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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).
or 1.15% of total forest area, where ~98.7% is from small-scale mortality, ~1.1% is from intermediate and only 0.3% from large disturbances.
We synthesize and characterize the frequency distribution of natural disturbance at all spatial scales across forests of the Amazon region using a combination of forest censuses, airborne lidar and passive optical remote sensing from satellites (Fig. We ask whether the net biomass gains inferred from forest census data are an artefact of the small size and limited number of plots in the plot network.
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.
Intermediate disturbances have a notable effect on the mean but relatively small effect on the variance.
In contrast, large disturbances have no perceptible effect on the mean but greatly increase the variance.
For this purpose we use a stochastic forest growth simulator of the form d M=G × dt–D × dt, where d M is aboveground forest biomass loss in units of carbon per area, dt a time interval, here 1 year, and G and D stochastic variables distributed according to the observed distributions of aboveground mass gain due to growth (G) and loss (D) due to mortality equivalent annual observations of each scenario and statistical significance of the results is assessed using a t-test (Table 1).
(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).
Bin-widths are chosen such as to include at least one event; the number density follows approximately Δlog (number of occurrences)/Δlog (disturbance size)≈−2.5.
(b) Return intervals versus severity of events calculated using the inverse of the cumulative PDF (see Methods) for various combinations of the data from repeated plot measurements, lidar surveys and Landsat imagery.
The scenario that we consider to be most realistic for the whole Amazon region is marked bold in Table 1.