resolution true orthophotos and digital surface models (DSMs) obtained through digital aerial photogrammetry later optimized for precise volume calculation in steep terrain. The analysis of seismic information from surrounding stations revealed the temporal evolution of the cliff fall. We identified the proportional contribution of 〉 600 rockfall events (〉 1 m3) from four rock slope catchments with different slope aspects and their volume estimates. In a sediment cascade approach, we evaluated erosion, transport, and deposition from the rock face to the upper channelized erosive debris flow channel, then to the widened dispersive debris flow channel, and finally to the outlet into the braided sediment-supercharged Jochbach river. We observe the decadal flux of more than 400 000 m3 of sediment, characterized by massive sediment waves that (i) exhibit reaction times of 0–4 years in response to a cliff fall sediment input and relaxation times beyond 10 years. The sediment waves (ii) manifest with faster response times of 0–2 years in the upper catchment and over 2 years in the lower catchments. The entire catchment (iii) undergoes a rapid shift from sedimentary (102–103 mm a−1) to massive erosive regimes (102 mm a−1) within single years, and the massive sediment redistribution (iv) shows limited dependency on rainfall frequency and intensity. This study provides generic information on spatial and temporal patterns of massive sediment pulses in highly sediment-charged alpine catchments.