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Forest canopy height (FCH) is crucial for monitoring forest structure and aboveground biomass. Light detecting and ranging (LiDAR), as a promising remote sensing technology, provides various forms of data for measuring and mapping FCH. Airborne laser scanning (ALS) could accurately measure FCH at the plot-level. Spaceborne lidar system (SLS) allows for global sampling of FCH at the footprint-level. However, ALS data has limited spatial coverage, while SLS data has relatively lower estimation accuracy. To this end, we proposed a two-step FCH mapping framework by combining ALS, SLS and auxiliary data. Firstly, using the ALS-derived FCH as reference, the SLS-derived relative height metrics were calibrated at the footprint-level using a regression method. Secondly, to further address the spatial discontinuities in SLS-derived FCH maps, a site-level FCH model was built using a weighted ensemble multi-machine learning model incorporating spatial effects (WEML_SE). The calibrated footprint-level calibration FCH model was used as a reference, and multiple remote sensing data metrics were selected and subjected to important variable selection. Specifically, a spatial adjacency matrix was established based on the spatial locations of SLS footprints, and spatial feature vectors were extracted. The result indicated that the correlation coefficient between the SLS-derived FCH and the ALS-derived FCH (r = 0.39–0.73, MRE=10.6–25.9 %, and RMSE=2.58–9.37 m) improved at footprint-level (r = 0.71–0.84, MRE=7.7–18.7 %, RMSE=1.96–7.68 m). Moreover, the WEML_SE exhibited better performance (r = 0.59–0.75, MRE=8.8–14.8 %, RMSE=2.12–5.4 m) compared to the model without incorporating spatial effects (r = 0.45–0.71, MRE=9.4–15.8 %, RMSE=2.28–5.89 m). This study emphasizes the advantages of integrating spaceborne and airborne LiDAR data to construct footprint-level estimation of FCH. The proposed WEML_SE model provides new possibilities for accurately generating wall-to-wall estimates of forest biomass.

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%). [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) mission can significantly enhance multisource national forest inventories (MSNFI) by improving the spatio-temporal resolution of forest attributes while preserving the statistical relevance of the design-based inference approach. The main challenge is the lack of systematic spatial alignment between GEDI footprints and National Forest Inventory (NFI) plots, which is necessary to accurately link in situ forest measurements with GEDI data. In this study, we aim to tackle the aforementioned issue by introducing a methodology for interpolating GEDI measurements to NFI plots, enabling the calibration of GEDI data using localized NFI estimates. Our proposed method incorporates clustering, classification, and regression techniques, and utilizes GEDI and NFI data, along with Sentinel-2 images, land-use information, and topographic data. Beginning with the prediction of profile structural classes and shapes on NFI plots, the proposed method ultimately projects actual measurements onto the NFI plot sites through profile pairing within the predicted structural classes. The method is conceived and validated using the data acquired across the mountainous area of $\sim $500 kha, covered by >500 NFI plots. Our validation framework shows that the method is able to project relative height profiles at NFI plots, allowing to partly interpolate the lower part of the profile and not only the canopy top height. This enables the construction of models that efficiently relate GEDI profiles and wood volume, demonstrating the importance of incorporating lower relative height values when linking forest attributes and lidar measurements ($R^{2}=0.65$, $MBE=2.31$ m$^{3}$/ha).

The global ecosystem dynamics investigation (GEDI) mission aims to provide large-scale, high-precision, and high-frequency measurements of the Earth's three-dimensional structures. However, uncertain geolocation error may hinder or restrict the further application of GEDI products. Based on the error matrix, the laser spot center positioning method was employed to quickly evaluate and quantify the geolocation errors of GEDI L2A (version 2) data in the high-latitude region of China, thereby reducing the impact of systematic geolocation errors on elevation and canopy height detection performance. Combining with high-resolution airborne light detection and ranging data, we provided the geolocation offset characteristics at footprint, beam, and orbit scales, while monitoring their performance over nearly a year. Correcting geolocation errors at the footprint scale can significantly enhance the elevation accuracy, butit has the largest standard deviation (approximately 15 m). Conversely, at the orbit and beam scales, the standard deviations of along- and cross-track error are smaller and the ability to improve elevation accuracy is lower. At beam scale, the level of improvement in elevation accuracy increases with the slope but diminishes when the slope exceeds 20°. Unfortunately, the effect of geolocation correction on canopy height accuracy in this study area is not obvious. Over time, GEDI's elevation detection performance exhibits greater stability, whereas canopy height accuracy decreases as the leaf on season transitions to the leaf off season. The proposed approach provides a rapid solution for preliminary evaluation (beam and orbit scale) and detailed assessment (footprint scale) of GEDI's geolocation errors, thereby laying the groundwork for its future applications.

