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GEDI (Global Ecosystem Dynamics Investigation) is a lidar system on-board the International Space Station designed to study forest ecosystems. However, GEDI footprint low accuracy geolocation is a major impediment to the optimal benefit of the data. We thus proposed a geolocation correction method, GeoGEDI, only based on high-resolution digital terrain models (DTMs) and GEDI derived ground elevations. For each footprint, an error map between GEDI ground estimates and reference DTM was computed, and a flow accumulation algorithm was used to retrieve the optimal footprint position. GeoGEDI was tested on 150 000 footprints in Landes and Vosges, two French forests with various stands and topographic conditions. It was applied to GEDI versions 1 (v1) and 2 (v2), by either a single or four full-power laser beam tracks. GeoGEDI output accuracy was evaluated by analyzing shift distributions and comparing GEDI ground elevations and surface heights to reference data. GeoGEDI corrections were greater for v1 than for v2 and agreed with errors announced by NASA. Within forests, GeoGEDI improved the RMSE of ground elevation in Landes by 26.8 % (0.34 m) and by 13.3 % (0.14 m) for v1 and v2, respectively. For Vosges, ground elevation RMSE improved by 59.6 % (3.82 m) and 36.2 % (1.41 m), for v1 and v2, respectively. Regarding surface heights, except for v2 in Landes, where insufficient variations in topography combined to GEDI ground detection issues might have penalized the adjustment, GeoGEDI improved GEDI estimates. Using GeoGEDI showed efficient to improve positioning bias and precision.

International audience; Highlights:• A new method for LAD estimation from T-LiDAR in occluded areas is developed.• This method use kriging on LAD estimator (LAD-Kriging) and relies on TLS data only.• LAD-Kriging can be applied to all voxels whatever their reliability.• LAD-Kriging retrieves reliable estimates in not-explored and poorly-sampled voxels.• This method can be fitted to other unbiased estimators extracting metrics in point clouds.Abstract:Terrestrial Laser Scanning (TLS) has been used during the past decade to capture the complexity of 3D forest canopy structures, especially Leaf or Plant Area Density (LAD/PAD). TLS data, i.e. point cloud, can be divided into voxels to estimate the three-dimensional distribution of LAD/PAD. However, the combination effects of vegetation occlusion and shooting pattern of TLS scanners lead to a highly heterogeneous sampling, which limits the reliability of some local estimates, since several voxels are either not explored or poorly-explored by laser beams. In practice, recommendations vary regarding the minimum number of beams crossing voxels or the minimum path lengths required to provide reliable predictions. In addition, assigning a value to non-explored and poorly-explored voxels is still an open question. The present work proposes a new method, called LAD-kriging, to mitigate the impact of non-uniform sampling and to increase the accuracy of LAD estimates in non-explored and poorly-explored voxels. The method takes advantage of i) an unbiased LAD estimator of known variance, which was recently developed; ii) the spatial correlation of the LAD field, which derives from vegetation clumping. LAD-kriging computes kriging weights from mathematical derivations, which takes into account both spatial dependencies in the LAD field and the reliability of the estimate available in each voxel. It was evaluated through numerical experiments, which enabled to validate the algorithm and to evaluate its performance through comparison with true references. An example application to field data shows that such spatial correlations truly exist in the field and that LAD-kriging entails to reduce sampling errors (with respect to full resolution scanning). Although a real validation is impossible due to the lack of precise references at the voxel scale, this example allows to gain confidence for its application to field data. In our realistic numerical experiment, up to 25-30% of voxels can be explored by

International audience; Evapotranspiration and energy partitioning are complex to estimate because they result from the interaction of many different processes, especially in multi-species and multi-strata ecosystems. We used MAESPA model, a mechanistic, 3D model of coupled radiative transfer, photosynthesis, and balances of energy and water, to simulate the partitioning of energy and evapotranspiration in homogeneous tree plantations, as well as in heterogeneous multi-species, multi-strata agroforests with diverse spatial scales and management schemes. The MAESPA model was modified to add (1) calculation of foliage surface water evaporation at the voxel scale; (2) computation of an average within-canopy air temperature and vapour pressure; and (3) use of (1) and (2) in iterative calculations of soil and leaf temperatures to close ecosystem-level energy balances. We tested MAESPA model simulations on a simple monospecific Eucalyptus stand in Brazil, and also in two complex, heterogeneous Coffea agroforests in Costa Rica. MAESPA satisfactorily simulated the daily and seasonal dynamics of net radiation (RMSE = 29.6 and 28.4 W m−2; R2 = 0.99 and 0.99 for Eucalyptus and Coffea sites respectively) and its partitioning between latent-(RMSE = 68.1 and 37.2 W m−2; R2 = 0.87 and 0.85) and sensible-energy (RMSE = 54.6 and 45.8 W m−2; R2 = 0.57 and 0.88) over a one-year simulation at half-hourly time-step. After validation, we use the modified MAESPA to calculate partitioning of evapotranspiration and energy between plants and soil in the above-mentioned agro-ecosystems. In the Eucalyptus plantation, 95% of the outgoing energy was emitted as latent-heat, while the Coffea agroforestry system’s partitioning between sensible and latent-heat fluxes was roughly equal. We conclude that MAESPA process-based model has an appropriate balance of detail, accuracy, and computational speed to be applicable to simple or complex forest ecosystems and at different scales for energy and evapotranspiration partitioning.

