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Improving tropical forest current biomass estimates can help more accurately evaluate ecosystem services in tropical forests. The Global Ecosystem Dynamics Investigation (GEDI) lidar provides detailed 3D forest structure and height data, which can be used to improve above‐ground biomass estimates. However, there is still debate on how best to predict tropical forest biomass using GEDI data. Here we compare stand biomass predicted by GEDI data with the observed data of 2,102 inventory plots in tropical forests and find that adding a remotely sensed (RS) trait map of leaf mass area (LMA) significantly (P < 0.001) improves field biomass predictions, but by only a small amount (r2 = 0.01). However, it may also help reduce the bias of the residuals because there was a negative relationship between both LMA (r2 of 0.34) and percentage of phosphorus (%P, r2 = 0.31) and residuals. Leaf spectral data (400–1,075 nm) from 523 individual trees along a Peruvian tropical forest elevation gradient predicted Diameter at Breast height (DBH) (the critical measurement underlying plot biomass) with an r2 = 0.01 and LMA predicts DBH with an r2 = 0.04. Other data sets may offer further improvements and max temperature (Tmax) predicts Amazonian biomass residuals with an r2 of 0.76 (N = 66). Finally, for a network of net primary production (NPP) and gross primary production (GPP) plots (N = 21), leaf traits predicted with remote sensing are better at predicting fluxes than structure variables. Overall, trait maps, especially future improved ones produced by Surface Biology Geology, may improve biomass and carbon flux predictions by a small but significant amount. Plain Language Summary: Improving predictions of tropical forest biomass can help us to fight climate change. In this paper, we tried to improve tropical forest biomass predictions of satellite lidar (Global Ecosystem Dynamics Investigation) by adding remote sensed estimates of leaf traits. Leaf traits like leaf mass area or phosphorus slightly improved predictions of forest biomass with both a ground data set and a remotely sensed data set. Further, remotely sensed trait data could help explain the differences in the prediction of remotely sensed biomass compared with field derived biomass (residuals). Maximum temperature, but not soil fertility, also improved biomass predictions. Remotely sensed leaf traits were better than structure, like tree height, for predicting net primary production (NPP) and gross primary production (GPP). Future improved hyperspectral satellite data may be used to further improve predictions of biomass, NPP and GPP. Key Points: Both field measured and remotely sensed trait data improved biomass predictions, but only by a small amountIn the Amazon region, maximum temperature, but not soil fertility, improved biomass predictionsTrait data was better than structure data for predicting tropical forest net primary production and gross primary production [ABSTRACT FROM AUTHOR]

Invasive species are a leading threat to biodiversity worldwide. Nypa palm (Nypa fruticans) has emerged as the predominant invasive species in the Niger Delta region of Nigeria. While endemic mangroves have high rates of carbon sequestration, stabilize coastlines and protect biodiversity, Nypa does not provide these services outside its native region of Southeast Asia. Oil exploration and urbanization in this region also exacerbate mangrove loss and Nypa spread. As Nypa is difficult to distinguish from endemic mangrove species in remotely sensed data, estimates of mangrove and ecosystem services losses in Nigeria are highly uncertain. Here, we analyse multisensor satellite data with machine learning to quantify the rapid expansion of Nypa from 2015 to 2020 in Nigeria. Using Landsat imagery and random forest classification, we quantify total potential Nypa extent in Nigeria in 2019. We then produced a Nypa extent map using iterative combinations of Sentinel‐1 SAR, Sentinel‐2 MSI and ALOS PALSAR. Random forest classifications using SAR data from ALOS and Sentinel‐1 were best suited for mapping Nypa extent with similar accuracies (78% and 75%, respectively). Based on data availability and accuracy, we focussed our change analysis on Sentinel‐1 SAR. Our results show ~28 000 ha of mangroves were converted to Nypa in Nigeria by 2020 and covered a larger extent than endemic mangroves, compounding the effect of the existing degradation and deforestation in the region. We also compared forest height and complexity estimates from Global Ecosystem Dynamics Investigation LiDAR to further distinguish between endemic mangroves and Nypa in three dimensions. Nypa structural variability, measured by top‐of‐canopy height, vegetation cover, plant area index, and foliage height diversity, was lower than that of mangroves. At current rates of Nypa expansion, the entire area of study would be invaded by Nypa by 2028, with potentially detrimental consequences to the ecosystem services provided by mangroves. [ABSTRACT FROM AUTHOR]

Above‐ground biomass (AGB) is an important metric used to quantify the mass of carbon stored in terrestrial ecosystems. For forests, this is routinely estimated at the plot scale (typically 1 ha) using inventory measurements and allometry. In recent years, terrestrial laser scanning (TLS) has appeared as a disruptive technology that can generate a more accurate assessment of tree and plot scale AGB; however, operationalising TLS methods has had to overcome a number of challenges. One such challenge is the segmentation of individual trees from plot level point clouds that are required to estimate woody volume, this is often done manually (e.g. with interactive point cloud editing software) and can be very time consuming.Here we present TLS2trees, an automated processing pipeline and set of Python command line tools that aims to redress this processing bottleneck. TLS2trees consists of existing and new methods and is specifically designed to be horizontally scalable. The processing pipeline is demonstrated on 7.5 ha of TLS data captured across 10 plots of seven forest types; from open savanna to dense tropical rainforest.A total of 10,557 trees are segmented with TLS2trees: these are compared to 1281 manually segmented trees. Results indicate that TLS2trees performs well, particularly for larger trees (i.e. the cohort of largest trees that comprise 50% of total plot volume), where plot‐wise tree volume bias is ±0.4 m3 and %RMSE is 60%. Segmentation performance decreases for smaller trees, for example where DBH ≤10 cm; a number of reasons are suggested including performance of semantic segmentation step.The volume and scale of TLS data captured in forest plots is increasing. It is suggested that to fully utilise this data for activities such as monitoring, reporting and verification or as reference data for satellite missions an automated processing pipeline, such as TLS2trees, is required. To facilitate improvements to TLS2trees, as well as modification for other laser scanning modes (e.g. mobile and UAV laser scanning), TLS2trees is a free and open‐source software. [ABSTRACT FROM AUTHOR]

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple‐scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing‐based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process‐based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process‐based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) lidar is a multibeam laser altimeter on the International Space Station (ISS). GEDI is the first spaceborne instrument designed to measure vegetation height and to quantify aboveground carbon stocks in temperate and tropical forests and woodlands. This document describes the algorithm theoretical basis underpinning the development of the GEDI Level‐4A (GEDI04_A) footprint aboveground biomass density (AGBD) data product. The GEDI04_A data product contains estimates of AGBD for individual GEDI footprints and associated prediction intervals. The algorithm uses GEDI02_A relative height metrics and 13 linear models to predict AGBD in 32 combinations of plant functional type and world region within the observation limits of the ISS. GEDI04_A models for the release 1 and release 2 data products were developed using 8,587 quality‐filtered simulated GEDI waveforms associated with field estimates of AGBD in 21 countries. Although this is the most geographically comprehensive data available for the development of AGBD models using lidar remote sensing, important regions are underrepresented, including the forests of continental Asia, deciduous broadleaf forests and savannas of the dry tropics, and evergreen broadleaf forests north of Australia. We describe the scientific and statistical assumptions required to develop globally representative estimates of AGBD using GEDI lidar, including generalization beyond training data, and exclusion of GEDI02_A observations that do not meet requirements of the GEDI04_A algorithm. The footprint‐level predictions generated by this process provide globally comprehensive estimates of AGBD. These footprint‐level predictions are a prerequisite for the GEDI04_B gridded AGBD data product. Plain Language Summary: The amount of carbon stored in aboveground vegetation is uncertain. This uncertainty limits our ability to calculate fluxes of carbon between the land surface and the atmosphere, and prevents rigorous carbon offset crediting in forests. Much of this uncertainty is attributed to inconsistent measurement techniques and the use of Earth‐observation methods that were not designed to quantify carbon density. The Global Ecosystem Dynamics Investigation (GEDI) can largely overcome these challenges by producing measurements of vegetation height using a lidar sensor on the International Space Station. This document describes methods developed by the GEDI Science Team to convert spaceborne measurements of vegetation height into estimates of aboveground biomass density. The algorithms depend on the geographic world region and the type of vegetation that is present at a sampled location. For example, evergreen broadleaf forests of the humid tropics in South America and deciduous broadleaf forests of Europe use different algorithms. Statistical models were developed using comprehensive field measurements and simulated GEDI data. This document describes the importance of filtering GEDI data to reduce the impact of measurement artifacts on aboveground biomass predictions. Quality flags and ancillary data contained in the GEDI04_A data product ensure that the best predictions can be used. Key Points: Global Ecosystem Dynamics Investigation (GEDI) aboveground biomass density is from models trained on a comprehensive database of field measurements and simulated GEDI waveformsOn‐orbit prediction requires stratification by plant functional type and world regionQuality flags and metrics distinguish GEDI measurements that are representative of the conditions under which models were developed [ABSTRACT FROM AUTHOR]

