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Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

The ICESat‐2 and GEDI missions were launched in 2018, becoming the new generation of space‐borne laser altimeters. These missions provide unprecedented global geodetic elevations, opening great opportunities for water level monitoring. The potential of these altimeters has been demonstrated in open‐water environments such as lakes, rivers, and reservoirs. However, detailed evaluations in vegetated environments, such as wetlands, floodplains, and other areas not constrained by water canal networks, are essential for continued improvement and further hydrological application. We developed a systematic accuracy assessment of ICESat‐2 ATL08, and GEDI L2A products to monitor spatial‐temporal water level and depth dynamics over the South Florida Everglades wetlands. The evaluation was performed on data acquired between 2020 and 2021, using gauge‐based water level and depth estimates as references. The results showed an RMSE of 0.17 m (water level) and 0.15 m (water depth) for ICESat‐2 and 0.75 m (water level) and 0.37 m (water depth) for GEDI. The analysis suggested that nighttime acquisitions were more accurate for both missions than daytime ones. The low‐power beams achieved slightly higher accuracies than those of the high‐power beams over the evaluated wetlands. Water level retrieval was more problematic in densely vegetated areas; however, we derived a correction model based on the leaf area index that improved the accuracy by up to 75% for water depth retrievals from GEDI. Furthermore, the analysis provides new insights to understand the potential of the altimeters in monitoring the spatial‐temporal dynamics of water levels in the evaluated wetlands. Plain Language Summary Monitoring water dynamics in wetlands is essential for understanding the ecosystem's functioning. Satellite‐based measurement of wetland water levels is an effective way of studying wetlands hydrology. In recent decades, available data from new satellites has increased, representing an opportunity to advance wetland monitoring. This study evaluated the latest generation space technology—satellite laser altimeter—which uses laser pulses to measure the elevation of land, ice, water, and vegetation on the Earth's surface. We used data from the ICESat‐2 and GEDI laser altimeters to measure water levels over the South Florida Everglades, a wide‐wetland area monitored by a dense ground‐based monitoring network. We evaluated the altimeters' accuracy by comparing them with the ground‐based measurements. Our findings demonstrated a strong agreement between the space‐ and ground‐based water levels, suggesting that altimeters can be used to monitor water levels in various wetlands. However, the altimeter characteristics and wetland vegetation influenced the observation's accuracy. The errors for ICESa‐2 were 0.17 m, and for GEDI were 0.75 m. We proposed a correction that reduced errors by up to 75% for GEDI. In summary, the new laser altimeters provide valuable measurements that can improve our understanding of water dynamics in wetlands worldwide, especially in data‐scarce regions. Key Points We present a systematic accuracy assessment of the ICESat‐2 and GEDI missions for water level and depth retrieval in the Everglades wetlands The study proposes a correction model for the observations of the altimeters based on the leaf area index The results demonstrate the potential of the missions for monitoring the spatial‐temporal dynamics of water level and depth in wetlands

Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems.

Lidar remote sensing has proven to be a powerful tool for estimating ground elevation, canopy height, and additional vegetation parameters, which in turn are valuable information for the investigation of ecosystems. Spaceborne lidar systems, like the Global Ecosystem Dynamics Investigation (GEDI), can deliver these height estimates on a near global scale. This paper analyzes the accuracy of the first version of GEDI ground elevation and canopy height estimates in two study areas with temperate forests in the Free State of Thuringia, central Germany. Digital terrain and canopy height models derived from airborne laser scanning data are used as reference heights. The influence of various environmental and acquisition parameters (e.g., canopy cover, terrain slope, beam type) on GEDI height metrics is assessed. The results show a consistently high accuracy of GEDI ground elevation estimates under most conditions, except for areas with steep slopes. GEDI canopy height estimates are less accurate and show a bigger influence of some of the included parameters, specifically slope, vegetation height, and beam sensitivity. A number of relatively high outliers (around 9–13% of the measurements) is present in both ground elevation and canopy height estimates, reducing the estimation precision. Still, it can be concluded that GEDI height metrics show promising results and have potential to be used as a basis for further investigations.

