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Earth Observation data are uniquely positioned to estimate forest aboveground biomass density (AGBD) in accordance with the United Nations Framework Convention on Climate Change (UNFCCC) principles of ‘transparency, accuracy, completeness, consistency and comparability’. However, the use of space-based AGBD maps for national-level reporting to the UNFCCC is nearly non-existent as of 2023, the end of the first global stocktake (GST). We conduct an evidence-based comparison of AGBD estimates from the NASA Global Ecosystem Dynamics Investigation and ESA Climate Change Initiative, describing differences between the products and National Forest Inventories (NFIs), and suggesting how science teams must align efforts to inform the next GST. Between the products, in the tropics, the largest differences in estimated AGBD are primarily in the Congolese lowlands and east/southeast Asia. Where NFI data were acquired (Peru, Mexico, Lao PDR and 30 regions of Spain), both products show strong correlation to NFI-estimated AGBD, with no systematic deviations. The AGBD-richest stratum of these, the Peruvian Amazon, is accurately estimated in both. These results are remarkably promising, and to support the operational use of AGB map products for policy reporting, we describe targeted ways to align products with Intergovernmental Panel on Climate Change (IPCC) guidelines. We recommend moving towards consistent statistical terminology, and aligning on a rigorous framework for uncertainty estimation, supported by the provision of open-science codes for large-area assessments that comprehensively report uncertainty. Further, we suggest the provision of objective and open-source guidance to integrate NFIs with multiple AGBD products, aiming to enhance the precision of national estimates. Finally, we describe and encourage the release of user-friendly product documentation, with tools that produce AGBD estimates directly applicable to the IPCC guideline methodologies. With these steps, space agencies can convey a comparable, reliable and consistent message on global biomass estimates to have actionable policy impact.

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.

Spaceborne LiDAR is essential for accurate forest canopy height measurement, which is crucial for assessing forest biomass and carbon storage. Since its launch in 2018, the Global Ecosystem Dynamics Investigation (GEDI) has been focused on estimating forest canopy parameters. However, traditional decomposition methods for GEDI waveform data often face challenges in complex terrain, which can reduce accuracy. This study introduces a composite Gaussian model decomposition (CGMD) method to improve canopy height estimation of the selected forest farm. The results showed that: (1) Canopy height model (CHM) resolution had minimal impact compared to data quality and buffer settings; (2) The proposed CGMD method consistently outperformed traditional methods, with RMSE reductions of 8.2% to 33.8%; (3) A two-sample t-test confirmed a statistically significant improvement (p < 0.05); (4) Further application of this method to another study area for verification confirmed its robustness and generalizability. The CGMD consistently outperformed the traditional decomposition methods, with RMSE improvement ranging from 12.9% to 23.6%. These results indicate that the CGMD offers a promising approach for enhancing canopy height estimation accuracy, particularly in complex terrains.

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%).

The goal of this study is to assess the accuracy of ground elevation estimates derived from data collected by the Global Ecosystem Dynamics Investigation (GEDI) full-waveform satellite LiDAR sensor, for a study area of approximately 246 km2 located in northwestern Romania and with a significant broadleaved forest cover. GEDI L2A footprints were filtered, resulting in 32,666 valid footprints. The analysis involved comparing GEDI-derived ground elevation with reference data collected by Airborne Laser Scanning (ALS), which offers superior vertical accuracy. Outlier values, potentially caused by degraded pointing/positioning data from GEDI’s auxiliary systems or cloud reflections, were identified using modified z-score, leading to the removal of 2.4% of observations. The study further explored factors influencing GEDI performance of ground surface retrieval, such as: the presence of forest vegetation, its height and type, use of coverage vs. power beam data, the time and season of data collection and geomorphological conditions (slope and terrain ruggedness). Overall, the accuracy is reasonably good (with an RMSE of 6.75 and a bias of − 0.53 m, after outlier removal), with a significant reduction of ground surface estimation in forested areas (RMSE of 7.75, versus 5.29 m in open ground). Accuracy is mainly influenced by the season of acquisition (RMSE between 5.45 and 8.75 m, the presence and height of forest canopy (RMSE of 6.04 m for canopies under 9.2 m and RMSE of 9.81 m for canopies taller than 22.7 m) and geomorphological conditions (RMSE between 5.61 m and 9.18 m, depending on slope). Understanding the interaction of GEDI with these factors is essential for improving the utilization of GEDI data in forest ecosystem monitoring. This research underscores the importance of accurate ground modeling in forestry applications and contributes to the ongoing evaluation of GEDI performance.