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.

Leaf area index (LAI) is defined as one half of the total green leaf area per unit ground surface area. Its vertical profile is critical for understanding the remote sensing radiative transfer processes. LAI profile has been derived from airborne and spaceborne LiDAR data, such as the Global Ecosystem Dynamics Investigation (GEDI) installed on the International Space Station. However, the capability of various algorithms for the LAI profile estimation with airborne LiDAR is not clearly evaluated, and the estimated LAI profiles, including the GEDI LAI products, are not been fully validated. This study conducted a quantitative retrieval and validation of the LAI profiles using terrestrial and airborne laser scanning (TLS and ALS) and spaceborne GEDI data over a deciduous needleleaf forest site in northern China. The vertical LAI profile was estimated in the field using an upward digital hemispherical photography (DHP) attached to a portable measurement system in 2020 and 2021. A suite of new LiDAR indices combining both LiDAR return number and return intensity was explored for the LAI profile estimation. All LAI profiles obtained from the DHP, TLS, ALS, and GEDI during the leaf-on season and leaf-off season were compared. The DHP shows a good agreement with the TLS LAI profiles (R2 = 0.97). The LAI profile derived from the ALS data using the combined light penetration index (LPIRI) agrees well (R2 ≥0.86) with the DHP, TLS, and GEDI estimates. In general, the LPIRI is advantageous for regional LAI profile mapping from ALS. The GEDI cumulative LAI corresponds well with the DHP during the leaf-on season (R2 = 0.90, RMSE = 0.23), but underestimates during the leaf-off season (R2 = 0.70, RMSE = 0.14, bias=−0.13). The underestimation is attributed to the higher canopy and ground reflectance ratio (ρv/ρg) assigned in the algorithm and the height discrepancy between the GEDI and field measurements. For the GEDI LAI profile product, further validation and improvement are necessary for other biome types and landscape conditions, especially during the leaf-off season.

Global Ecosystem Dynamics Investigation (GEDI) is a lidar system on-board the International Space Station designed to study forest ecosystems. However, GEDI footprint low accuracy geolocation is a major impediment to the optimal benefit of the data. We thus proposed a geolocation correction method, GeoGEDI, only based on high-resolution digital elevation models (DEMs) and GEDI derived ground elevations. For each footprint, an error map between GEDI ground estimates and reference DEM was computed, and a flow accumulation algorithm was used to retrieve the optimal footprint position. GeoGEDI was tested on 150 000 footprints in Landes and Vosges, two French forests with various stands and topographic conditions. It was applied to GEDI versions 1 (v1) and 2 (v2), by either a single or four full-power laser beam tracks. GeoGEDI output accuracy was evaluated by analyzing shift distributions and comparing GEDI ground elevations and surface heights to reference data. GeoGEDI corrections were greater for v1 than for v2 and agreed with errors published by NASA. Within forests, GeoGEDI improved the root mean square error (RMSE) of ground elevation in Landes by 26.8% (0.34 m) and by 13.3% (0.14 m) for v1 and v2, respectively. For Vosges, ground elevation RMSE improved by 59.6% (3.82 m) and 36.2% (1.41 m), for v1 and v2, respectively. Regarding surface heights, except for v2 in Landes, where insufficient variations in topography combined to GEDI ground detection issues might have penalized the adjustment, GeoGEDI improved GEDI estimates. Using GeoGEDI showed efficient to improve positioning bias and precision. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) aims to provide improved characterization of forest structure, and plant area index (PAI) is one of many variables provided in the official GEDI Level 2B (L2B) product suite. However, since release, few quantitative validation studies have been conducted. To reach Stage 1 of the validation hierarchy proposed by the Land Product Validation (LPV) subgroup of the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV), we provide an initial assessment of PAI estimates from GEDI’s L2B product. This is achieved using 18 in situ reference measurements available through the Copernicus Ground Based Observations for Validation (GBOV) service. We show that GEDI L2B PAI retrievals provide a nearly unbiased estimate of effective (PAIe; RMSD = 0.95, bias = 0.02, slope = 1.07), but systematically underestimate PAI (RMSD = 1.42, bias = −0.91, slope = 0.77). This is attributed to an assumed random distribution of plant material in the algorithm. To reach Stage 2 of the CEOS WGCV LPV hierarchy, continued work is needed to validate the product against additional in situ reference measurements covering further locations and time periods. [ABSTRACT FROM AUTHOR]