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.

Forests are widely recognized as critical to combating climate change due to their ability to sequester and store carbon in the form of biomass. In recent years, the combined use of data from ground-based forest inventories and remotely sensed data from light detection and ranging (lidar) has proven useful for large-scale assessment of forest biomass, but airborne lidar is expensive and data acquisition is infeasible for many countries. By contrast, the spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument has collected freely available data for most of the world’s temperate and tropical forests since 2019. GEDI’s biomass products rely on models calibrated with a global network of field plots paired with GEDI waveforms simulated from airborne lidar to predict biomass. While this calibration strategy minimizes spatial and temporal offsets between field measurements and corresponding lidar returns, calibration data are sparse in many regions. Paraguay’s forests are known to be poorly represented in GEDI’s current calibration dataset, and here we demonstrate that local models calibrated opportunistically with on-orbit GEDI data and field surveys from Paraguay’s national forest inventory can be used with GEDI’s statistical estimators of aboveground biomass density (AGBD). We specify a protocol for opportunistically matching GEDI observations with field plots to calibrate a field-to-GEDI biomass model for use in GEDI’s hybrid statistical framework. Country-specific calibration using on-orbit data resulted in relatively accurate and unbiased predictions of footprint-level biomass, and importantly, supported the assumption underlying model-based inference that the model must ‘apply’ to the area of interest. Using a locally calibrated biomass model, we estimate that the mean AGBD in Paraguay is 65.55 Mg ha −1 , which coincides well with the design-based approach employed by the national forest inventory. The GEDI estimates for individual forest strata range from 52.34 Mg ha −1 to 103.88 Mg ha −1 . On average, the standard errors are 47% lower for estimates based on GEDI than the forest inventory, representing a significant gain in precision. Our research demonstrates that GEDI can be used by national forest inventories in countries that seek reliable estimates of AGBD, and that local calibration using existing field plots may be more appropriate in some applications than using GEDI global models, especially in regions where those models are sparsely calibrated.

While Landsat has proved to be effective for monitoring many elements of forest condition and change, the platform has well-documented limitations in measuring forest structure, the vertical distribution of the canopy. This is important because structure determines several key ecosystem functions, including: carbon storage; habitat suitability; and timber volume. Canopy structure is directly measured by LiDAR, and it should be possible to train Landsat structure models at a highly local scale with the dense, global sample of full waveform LiDAR observations collected by NASA’s Global Ecosystem Dynamics Investigation (GEDI). Local models are expected to perform better because: (a) such models may take advantage of localized correlations between structure and canopy surface reflectance; and (b) to the extent that models revert to the mean of the calibration data due to a lack of discrimination, local models will revert to a more representative mean. We tested Landsat-based relative height predictions using a new GEDI asset on Google Earth Engine, described here. Mean prediction error declined by 23% and important prediction biases at the extremes of the range of canopy height dropped as model calibration became more local, minimizing forest structure signal saturation commonly associated with Landsat and other passive optical sensors. Our results suggest that Landsat-based maps of structural variables such as height and biomass may substantially benefit from the kind of local calibration that GEDI’s dense sample of LiDAR data supports.

High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth labels for model training. However, these labels are absent in many regions, and models trained on optical satellite features often exhibit low performance when transferred across geographies. Here we explore the use of NASA’s global ecosystem dynamics investigation (GEDI) spaceborne lidar instrument, combined with Sentinel-2 optical data, for crop type mapping. Using data from three major cropped regions (in China, France, and the United States) we first demonstrate that GEDI energy profiles can reliably distinguish maize, a crop typically above 2 m in height, from crops like rice and soybean that are shorter. We further show that these GEDI profiles provide much more invariant features across geographies compared to spectral and phenological features detected by passive optical sensors. GEDI is able to distinguish maize from other crops within each region with accuracies higher than 84%, and able to transfer across regions with accuracies higher than 82%, compared to 64% for transfer of optical features. Finally, we show that GEDI profiles can be used to generate training labels for models based on optical imagery from Sentinel-2, thereby enabling the creation of 10 m wall-to-wall maps of tall versus short crops in label-scarce regions. As maize is the second most widely-grown crop in the world and often the only tall crop grown within a landscape, we conclude that GEDI offers great promise for improving global crop type maps.

