Closing a gap in tropical forest biomass estimation : taking crown mass variation into account in pantropical allometries

Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees = 45 Mg. This increase coincided with a progressive deviation between the pantropical biomass model estimations and actual tree mass. Taking a crown mass proxy into account in a newly developed model consistently removed the bias observed for large trees (> 1 Mg) and reduced the range of plot- level error (in %) from [-23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far- from- negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost

Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries

Accurately monitoring tropical forest carbon stocks is a challenge that remains outstanding. Allometric models that consider tree diameter, height and wood density as predictors are currently used in most tropical forest carbon studies. In particular, a pantropical biomass model has been widely used for approximately a decade, and its most recent version will certainly constitute a reference model in the coming years. However, this reference model shows a systematic bias towards the largest trees. Because large trees are key drivers of forest carbon stocks and dynamics, understanding the origin and the consequences of this bias is of utmost concern. In this study, we compiled a unique tree mass data set of 673 trees destructively sampled in five tropical countries (101 trees > 100 cm in diameter) and an original data set of 130 forest plots (1 ha) from central Africa to quantify the prediction error of biomass allometric models at the individual and plot levels when explicitly taking crown mass variations into account or not doing so. We first showed that the proportion of crown to total tree aboveground biomass is highly variable among trees, ranging from 3 to 88 %. This proportion was constant on average for trees  1 Mg) and reduced the range of plot-level error (in %) from [−23; 16] to [0; 10]. The disproportionally higher allocation of large trees to crown mass may thus explain the bias observed recently in the reference pantropical model. This bias leads to far-from-negligible, but often overlooked, systematic errors at the plot level and may be easily corrected by taking a crown mass proxy for the largest trees in a stand into account, thus suggesting that the accuracy of forest carbon estimates can be significantly improved at a minimal cost.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Copernicus Publications on behalf of the European Geosciences Union. The published article can be found at: http://www.biogeosciences.net

Terrestrial LiDAR point cloud dataset of cocoa trees grown in agroforestry systems in Cameroon

International audienceThis paper presents a dataset aimed at characterizing cocoa trees cultivated within complex agroforestry systems managed by smallholder farmers in the Central region of Cameroon. The dataset highlights the architectural structure of the trees as well as the distribution of their leaves and wood using 3D point clouds obtained through the Leica ScanStation C10 terrestrial LiDAR. The data collection campaign was conducted in August 2019 in the district of Bokito (latitude 4°34′ N and longitude 11°07′ E), specifically within the village of Yorro located in a transition zone between forest and savannah. The dataset includes information on 55 cocoa trees, spread over five distinct architectural types. These trees were sampled from various age stands ranging from 5- to 70-year-old. For 29 of these trees, a leaf/wood segmentation of the point clouds was performed. For each of these trees, the dataset comprises the raw point cloud of the entire tree, as well as separate point clouds for the leaves and wood, each in two distinct sets of 3D points. The data provides the foundation for conducting numerous cocoa tree measurements based on their representation in point clouds, allowing for a more comprehensive understanding of their architecture, photosynthetic capacity, and distribution of above-ground biomas

LeWoS: A Universal Leaf-wood Classification Method to Facilitate the 3D Modelling of Large Tropical Trees Using Terrestrial LiDAR

Leaf-wood separation in terrestrial LiDAR data is a prerequisite for non-destructively estimating biophysical forest properties such as standing wood volumes and leaf area distributions. Current methods have not been extensively applied and tested on tropical trees. Moreover, their impacts on the accuracy of subsequent wood volume retrieval were rarely explored. We present LeWoS, a new fully automatic tool to automate the separation of leaf and wood components, based only on geometric information at both the plot and individual tree scales. This data-driven method utilizes recursive point cloud segmentation and regularization procedures. Only one parameter is required, which makes our method easily and universally applicable to data from any LiDAR technology and forest type. We conducted a twofold evaluation of the LeWoS method on an extensive dataset of 61 tropical trees. We first assessed the point-wise classification accuracy, yielding a score of 0.91 ± 0.03 in average. Second, we evaluated the impact of the proposed method on 3D tree models by cross-comparing estimates in wood volume and branch length with those based on manually separated wood points. This comparison showed similar results, with relative biases of less than 9% and 21% on volume and length respectively. LeWoS allows an automated processing chain for non-destructive tree volume and biomass estimation when coupled with 3D modelling methods. The average processing time on a laptop was 90s for 1 million points. We provide LeWoS as an open-source tool with an end-user interface, together with a large dataset of labelled 3D point clouds from contrasting forest structures. This study closes the gap for stand volume modelling in tropical forests where leaf and wood separation remain a crucial challenge. © 2019 British Ecological SocietyPeer reviewe

Domestication Syndrome in Dacryodes edulis (Burseraceae): Comparison of Morphological and Biochemical Traits between Wild and Cultivated Populations