The global ecosystem dynamics investigation (GEDI) contains a full waveform, multibeam laser altimeter, which provides vertical vegetation structure information. However, few studies have assessed the accuracy of using GEDI light detection and ranging (LiDAR) data in retrieving ground topography from dense forest environments. There has been no in-depth study assessing the accuracy of GEDI data with different kinds of elevation index in footprint samples from densely forested areas (canopy cover > 70%). To address this limitation, this study aims to assess the accuracy of ground topography estimates from dense forested terrain through the combination of airborne LiDAR data. The results show that under dense forest conditions, the mean elevation in the GEDI footprint performed more accurate than the max elevation, the min elevation, and the median elevation in the GEDI footprint. GEDI provides reasonable estimates of mean terrain height, with ${R} ^{2}$ of 0.99, root mean squared error (RMSE) of 6.08 m, and mean absolute error (MAE) of 3.92 m. In addition, dense forest ecosystems reduce the accuracy of the terrain height estimates. Overall, GEDI with its full-waveform technology represents promising spaceborne LiDAR for terrain height retrieval in dense forest environments. [ABSTRACT FROM AUTHOR]

Measuring the vertical structure of tropical forests using remote sensing technology is challenging. To overcome this, active sensors, such as P-band synthetic aperture radar (SAR) and light detection and ranging (LiDAR), are used to penetrate thick vegetation layers. NASA’s global ecosystem dynamics investigation (GEDI) uses spaceborne LiDAR data. In contrast, the European Space Agency’s (ESA) BIOMASS mission uses multiple acquisitions of SAR data to create 3-D images through a technique called SAR tomography (TomoSAR). This letter discusses the forest’s vertical structure, such as volume peak (or volume scattering center), penetration, and reflectivity, using GEDI and airborne P-band TomoSAR by analyzing measurements at tropical forest sites in South America and Africa. It was found that the location of the volume peak in TomoSAR is consistently lower than in GEDI, with a range of 2–4 m depending on the polarization and the height of the forest layers. Compared with GEDI, TomoSAR data have a better ground reflection for vegetation taller than 25 m. GEDI and TomoSAR data can accurately capture vertical information in the canopy levels (between 10 and 40 m), displaying a strong correlation in the volume layers. The highest correlation occurs around 30 m above ground level, aligning with previous research in developing algorithms for the BIOMASS mission in aboveground biomass (AGB) retrieval. Together, TomoSAR and GEDI are robust and comparable in studying tropical forests and support the BIOMASS mission for global biomass mapping. [ABSTRACT FROM AUTHOR]

Key message: Despite 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. [ABSTRACT FROM AUTHOR]

Over the past two decades spaceborne LiDAR systems have gained momentum in the remote sensing community with their ability to accurately estimate canopy heights and aboveground biomass. This article aims at using the most recent global ecosystem dynamics investigation (GEDI) LiDAR system data to estimate the stand-scale dominant heights (${{\boldsymbol{H}}_{{\rm{dom}}}}$), and stand volume (V) of Eucalyptus plantations in Brazil. These plantations provide a valuable case study due to the homogenous canopy cover and the availability of precise field measurements. Several linear and nonlinear regression models were used for the estimation of ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V based on several GEDI metrics. ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V estimation results showed that over low-slopped terrain the most accurate estimates of ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V were obtained using the stepwise regression, with an root-mean-square error (RMSE) of 1.33 m (R2 of 0.93) and 24.39 m3.ha−1 (R2 of 0.90) respectively. The principal metric explaining more than 87% and 84% of the variability (R2) of ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V was the metric representing the height above the ground at which 90% of the waveform energy occurs. Testing the postprocessed GEDI metric values issued from six available different processing algorithms showed that the accuracy on ${{\boldsymbol{H}}_{{\rm{dom}}}}$ and V estimates is algorithm dependent, with a 16% observed increase in RMSE on both variables using algorithm a5 vs. a1. Finally, the choice to select the ground return from the last detected mode or the stronger of the last two modes could also affect the ${H_{{\rm{dom}}}}$ estimation accuracy with 12 cm RMSE decrease using the latter.

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. [ABSTRACT FROM AUTHOR]