The Global Ecosystem Dynamics Investigation LiDAR (GEDI) is a new full waveform (FW) based LiDAR system that presents a new opportunity for the observation of forest structures globally. The backscattered GEDI signals, as all FW systems, are distorted by topographic conditions within their footprint, leading to uncertainties on the measured forest variables. In this study, we explore how well several approaches based on waveform metrics and ancillary digital elevation model (DEM) data perform on the estimation of stand dominant heights (Hdom) and wood volume (V) across different sites of Eucalyptus plantations with varying terrain slopes. In total, five models were assessed on their ability to estimate Hdom and four models for V. Results showed that the models using the GEDI metrics, such as the height at different energy quantiles with terrain data from the shuttle radar topography mission’s (SRTM) digital elevation model (DEM) were still dependent on the topographic slope. For Hdom, an RMSE increase of 14% was observed for data acquired over slopes higher than 20% in comparison to slopes between 10 and 20%. For V, a 74% increase in RMSE was reported between GEDI data acquired over slopes between 0–10% and those acquired over slopes higher than 10%. Next, a model relying on the height at different energy quantiles of the entire waveform (HTn) and the height at different energy quartiles of the bare ground waveform (HGn) was assessed. Two sets of the HGn metrics were generated, the first one was obtained using a simulated waveform representing the echo from a bare ground, while the second one relied on the actual ground return from the waveform by means of Gaussian fitting. Results showed that both the simulated and fitted models provide the most accurate estimates of Hdom and V for all slope ranges. The simulation-based model showed an RMSE that ranged between 1.39 and 1.66 m (between 26.76 and 39.26 m3·ha−1 for V) while the fitting-based method showed an RMSE that ranged between 1.26 and 1.34 m (between 26.78 and 36.29 m3·ha−1 for V). Moreover, the dependency of the GEDI metrics on slopes was greatly reduced using the two sets of metrics. As a conclusion, the effect of slopes on the 25-m GEDI footprints is rather low as the estimation on canopy heights from uncorrected waveforms degraded by a maximum of 1 m for slopes between 20 and 45%. Concerning the wood volume estimation, the effect of slopes was more pronounced, and a degradation on the accuracy (increased RMSE) of a maximum of 20 m3·ha−1 was observed for slopes between 20 and 45%.

Accurate information on the global distribution and the three-dimensional (3D) structure of Earth’s forests is needed to assess forest biomass stocks and to project the future of the terrestrial Carbon sink. In spite of its importance, the 3D structure of forests continues to be the most crucial information gap in the observational archive. The Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) sensor is providing an unprecedented near-global sampling of tropical and temperate forest structural properties. The integration of GEDI measurements with spatially-contiguous observations from polar orbiting optical satellite data therefore provides a unique opportunity to produce wall-to-wall maps of forests’ 3D structure. Here, we utilized Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data to extrapolate GEDI-derived forest structure attributes into 1-km resolution contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS). The maps were validated using an independent subset of GEDI data. Validation results for TH (r2 = 0.8; RMSE = 3.35 m), CFC (r2 = 0.79; RMSE = 0.09), PAI (r2 = 0.76; RMSE = 0.41), and FHD (r2 = 0.83; RMSE = 0.25) demonstrated the robustness of VIIRS data for extrapolating GEDI measurements across the nation or even over larger areas. The methodology developed through this study may allow multi-decadal monitoring of changes in multiple forest structural attributes using consistent satellite observations acquired by orbiting and forthcoming VIIRS instruments.