Abstract
Tropical forests play an important role in the global carbon cycle. High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. Here, we develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. This detailed interpretation of remote sensing data improves estimates of forest attributes by 20–43% as compared to (conventional) estimates using mean canopy height. The inclusion of forest modeling has a high potential to close a missing link between remote sensing measurements and the 3D structure of forests, and may thereby improve continent-wide estimates of biomass and productivity.
Highlights
Tropical forests play an important role in the global carbon cycle
The approach is used to address the two primary questions: (1) How much information about forests (AGB, stem volume (SV), basal area (BA), gross primary productivity (GPP), aboveground woody productivity (AWP)) can be derived from full lidar profiles? (2) Can we reduce the uncertainties in estimates of forest attributes when using entire profiles as compared with using solely mean canopy height (MCH)? Among all forest attributes tested, we find the highest uncertainties for aboveground biomass (AGB) estimations
We used the coefficient of variation of the probability distribution of forest attributes to define an uncertainty index ε (Fig. 1; (3))
Summary
High-resolution remote sensing techniques, e.g., spaceborne lidar, can measure complex tropical forest structures, but it remains a challenge how to interpret such information for the assessment of forest biomass and productivity. We develop an approach to estimate basal area, aboveground biomass and productivity within Amazonia by matching 770,000 GLAS lidar (ICESat) profiles with forest simulations considering spatial heterogeneous environmental and ecological conditions. This allows for deriving frequency distributions of key forest attributes for the entire Amazon. Assessing forest biomass with lidar confronts several challenges These arise, among others, from the fact that the interpretation of lidar remote sensing measurements is based on statistical relations (e.g., height to biomass) that are derived from field inventory data. Biomass maps derived from remote sensing mainly build on a general pan-topical relation between AGB and a lidar height metric it is known that these can vary for different regions, e.g., due to variations in tree wood densities[19,20,21]
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