International audience; Highlights:• Stand-scale estimates of leaf density from voxelized LiDAR data can be biased.• We identified a negative bias caused by the oversampling of light voxels.• Its magnitude depends on location, vegetation structure, scan design and voxel size• Voxel-scale measurement accuracy is too low in small voxels.• 0.5 m voxel size is the best option because corrections are more straightforward.Abstract:The need for fine scale description of vegetation structure is increasing as Leaf Area Density (LAD, m(2)/m(3)) becomes a critical parameter to understand ecosystem functioning and energy and mass fluxes in heterogeneous ecosystems. Terrestrial Laser Scanning (TLS) has shown great potential for retrieving the foliage area at stand, plant or voxel scales. Several sources of measurement errors have been identified and corrected over the past years. However, measurements remain sensitive to several factors, including, 1) voxel size and vegetation structure within voxels, 2) heterogeneity in sampling from TLS instrument (occlusion and shooting pattern), the consequences of which have been seldom analyzed at the scale of forest plots. In the present paper, we aimed at disentangling biases and errors in plot-scale measurements of LAD with TLS in a simulated vegetation scene. Two negative biases were formerly attributed to (i) the unsampled voxels and to (ii) the subgrid vegetation heterogeneity (i.e. clumping effect), and then quantified, thanks to a the simulation experiment providing known LAD references at voxel scale, vegetation manipulations and unbiased point estimators. We used confidence intervals to evaluate voxel-scale measurement accuracy. We found that the unsampled voxel effect (i) led to underestimations with the "mean layer" method -commonly used to fill unsampled voxels- for small voxels (0.1-0.2 m) and/or low number of scans (

Huit ans de travaux de recherche sur les services écosystémiques dans une grande ferme caféière du Costa Rica (observatoire collaboratif Coffee-Flux, en système agroforestier à base de caféiers sous de grands arbres d'Erythrina poeppigiana, surface projetée de couronne de l'ordre de 16 %) ont suggéré plusieurs applications pour les agriculteurs et les décideurs. Il est apparu que de nombreux services écosystémiques dépendaient des propriétés du sol (ici des Andisols), en particulier de l'érosion, de l'infiltration, de la capacité de stockage de l'eau et des éléments nutritifs. Nous confirmons qu'il est essentiel de lier les services hydrologiques et de conservation au type de sol en présence. Une densité adéquate d'arbres d'ombrage (plutôt faible ici) permet de réduire la sévérité des maladies foliaires avec, en perspective, une réduction de l'usage de pesticides-fongicides. Un simple inventaire de la surface basale au collet des caféiers permet d'estimer la biomasse souterraine et la moyenne d'âge d'une plantation de caféiers, ce qui permet d'évaluer sa valeur marchande ou de planifier son remplacement. Le protocole de calcul actuel pour la neutralité carbone des systèmes agroforestiers ne prend en compte que les arbres d'ombrage, pas la culture intercalaire. Dans la réalité, si on inclut les caféiers, on se rapproche très probablement de la neutralité. Des évaluations plus complètes, incluant les arbres, les caféiers, la litière, le sol et les racines dans le bilan en carbone du système agroforestier sont proposées. Les arbres d'ombrage offrent de nombreux servies écosystémiques s'ils sont gérés de manière adéquate dans le contexte local. Par rapport aux parcelles en plein soleil, nous montrons qu'ils réduisent l'érosion laminaire d'un facteur 2, augmentent la fixation de l'azote (N2) atmosphérique et le pourcentage d'azote recyclé dans le système, réduisant ainsi les besoins en engrais. Ils réduisent aussi la sévérité des maladies foliaires, augmentent la séquestration de carbone, améliorent le microclimat et atténuent substantiellement les effets des changements climatiques. Dans notre étude de cas, aucun effet négatif sur le rendement n'a été enregistré., Eight years of studying coffee ecophysiology and monitoring ecosystem services (ES) in a large coffee farm in Costa Rica revealed several practical recommendations for farmers and policy makers. The cropping system studied within our collaborative observatory (Coffee-Flux) corresponds to a coffee-based agroforestry system (AFS) under the shade of large trees of Erythrina poeppigiana (16 % of canopy cover). A lot of ES and disservices depend on local soil properties (here Andisols), especially erosion/infiltration, water/carbon and nutrient storage capacity. Therefore, for ES assessment, the type of soil is crucial. An adequate density of shade trees (rather low here) reduced the severity