the current spatial variation of biomass in the Amazon rain forest is a
challenge and remains a source of substantial uncertainty in the
assessment of the global carbon cycle. Precise estimates need to
consider small-scale variations of forest structures resulting from
local disturbances, on the one hand, and require large-scale information
on the state of the forest that can be detected by remote sensing, on
the other hand. In this study, we introduce a novel method that links a
forest gap model and a canopy height map to derive the biomass
distribution of the Amazon rain forest.
individual-based forest model was applied to estimate the variation of
aboveground biomass across the Amazon rain forest. The forest model
simulated individual trees; hence, it allowed the direct comparison of
simulated and observed canopy heights from remote sensing. The
comparison enabled the detection of disturbed forest states and the
derivation of a simulation-based biomass map at 0.16 ha resolution.
Simulated biomass values ranged from 20 to 490 t (dry mass)/ha across 7.8 Mio km2
of Amazon rain forest. We estimated a total aboveground biomass stock
of 76 GtC, with a coefficient of variation of 45%. We found mean
differences of only 15% when comparing biomass values of the map with
114 field inventories. The forest model enables the derivation of
additional estimates, such as basal area and stem density.
a canopy height map with an individual-based forest model captures the
spatial variation of biomass in the Amazon rain forest at high
resolution. The study demonstrates how this linkage allows for
quantifying the spatial variation in forest structure caused by
tree-level to regional-scale disturbances. It thus provides a basis for
large-scale analyses on the heterogeneous structure of tropical forests
and their carbon cycle.