Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

peer reviewedWood density (WD) relates to important tree functions such as stem mechanics and resistance against pathogens. This functional trait can exhibit high intraindividual variability both radially and vertically. With the rise of LiDAR-based methodologies allowing nondestructive tree volume estimations, failing to account for WD variations related to tree function and biomass investment strategies may lead to large systematic bias in AGB estimations. Here, we use a unique destructive dataset from 822 trees belonging to 51 phylogenetically dispersed tree species harvested across forest types in Central Africa to determine vertical gradients in WD from the stump to the branch tips, how these gradients relate to regeneration guilds and their implications for AGB estimations. We find that decreasing WD from the tree base to the branch tips is characteristic of shade-tolerant species, while light-demanding and pioneer species exhibit stationary or increasing vertical trends. Across all species, the WD range is narrower in tree crowns than at the tree base, reflecting more similar physiological and mechanical constraints in the canopy. Vertical gradients in WD induce significant bias (10%) in AGB estimates when using database-derived species-average WD data. However, the correlation between the vertical gradients and basal WD allows the derivation of general correction models. With the ongoing development of remote sensing products providing 3D information for entire trees and forest stands, our findings indicate promising ways to improve greenhouse gas accounting in tropical countries and advance our understanding of adaptive strategies allowing trees to grow and survive in dense rainforests. © 2020, The Author(s)

LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa

Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth’s carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR)

Toward a general tropical forest biomass prediction model from very high resolution optical satellite images

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Terrestrial laser scanning reveals convergence of tree architecture with increasingly dominant crown canopy position

International audienceTo fulfill their growth and reproductive functions, trees develop a three-dimensional structure that is subject to both internal and external constraints. This is reflected by the unique architecture of each individual at a given time. Addressing the crown dimensions and topological structure of large tropical trees is challenging considering their complexity, size and longevity. Terrestrial laser scanning (TLS) technology offers a new opportunity for characterizing and comparing these properties across a large number of individuals and species. In the present study, we specifically developed topology and geometry metrics of crown architecture from TLS data and investigated how they correlated with metrics of tree and crown form, crown position and shade tolerance. Fifty-nine trees belonging to 14 coexisting canopy species in semideciduous forests of Cameroon were scanned with TLS and reconstructed using quantitative structural models (QSMs). The species belonged to different shade-tolerance groups and were sampled in different crown positions. Crown-form metrics and branch topology metrics were quantified from the TLS data, and principal component analysis (PCA) was used to study how the 59 sampled trees were distributed along axes of architectural diversity. Allometric scaling parameters derived from West Brown and Enquist (WBE) metabolic theory were also quantified from the QSMs, and their correlations with the PCA axes were evaluated. The results revealed that the branch topology and crown-form metrics were not correlated since similar topologies could lead to contrasting crown forms. Crown form, but not branch topology, changed with tree shade tolerance, while convergence in tree topology and towards expected WBE parameters was observed for all trees reaching dominant crown positions independent of species shade tolerance. This convergence is interpreted as resulting from a liberation effect of canopy trees from side-shading constraints, leading to crown development processes through sequential reiteration

LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa

International audienceAccurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth’s carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR)