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Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

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 AG

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.

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 AG

Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD—that forest stands with different AGBD may produce highly similar waveforms—and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non-uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7–411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25ha scale from field measurements made in permanent research plots across the world's forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities. Live, most up-to-date dataset is available at https://forest-observation-system.net

Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

Several remote sensing missions will soon produce detailed carbon maps over all terrestrial ecosystems. These missions are dependent on accurate and representative in situ datasets for the training of their algorithms and product validation. However, long-term ground-based forest-monitoring systems are limited, especially in the tropics, and to be useful for validation, such ground-based observation systems need to be regularly revisited and maintained at least over the lifetime of the planned missions. Here we propose a strategy for a coordinated and global network of in situ data that would benefit biomass remote sensing missions. We propose to build upon existing networks of long-term tropical forest monitoring. To produce accurate ground-based biomass estimates, strict data quality must be guaranteed to users. It is more rewarding to invest ground resources at sites where there currently is assurance of a long-term commitment locally and where a core set of data is already available. We call these ‘supersites’. Long-term funding for such an inter-agency endeavour remains an important challenge, and we here provide costing estimates to facilitate dialogue among stakeholders. One critical requirement is to ensure in situ data availability over the lifetime of remote sensing missions. To this end, consistent guidelines for supersite selection and management are proposed within the Forest Observation System, long-term funding should be assured, and principal investigators of the sites should be actively involved.

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha-1 vs. 21 and 28 Mg ha-1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

Allometric equations are currently used to estimate above-ground biomass (AGB) based on the indirect relationship with tree parameters. Terrestrial laser scanning (TLS) can measure the canopy structure in 3D with high detail. In this study, we develop an approach to estimate AGB from TLS data, which does not need any prior information about allometry. We compare these estimates against destructively harvested AGB estimates and AGB derived from allometric equations. We also evaluate tree parameters, diameter at breast height (DBH) and tree height, estimated from traditional field inventory and TLS data. Tree height, DBH and AGB data are collected through traditional forest inventory, TLS and destructive sampling of 65 trees in a native Eucalypt Open Forest in Victoria, Australia. Single trees are extracted from the TLS data and quantitative structure models are used to estimate the tree volume directly from the point cloud data. AGB is inferred from these volumes and basic density information and is then compared with the estimates derived from allometric equations and destructive sampling. AGB estimates derived from TLS show a high agreement with the reference values from destructive sampling, with a concordance correlation coefficient (CCC) of 0·98. The agreement between AGB estimates from allometric equations and the reference is lower (CCC = 0·68–0·78). Our TLS approach shows a total AGB overestimation of 9·68% compared to an underestimation of 36·57–29·85% for the allometric equations. The error for AGB estimates using allometric equations increases exponentially with increasing DBH, whereas the error for AGB estimates from TLS is not dependent on DBH. The TLS method does not rely on indirect relationships with tree parameters or calibration data and shows better agreement with the reference data compared to estimates from allometric equations. Using 3D data also enables us to look at the height distributions of AGB, and we demonstrate that 80% of the AGB at plo

Within the global forest area, a diverse range of forest types exist with each supporting varying amounts of biomass and allocations to different plant components. At country to continental scales, remote sensing techniques have been progressively developed to quantify the above-ground biomass (AGB) of these forests, with these based on optical, radar, and/or light detection and ranging (LiDAR) (airborne and spaceborne) data. However, none have been found to be globally applicable at high (≤30 m) resolution, largely because of different forest structures (e.g., heights, covers, allocations of AGB) and varying environmental conditions (e.g., frozen, inundated). For this reason, techniques have varied between the major forest biomes. However, when combined, these estimates provide some insight into the distribution of AGB at country to global levels with associated levels of uncertainty. Comparisons of data and derived products have, in some cases, also contributed to our understanding of changes in carbon stocks across large areas. Further improvements in estimates are anticipated with the launch of new spaceborne LiDAR and SAR that have been specifically designed for better retrieval of forest structure and AGB.

© 2020 John Wiley & Sons Ltd 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

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 AG