of leaf diseases with the prospect of reducing pesticide-fungicide use. A simple inventory of the basal area at collar of the coffee plants allowed estimating the belowground biomass and the average age of the plantation, to judge of its market value and to decide when to replace it. Coffee farms are probably much closer to C neutrality than predicted by the current C-Neutral protocol, which only considers shade trees. More comprehensive assessments, including trees, coffee, litter, soil, and roots in the C balance of the AFS are proposed. Shade trees offer many ES if they are adequately managed in the local context. As compared to full sun conditions, shade trees may (i) reduce laminar erosion by a factor of 2, (ii) increase N2 fixation and the % of N recycled into the system, thus reducing fertilizer requirements, (iii) reduce the severity of leaf diseases, (iv) increase C sequestration, (v) improve the microclimate, and (vi) substantially reduce the effects of climate change. In our case study, no negative effect on coffee yield was found.

International audience; Estimating leaf and plant area density with Terrestrial Laser Scanners (TLS) continues to be more and more popular, as tridimensional point clouds appear as an appealing measurement technique for heterogeneous environments. Some approaches implement a discretization of the point cloud in a grid (referred to as “voxelbased”) to account for this vegetation heterogeneity and significant work has been done in this recent research field, but no general theoretical analysis is available. Although estimators have been proposed and several causes of biases have been identified, their unbiasedness (zero bias) and efficiency (smallest error) have not been evaluated. Also, confidence intervals are almost never provided. In the present paper, we assumed that the vegetation elements were randomly distributed within voxels and that TLS beams were infinitely thin, in order to focus on the remaining sources of biases and errors. In this simplified context, we both solve the transmittance equation and use the Maximum Likelihood Estimation (MLE), to derive some new estimators for the attenuation coefficient, which is proportional to leaf area density at voxel scale in this idealized context. These estimators include bias corrections and confidence intervals, and account for the number of beams crossing the voxel (beam number), the inequality of path lengths in voxel, the size of vegetation elements, as well as for the variability of element positions between vegetation samples. These theoretical derivations are complemented by numerous numerical simulations for the evaluation of estimator bias and efficiency, as well as the assessment of the coverage probabilities of confidence intervals. Our simulations reveal that the usual estimators are biased and exhibit 95% confidence intervals on the order of±100% of the estimate, when the beam number is smaller than 30. Second, our bias-corrected estimators -especially the bias-corrected MLE- are truly unbiased and efficient in a wider range of validity than the usual ones, even for beam number as low as 5. Third, we found that the confidence intervals can be as high as ≈ ± 50% when the projected area of a single element was on the order of 10% of voxel cross-sectional area and vegetation was dense (optical depth of the voxel equal to 2), even for a beam number larger than 1000. This is explained by the variability of element positions between vegetation samples, which implies that a significant part of residual error is caused by random effects. When LAD estimates are averaged over several small voxels -typically to determine a vertical profile at plot scale or to compute the total leaf area of a single plant-, confidence intervals are typically on the order of±5 to 10% with bias-corrected estimators, which is twice as small as with usual estimators.Our study provides some new ready-to-use estimators and confidence intervals for attenuation coefficients,which are unbiased and efficient within a fairly large range of parameter values. The unbiasedness is achieved for a fairly low beam number, which is promising for application to airborne LiDAR data. They permit to raise the level of understanding and confidence on LAD estimation. Among other applications, their usage should help determine the most suitable voxel size, for given vegetation types and scanning density, whereas existing guidelines are highly variable among studies, probably because of differences in vegetation, scanning design and estimators. The impact of other sources of biases and errors, such as vegetation heterogeneity inside voxels or TLS specifications are not addressed in the present manuscript and would require further investigations.