International audienceFor millennia, people have harvested fruits from the wild for their alimentation. Gradually, they have started selecting wild individuals presenting traits of interest, protecting and cultivating them. This was the starting point of their domestication. The passage from a wild to a cultivated status is accompanied by a modification of a number of morphological and genetic traits, commonly known as the domestication syndrome. We studied the domestication syndrome in Dacryodes edulis (G.Don) H.J.Lam (known as ‘African plum’ or ‘safoutier/prunier’), a socio-economically important indigenous fruit tree species in West and Central Africa. We compared wild and cultivated individuals for their sex distribution; flower, fruit and seed morphometric characteristics; seed germination temporal dynamic and fruit lipid composition. We found a higher percentage of male and male-hermaphrodite sexual types in wild populations than in cultivated ones; a lower fruit and seed mass in wild individuals; and similar mean time of germination, oil content and fatty acid composition between wild and cultivated individuals. Our results are interpreted in light of the presence of a domestication syndrome in D. eduli

Data from: Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: a comparison with traditional destructive approach

1. Calibration of local, regional or global allometric equations to estimate biomass at the tree level constitutes a significant burden on projects aiming at reducing Carbon emissions from forest degradation and deforestation. The objective of this contribution is to assess the precision and accuracy of Terrestrial Laser Scanning (TLS) for estimating volumes and above-ground biomass (AGB) of the woody parts of tropical trees, and for the calibration of allometric models. 2. We used a destructive dataset of 61 trees, with diameters and AGB of up to 186.6 cm and 60 Mg respectively, which were scanned, felled and weighed in the semi-deciduous forests of eastern Cameroon. We present an operational approach based on available software allowing the retrieving of TLS volume with low bias and high accuracy for large tropical trees. Edition of the obtained models proved necessary, mainly to account for the complexity of buttressed parts of tree trunks, which were separately modelled through a meshing approach, and to bring a few corrections in the topology and geometry of branches, thanks to the amapstudio-scan software. 3. Over the entire dataset, TLS-derived volumes proved highly reliable for branches larger than 5 cm in diameter. The volumes of the remaining woody parts estimated for stumps, stems and crowns as well as for the whole tree proved very accurate (RMSE below 2.81% and R² above of .98) and unbiased. Once converted into AGB using mean local-specific wood density values, TLS estimates allowed calibrating a biomass allometric model with coefficients statistically undistinguishable from those of a model based on destructive data. The Unedited Quantitative Structure Model (QSM) however leads to systematic overestimations of woody volumes and subsequently to significantly different allometric parameters. 4. We can therefore conclude that a non-destructive TLS approach can now be used as an operational alternative to traditional destructive sampling to build the allometric equations, although attention must be paid to the quality of QSM model adjustments to avoid systematic bias

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

Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them

International audienceForest biomass monitoring is at the core of the research agenda due to the critical importance of forest dynamics in the carbon cycle. However, forest biomass is never directly measured; thus, upscaling it from trees to stand or larger scales (e.g., countries, regions) relies on a series of statistical models that may propagate large errors. Here, we review the main steps usually adopted in forest aboveground biomass mapping, highlighting the major challenges and perspectives. We show that there is room for improvement along the scaling-up chain from field data collection to satellite-based large-scale mapping, which should lead to the adoption of effective practices to better control the propagation of errors. We specifically illustrate how the increasing use of emerging technologies to collect massive amounts of high-quality data may significantly improve the accuracy of forest carbon maps. Furthermore, we discuss how sources of spatially structured biases that directly propagate into remote sensing models need to be better identified and accounted for when extrapolating forest carbon estimates, e.g., through a stratification design. We finally discuss the increasing realism of 3D simulated stands, which, through radiative transfer modelling, may contribute to a better understanding of remote sensing signals and open avenues for the direct calibration of large-scale products, thereby circumventing several current difficulties

Estimating forest aboveground biomass with terrestrial laser scanning: current status and future directions

1 Improving the global monitoring of aboveground biomass (AGB) is crucial for forest management to be effective in climate mitigation. In the last decade, methods have been developed for estimating AGB from terrestrial laser scanning (TLS) data. TLS-derived AGB estimates can address current uncertainties in allometric and Earth observation (EO) methods that quantify AGB. 2 We assembled a global dataset of TLS scanned and consecutively destructively measured trees from a variety of forest conditions and reconstruction pipelines. The dataset comprised 391 trees from 111 species with stem diameter ranging 8.5 to 180.3 cm and AGB ranging 13.5 – 43,950 kg. 3 TLS-derived AGB closely agreed with destructive values (bias 1,000 kg). 4 More effort should go to further understanding and constraining several TLS error sources. We currently lack an objective method of evaluating point cloud quality for tree volume reconstruction, hindering the development of reconstruction algorithms and presenting a bottleneck for tracking down the error sources identified in our synthesis. Since quantifying AGB with TLS requires only a fraction of the efforts as compared to destructive harvesting, TLS-calibrated ASMs can become a powerful tool in AGB upscaling. TLS will be critical for calibrating/validating scheduled and launched remote sensing initiatives aiming at global AGB mapping