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NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

The Amazon forest contains globally important carbon stocks, but in recent years, atmospheric measurements suggest that it has been releasing more carbon than it has absorbed because of deforestation and forest degradation. Accurately attributing the sources of carbon loss to forest degradation and natural disturbances remains a challenge because of the difficulty of classifying disturbances and simultaneously estimating carbon changes. We used a unique, randomized, repeated, very high-resolution airborne laser scanning survey to provide a direct, detailed, and high-resolution partitioning of aboveground carbon gains and losses in the Brazilian Arc of Deforestation. Our analysis revealed that disturbances directly attributed to human activity impacted 4.2% of the survey area while windthrows and other disturbances affected 2.7% and 14.7%, respectively. Extrapolating the lidar-based statistics to the study area (544,300 km 2 ), we found that 24.1, 24.2, and 14.5 Tg C y -1 were lost through clearing, fires, and logging, respectively. The losses due to large windthrows (21.5 Tg C y -1 ) and other disturbances (50.3 Tg C y -1 ) were partially counterbalanced by forest growth (44.1 Tg C y -1 ). Our high-resolution estimates demonstrated a greater loss of carbon through forest degradation than through deforestation and a net loss of carbon of 90.5 ± 16.6 Tg C y -1 for the study region attributable to both anthropogenic and natural processes. This study highlights the role of forest degradation in the carbon balance for this critical region in the Earth system., Competing Interests: Competing interests statement:The authors declare no competing interest.

Field measurements demonstrate a carbon sink in the Amazon and Congo basins, but the cause of this sink is uncertain. One possibility is that forest landscapes are experiencing transient recovery from previous disturbance. Attributing the carbon sink to transient recovery or other processes is challenging because we do not understand the sensitivity of conventional remote sensing methods to changes in aboveground carbon density (ACD) caused by disturbance events. Here we use ultra-high-density drone lidar to quantify the impact of a blowdown disturbance on ACD in a lowland rain forest in Costa Rica. We show that the blowdown decreased ACD by at least 17.6%, increased the number of canopy gaps, and altered the gap size-frequency distribution. Analyses of a canopy-height transition matrix indicate departure from steady-state conditions. This event will initiate a transient sink requiring an estimated 24-49 years to recover pre-disturbance ACD. Our results suggest that blowdowns of this magnitude and extent can remain undetected by conventional satellite optical imagery but are likely to alter ACD decades after they occur.

Tropical forests vary widely in biomass carbon (C) stocks and fluxes even after controlling for forest age. A mechanistic understanding of this variation is critical to accurately predicting responses to global change. We review empirical studies of spatial variation in tropical forest biomass, productivity and woody residence time, focusing on mature forests. Woody productivity and biomass decrease from wet to dry forests and with elevation. Within lowland forests, productivity and biomass increase with temperature in wet forests, but decrease with temperature where water becomes limiting. Woody productivity increases with soil fertility, whereas residence time decreases, and biomass responses are variable, consistent with an overall unimodal relationship. Areas with higher disturbance rates and intensities have lower woody residence time and biomass. These environmental gradients all involve both direct effects of changing environments on forest C fluxes and shifts in functional composition - including changing abundances of lianas - that substantially mitigate or exacerbate direct effects. Biogeographic realms differ significantly and importantly in productivity and biomass, even after controlling for climate and biogeochemistry, further demonstrating the importance of plant species composition. Capturing these patterns in global vegetation models requires better mechanistic representation of water and nutrient limitation, plant compositional shifts and tree mortality., (© 2020 The Authors New Phytologist © 2020 New Phytologist Foundation.)

Current and planned space missions will produce aboveground biomass density data products at varying spatial resolution. Calibration and validation of these data products is critically dependent on the existence of field estimates of aboveground biomass and coincident remote sensing data from airborne or terrestrial lidar. There are few places that meet these requirements, and they are mostly in the northern hemisphere and temperate zone. Here we summarize the potential for low-altitude drones to produce new observations in support of mission science. We describe technical requirements for producing high-quality measurements from autonomous platforms and highlight differences among commercially available laser scanners and drone aircraft. We then describe a case study using a heavy-lift autonomous helicopter in a temperate mountain forest in the southern Czech Republic in support of calibration and validation activities for the NASA Global Ecosystem Dynamics Investigation. Low-altitude flight using drones enables the collection of ultra-high-density point clouds using wider laser scan angles than have been possible from traditional airborne platforms. These measurements can be precise and accurate and can achieve measurement densities of thousands of points · m -2 . Analysis of surface elevation measurements on a heterogeneous target observed 51 days apart indicates that the realized range accuracy is 2.4 cm. The single-date precision is 2.1-4.5 cm. These estimates are net of all processing artifacts and geolocation errors under fully autonomous flight. The 3D model produced by these data can clearly resolve branch and stem structure that is comparable to terrestrial laser scans and can be acquired rapidly over large landscapes at a fraction of the cost of traditional airborne laser scanning.

The distribution of wind-dispersed seeds around a parent tree depends on diaspore and tree traits, as well as wind conditions and surrounding vegetation. This study of a neotropical canopy tree, Platypodium elegans, explored the extent to which parental variation in diaspore and tree traits explained (1) rate of diaspore descent in still air, (2) distributions of diaspores dispersed from a 40-m tower in the forest, and (3) natural diaspore distributions around the parent tree. The geometric mean rate of descent in still air among 20 parents was highly correlated with geometric mean wing loading(1/2) (r = 0.84). However, diaspore traits and rate of descent predicted less variation in dispersal distance from the tower, although descent rate(-1) consistently correlated with dispersal distance. Measured seed shadows, particularly their distribution edges, differed significantly among six parents (DBH range 62-181 cm) and were best fit by six separate anisotropic dispersal kernels and surveyed fecundities. Measured rate of descent and tree traits, combined in a mechanistic seed dispersal model, did not significantly explain variation among parents in natural seed dispersal distances, perhaps due to the limited power to detect effects with only six trees. Seedling and sapling distributions were at a greater mean distance from the parents than seed distributions; saplings were heavily concentrated at far distances. Variation among parents in the distribution tails so critical for recruitment could not be explained by measured diaspore or tree traits with this sample size, and may be determined more by wind patterns and the timing of abscission in relation to wind conditions. Studies of wind dispersal need to devote greater field efforts at recording the "rare" dispersal events that contribute to far dispersal distances, following their consequences, and in understanding the mechanisms that generate them.

Within boreal and temperate forest ecosystems, the majority of trees and shrubs form beneficial relationships with mutualistic ectomycorrhizal (ECM) fungi that support plant health through increased access to nutrients as well as aiding in stress and pest tolerance. The intimate interaction between fungal hyphae and plant roots results in a new symbiotic "organ" called the ECM root tip. Little is understood concerning the metabolic reprogramming that favors the formation of this hybrid tissue in compatible interactions and what prevents the formation of ECM root tips in incompatible interactions. We show here that the metabolic changes during favorable colonization between the ECM fungus Laccaria bicolor and its compatible host, Populus trichocarpa, are characterized by shifts in aromatic acid, organic acid, and fatty acid metabolism. We demonstrate that this extensive metabolic reprogramming is repressed in incompatible interactions and that more defensive compounds are produced or retained. We also demonstrate that L. bicolor can metabolize a number of secreted defensive compounds and that the degradation of some of these compounds produces immune response metabolites (e.g., salicylic acid from salicin). Therefore, our results suggest that the metabolic responsiveness of plant roots to L. bicolor is a determinant factor in fungus-host interactions.