Forests contribute to climate change mitigation through carbon storage and uptake, but the extent to which this carbon pool varies in space and time is still poorly known. Several Earth Observation missions have been specifically designed to address this issue, for example, NASA's GEDI, NASA‐ISRO's NISAR and ESA's BIOMASS. Yet, all these missions' products require independent and consistent validation. A permanent, global, in situ, site‐based forest biomass reference measurement system relying on ground data of the highest possible quality is therefore needed. Here, we have assembled a list of almost 200 high‐quality sites through an in‐depth review of the literature and expert knowledge. In this study, we explore how representative these sites are in terms of their coverage of environmental conditions, geographical space and biomass‐related forest structure, compared to those experienced by forests worldwide. This work also aims at identifying which sites are the most representative, and where to invest to improve the representativeness of the proposed system. We show that the environmental coverage of the system does not seem to improve after at least the 175 most representative sites are included, but geographical and structural coverages continue to improve as more sites are added. We highlight the areas of poor environmental, geographical, or structural coverage, including, but not limited to, Canada, the western half of the USA, Mexico, Patagonia, Angola, Zambia, eastern Russia, and tropical and subtropical highlands (e.g. in Colombia, the Himalayas, Borneo, Papua). For the proposed system to succeed, we stress that (1) data must be collected and processed applying the same standards across all countries and continents; (2) system establishment and management must be inclusive and equitable, with careful consideration of working conditions; and (3) training and site partner involvement in downstream activities should be mandatory. [ABSTRACT FROM AUTHOR]

Improving the global monitoring of above‐ground 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.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.TLS‐derived AGB closely agreed with destructive values (bias <1%, concordance correlation coefficient of 98%). However, we identified below‐average performances for smaller trees (<1,000 kg) and conifers. In every individual study, TLS estimates of AGB were less biased and more accurate than those from allometric scaling models (ASMs), especially for larger trees (>1,000 kg).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. [ABSTRACT FROM AUTHOR]

Aim: Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full‐waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness. Location: Tropics. Time period: Present. Major taxa studied: Trees. Methods: First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large‐footprint full‐waveform lidar data (22 m diameter) and related these findings to in‐situ tree species information. Then, we developed structure–richness models at the local (within 25–50 ha plots), regional (biogeographical regions) and pan‐tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression. Results: The results showed a weak structure–richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R2 ranging from.44–.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23–61%]. Modelling the relationship pan‐tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R2 =.39 and RMSD% = 43%, 0.25‐ha resolution). Main conclusions: Our results may serve as a basis for the future development of a set of structure–richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics. [ABSTRACT FROM AUTHOR]

