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These measurements indicate that at the plot-level old-growth forests in Amazonia and Africa have apparently gained carbon over the last 30 years. The extrapolation of regional trends from a relatively small number of ~1-ha-sized plots has been questioned because potentially undersampled natural disturbances at the landscape-scale could counterbalance tree level growth. In (b,d) solid lines correspond to the case where large blow-downs are included only in the Central Amazon while the dashed lines correspond to the case where largest blow-downs are assumed to occur everywhere in the region (as a sensitivity study) and similarly the dashed light blue line corresponds to the case where also floodplain lidar data with river-driven disturbances are included (note that the forest plot network is based overwhelmingly on non-floodplain plots).
We address this question using a simple stochastic forest simulator based on growth statistics from the forest census network and the new regional disturbance size-frequency distribution scaled to all Amazon forest regions. 25) (yellow dots, n=279 blow-downs) underlain by an ABG map of the Amazon. White, yellow and turquoise in (a) indicate the Brazilian border, the mosaic of Landsat images in the Central Amazon, respectively. 23) with ground gaps (yellow polygons, n=55) overlain on a high-resolution IKONOS-2 image acquired in 2008 in the Eastern Amazon. 23) with ground gaps (n=51) over a second high-resolution IKONOS-2 image acquired in 2009. (f) Location of the lidar airborne campaigns in the Southern Peruvian Amazon in erosional terra firme and depositional forests). created the stochastic simulator, ran the simulations and produced the regional frequency and return interval distributions. (h) Details of the detection of gaps in lidar canopy height model (CHM)—a 2 m height threshold was used to detect tree-fall gaps in CHM (h).