Key messageDespite showing a cost-effective potential for quantifying vertical forest structure, the GEDI and ICESat-2 satellite LiDAR missions fall short of the data accuracy standards required by tree- and stand-level forest inventories.Tree and stand heights are key inventory variables in forestry, but measuring them manually is time-consuming for large forestlands. For that reason, researchers have traditionally used terrestrial and aerial remote sensing systems to retrieve forest height information. Recent developments in sensor technology have made it possible for spaceborne LiDAR systems to collect height data. However, there is still a knowledge gap regarding the utility and reliability of these data in varying forest structures. The present study aims to assess the accuracies of dominant stand heights retrieved by GEDI and ICESat-2 satellites. To that end, we used stand-type maps and field-measured inventory data from forest management plans as references. Additionally, we developed convolutional neural network (CNN) models to improve the data accuracy of raw LiDAR metrics. The results showed that GEDI generally underestimated dominant heights (RMSE = 3.06 m, %RMSE = 21.80%), whereas ICESat-2 overestimated them (RMSE = 4.02 m, %RMSE = 30.76%). Accuracy decreased further as the slope increased, particularly for ICESat-2 data. Nonetheless, using CNN models, we improved estimation accuracies to some extent (%RMSEs = 20.12% and 19.75% for GEDI and ICESat-2). In terms of forest structure, GEDI performed better in fully-covered stands than in sparsely-covered forests. This is attributable to the smaller height differences between canopy tops in dense forest conditions. ICESat-2, on the other hand, performed better in thin forests (DBH < 20 cm) than in large-girth and mature stands of Crimean pine. We conclude that GEDI and ICESat-2 missions, particularly in hilly landscapes, rarely achieve the standards needed in stand-level forest inventories when used alone.

Light Detection and Ranging (LiDAR), with its ability to capture vegetation vertical profile, could be a unique technique for deriving Leaf Area Index (LAI). A global LAI product at 25-m spatial resolution was derived from the Global Ecosystem Dynamics Investigation (GEDI) LiDAR data since 2019, but it was often significantly underestimated in dense forests. Here we explored the potential for improving the estimation of the underestimated GEDI LAI in dense forests by using the Digital Elevation Model (DEM) as auxiliary data to separate ground and canopy returns in the received waveform. Dense forests were defined as forests with high vegetation greenness (annual maximum NDVI ≥ 0.8). The newly estimated GEDI footprint LAI was first validated with the ground-measured LAI at two sites in Fujian, China, and the results showed that the underestimation was significantly reduced compared to the original GEDI LAI product (r increased from −0.55 to 0.81, RMSE decreased from 3.94 to 1.43, Bias decreased from 3.17 to 0.48). To evaluate whether the improvement was applicable to other areas and forest types, the newly estimated GEDI footprint LAI for the entire Fujian and Contiguous US (CONUS) was then compared to the consistent LAI among three widely used global LAI products. The comparison results demonstrated that the use of DEM as auxiliary data could largely reduce the underestimation of GEDI footprint LAI (In Fujian, RMSE decreased from 4.75 to 2.52, and Bias decreased from 4.61 to 0.58; in CONUS, RMSE decreased from 5.24 to 1.96, and Bias decreased from 5.1 to 0.73). Overall, this study demonstrates the effectiveness of correcting the large underestimation of GEDI footprint LAI in dense forests by utilizing DEM, which has an important influence on the results, as auxiliary data.

Tropical forests play a significant role in regulating the average global atmospheric temperature encompassing 25 % of the carbon present in the terrestrial biosphere. However, the rapid change in climate, arising from unsustainable human practices, can significantly affect their carbon uptake capability in the future. For understanding these deviations, it is important to identify and quantify the large-scale canopy height variations arising from previous anthropogenic disturbances. With the advent of NASA GEDI spaceborne LiDAR (light detection and ranging), it is now possible to acquire three-dimensional vertical structural data of forests globally. In this study, we evaluate the applicability of GEDI for analyzing relative canopy height variations of secondary tropical forests of different age groups located across multiple geographical regions of peninsular Malaysia. The results for RH98 GEDI metric trends for the lowland and hill forests category across 4 different disturbance groups show a positive correlation between mean relative height and secondary forest ages. The consistency of these findings with previous studies in the region indicate the usefulness of GEDI to provide valuable insights into the patterns and drivers of forest height variation. Thus, this study contributes toward the operationalization of spaceborne LiDAR technology for monitoring forest disturbances and measuring biomass recovery rates and should help support large-scale sustainable forest management initiatives with respect to the tropical forests of Malaysia.

Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.

Digital elevation models (DEM) are an essential data source in many professional disciplines, with the help of gridded height information and values such as slope and aspect produced from that information. In this study, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI) satellite-altimetry data, and SRTM, ASTER-GDEM, and ALOS World3D data were used as Global DEMs (GDEMs) data in three different areas (U.S.A., New Zealand and Puerto Rico). We used kriging methods for interpolation to create the new rasters. Point-based accuracies were compared with the GDEMs from satellite-altimetry systems and raster-based comparisons were made by deriving DEMs with satellite-altimetry data in three different areas. It was seen that the ICESat-2 data in point-based results had similar accuracy with other GDEMs. DEMs produced by using ICESat-2 and GEDI data together gave relatively better results than using alone. In particular, the correlation was found to be highly correlated with 99%.