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above‐ground biomass density during its 2‐year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large‐footprint, full‐waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root‐mean‐square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70–85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre‐launch calibration and performance assessment of the GEDI mission. Plain Language Summary: NASA's Global Ecosystem Dynamics Investigation (GEDI) will be the first spaceborne lidar optimized for forest measurement and will produce a range of near‐global forest products. This paper assesses the accuracy of the GEDI simulator, which underpins the pre‐launch calibration of GEDI's data products. Key Points: GEDI's simulator has been validated and found accurate enough for pre‐launch calibration activitiesThe uncertainties of the simulator have been quantified and ALS beam density identified as a sufficient measure of accuracyInteresting quirks of full‐waveform metrics have been highlighted and investigated [ABSTRACT FROM AUTHOR]

Abstract: Understanding the carbon flux of forests is critical for constraining the global carbon cycle and managing forests to mitigate climate change. Monitoring forest growth and mortality rates is critical to this effort, but has been limited in the past, with estimates relying primarily on field surveys. Advances in remote sensing enable the potential to monitor tree growth and mortality across landscapes. This work presents an approach to measure tree growth and loss using multidate lidar campaigns in a high‐biomass forest in California, USA. Individual tree crowns were delineated in 2008 and again in 2013 using a 3D crown segmentation algorithm, with derived heights and crown radii extracted and used to estimate individual tree aboveground biomass. Tree growth, loss, and aboveground biomass were analyzed with respect to tree height and crown radius. Both tree growth and loss rates decrease with increasing tree height, following the expectation that trees slow in growth rate as they age. Additionally, our aboveground biomass analysis suggests that, while the system is a net source of aboveground carbon, these carbon dynamics are governed by size class with the largest sources coming from the loss of a relatively small number of large individuals. This study demonstrates that monitoring individual tree‐based growth and loss can be conducted with multidate airborne lidar, but these methods remain relatively immature. Disparities between lidar acquisitions were particularly difficult to overcome and decreased the sample of trees analyzed for growth rate in this study to 21% of the full number of delineated crowns. However, this study illuminates the potential of airborne remote sensing for ecologically meaningful forest monitoring at an individual tree level. As methods continue to improve, airborne multidate lidar will enable a richer understanding of the drivers of tree growth, loss, and aboveground carbon flux. [ABSTRACT FROM AUTHOR]

Aim Understanding the drivers of forest structure, function and change is a fundamental problem in both theoretical ecology and applied forestry for carbon mapping and monitoring. An important component of forest ecology research often utilizes allometric equations to scale up local measurements to predict large-scale forest and ecosystem-level properties. However, both applied and theoretical allometries in forest ecology (such as metabolic scaling theory, MST) assume that many scaling relationships are insensitive to broad-scale climate gradients or species life histories. We aim to test these assumptions by mapping continental-scale forest allometry across environmental gradients in the United States. Location United States. Methods We fit exponents to two allometric relationships in c. 100,000 Forest Inventory Analysis (FIA) field plots: (1) the relationship between the height of an individual tree and its diameter, and (2) plot-level tree size distributions. We compare fitted exponents to environmental and life-history variables, such as climate, topography and forest structure, in an attempt to explain allometric variability and deviations from theoretically predicted allometries. Results We find that the structural allometry of forests varies strongly as a function of location in the United States. Allometric exponents appear to asymptote at approximately where MST theory predicts with increasing forest height, while deviations from MST are partially explained as a function of environmentally driven recruitment limitations and successional status. Main conclusions While we find support for invariant tree and stand allometric scaling relationships in forests that are in steady state with regard to demography and resources, we also find considerable spatial variability in forest allometric relationships when steady-state conditions are violated. These findings suggest that extensions of metabolic scaling theory should incorporate variation in demographic dynamics in younger successional forests, and factors influencing recruitment limitation. [ABSTRACT FROM AUTHOR]