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Many agronomic systems could be at risk considering the shorter-term climate changes but several effects and interactions are still uncertain. Process-based models (PBMs) are generally well suited for predictions under new conditions but their complexity causes high computational demand which limits their application. This is particularly true for complex multi-layer or agroforestry systems requiring plant-scale simulations in 3D. An alternative is to summarize their outputs in statistical models called metamodels, which can be used afterwards rapidly in a prediction mode. These metamodels also 1) allow to understand better the processes within the source model (uncertainty and sensitivity analysis) and 2) can be used for coupling different spatial or time scales models. Coffee growth and fruit production are particularly sensitive to high temperatures and water availability, and previous studies often predicts future huge loss of productions or area cover. Nevertheless, shade provided in agroforestry systems could mitigate the effects of climate changes under different management options. In this study, we modeled several management options of coffee agroforestry systems, e.g. shade tree density and species, and the use of a hybrid cultivar of coffee to estimate their suitability and provision of ecosystem services under predicted future climate changes. In Tarrazu (Costa Rica), the models for future climate (AR5, statistically downscaled) predicted an increase of air temperature, while rainfall would remain at similar levels. Two numeric models were coupled using metamodels, a tree scale PBM (MAESPA) for extinction coefficients (diffuse and direct K) and light-use efficiency (LUE) simulations, and a plot scale PBM (GO+) for growth and yield simulations. A rating between management scenarios was then proposed by comparing canopy temperature, coffee and timber yield, carbon balance and water use of past and future coffee growth cycles, under the two contrasted RCP4.5 and 8.5 future climatic scenarios. First results show that management could strongly impact coffee canopy temperature, light use efficiency, water use efficiency and photosynthesis, which are the main drivers of coffee plant fitness and thus coffee beans production potential. Figure 1 shows that coffee grown under shade trees had reduced mean canopy temperature (-1.22°C*** and -2.21°C*** for E. poeppigiana and C. alliodora respectively) compared to full sun management, while showing a moderate reduction of net photosynthesis (-21.6%*** and -17.8%*** resp.) thanks to an increased light use efficiency (+23.4%*** and +26.2%*** resp). Also, Coffee is particularly sensitive to maximum daily temperature, and results showed that in near future (2030-2049), all C. alliodora densities (i.e. 50-75-100-125 Trees.ha-1) and E. poeppigiana 350 Trees.ha-1 density will be able to significantly reduce the maximum daily canopy temperature (down to -6.83°C*** in average for C. alliodora with 125 Trees.ha-1) compared to full sun plantations on the same period (Figure 2). Moreover, simulations showed negative maximum temperature effect on assimilation was present under full sun management only, and eventually largely compensated by positive [CO2] effect (Figure 3). These findings could drive climate change adaptations options, as the scenarios made from the simulations will now be presented to stakeholders to assess their adoptability according to their performance. Figure 1. Shade effect (average for all shade tree densities) on -from left to right- coffee canopy temperature, canopy net assimilation and light use efficiency. C. alliodora and E. poeppigiana densities range: 50 to 125 and 200 to 400 Trees.ha-1 respectively. Results from MAESPA simulations. Figure 2. Mitigation of maximum daily coffee canopy temperature by shade tree density (simulations for the period between 2030 and 2049). Figure 3. Effects of maximum daily canopy temperature and CO2 compensation interaction on coffee canopy assimilation and coffee canopy respiration under three shade management.