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Distribution of measurements of (a) dominant canopy heights and (b) wood volume from field inventories of the 566 Eucalyptus stands. The Y-axis represents the percentage of samples within each height range (a) and wood volume (b).
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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 study aims at using the most recent Global Ecosystem Dynamics Investigation (GEDI) LiDAR system data to estimate the stand-scale dominant heights (H<sub>dom</...
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... GEDI acquisitions and inventory were used. In fact, on these fast growing plantations, a two-month difference could result in an up to 50 cm growth in H dom [see Fig. 4(a)] and 10 m3.ha −1 in V [see Fig. 4(b)]. However, this reasonable compromise allows keeping a large number of stands including a large variability of age and growing conditions. Fig. 5 shows the distribution of field measured H dom and ...
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... Forest height estimated on these plantations with GEDI are accurate and offer a possibility to monitor the forest plantation biomass (I. Fayad, N. Baghdadi, et al. 2021a;I. Fayad, N. N. Baghdadi, et al. 2021b). ...
The Global Ecosystem Dynamics Investigation (GEDI) is a high-resolution lidar instrument, facilitating landscape monitoring through forest structure and biomass assessments. Previous research has mainly focused on estimating forest attributes such as height and biomass from GEDI data. In this comprehensive study, we empirically investigate how Eucalyptus plantation characteristics and environmental factors collectively influence GEDI waveform metrics. Using a diverse dataset that includes measurements of canopy height, planting density, foliage characteristics, species composition, understorey presence and environmental conditions such as climate and soil properties, we explore the complex relationships between these attributes and GEDI wave-form metrics using a two-step approach that includes assessing the linear relationship between each in-situ parameter and GEDI metrics, and also random forest modelling. Key findings include: 1) While canopy height (Ht) plays a significant role in shaping the GEDI wave-form, forest wood volume (Volume), which incorporates height and diameter at breast height (DBH) of the trees and also tree stem density (Ntree/ha), exerts an even greater influence on GEDI metrics, in particular on the waveform extent (WE) and relative heights (RH); 2) The influence of factors such as foliage biomass and wood volume on GEDI waveform varies vertically from the forest floor up to forest top, and the model was more precise to predict the top part of the waveform; 3) No single attribute can solely account for the observed variations in the waveform characteristics, but multiple interactions between different forest and environmental features contribute to the complex patterns in the received waveforms. This research contributes to a deeper understanding of the complex relationships between forest plantation characteristics and GEDI waveform metrics and highlights the need to consider a wide range of forest attributes in the analyse of GEDI wave-form to produce more accurate canopy height or volume estimates. ARTICLE HISTORY
... The GEDI (Global Ecosystem Dynamics Investigation) LiDAR mission, developed and operated by NASA onboard the International Space Station (ISS) since 2019, has produced accurate point-wise observations of forest structure (Dubayah et al., 2020). Combined with other space-borne and airborne data, this instrument has shown promising capabilities for height mapping (Fayad et al., 2021a;Lang et al., 2022Lang et al., , 2023Potapov et al., 2021). Sentinel-1 (S1) and Sentinel-2 (S2) are two satellite missions of ESA's Copernicus program for Earth observation. ...
... Comprehensive and large-scale observations of forest ecosystems have become indispensable for understanding and addressing these challenges (Silva et al. 2021). Accurately quantifying vertical structural parameters within forests, such as forest height, is crucial not only for reducing uncertainties in carbon stock estimations but also for advancing our knowledge of terrestrial ecosystems and the global carbon cycle (Fayad et al. 2021;Narine et al. 2019b). ...
Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides effective photon-counting light detection and ranging (LiDAR) data for estimating forest height across extensive geographical areas. Although prior studies have illustrated canopy conditions during leaf-on and leaf-off phases may influence ICESat-2 derived forest heights, a comprehensive understanding of this effect remains incomplete. This study seeks to comprehensively assess how varying canopy conditions (leaf-on/leaf-off) affect ICESat-2 forest height retrieval and modelling. First, the accuracies of ICESat-2 terrain and canopy heights under leaf-on and leaf-off conditions were validated. Second, random forest algorithm was utilized to model forest height by integrating ICESat-2, Sentinel-2, and other ancillary datasets. Finally, we evaluated the influence of leaf-on and leaf-off conditions on forest height retrieval and modelling. Results reveal higher consistency between ICESat-2 and airborne LiDAR-derived terrain heights compared to the agreement between two canopy height datasets. Accuracies of ICESat-2 terrain and canopy heights are higher under leaf-off conditions in contrast to leaf-on conditions. Notably, the accuracies of ICESat-2 terrain and canopy heights under various conditions are closely linked to canopy cover. Furthermore, the accuracy of forest height modelling can be enhanced by combining ICESat-2 data collected during both leaf-on and leaf-off seasons with further eliminating low-quality samples.
... The key to extracting the maximum canopy height from waveform LiDAR data is to accurately extract two elevation points from the waveform data, i.e., the ground elevation and the canopy top elevation (corresponding to the signal start position), where the difference between the two points is the maximum canopy height [18], [19], [20], [21], [22]. For ground elevation extraction, the most commonly used method is Gaussian decomposition [23], [24], [25]. ...
The Global Ecosystem Dynamics Investigation (GEDI) instrument, which represents a new generation of spaceborne full-waveform LiDAR systems, is also the first spaceborne LiDAR system specifically designed to monitor the vertical structure of vegetation. In the four years of operation to date, the GEDI instrument has provided unprecedented observations for global forest height and forest above-ground biomass estimation studies. However, it remains a daunting challenge to obtain accurate canopy height estimates based on GEDI observations in topographically undulating areas. Studies on how to make full use of the actual topographic conditions within each GEDI footprint, to aid in canopy height extraction, are still lacking. In this paper, we propose a method based on the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM)-simulated ground echo for assisting in ground waveform identification and canopy top elevation correction, which in turn improves the accuracy of the maximum canopy height extraction in topographically undulating areas. The validation results show that the accuracy of the ground elevation, canopy top elevation, and maximum canopy height obtained by the proposed method is improved by 9.2%, 15%, and 14%, respectively, compared with the GEDI L2A product. Since the only auxiliary data used in this approach are the publicly available SRTM DEM product data, the proposed method has the potential to be applied to large regions or even globally. This paper will promote the application of GEDI observations in monitoring the vertical structure of vegetation in areas of topographic relief.
... Compared to other spaceborne LiDAR instruments, GEDI has a smaller footprint of 25 m, which was designed to minimize such slope effects (Duncanson et al., 2022). However, the footprint is still relatively large compared to ALS data (~0.25 m), and a substantial bias can be expected when using GEDI to estimating forest height on steep slopes (Fayad, Baghdadi, Alcarde Alvares, et al., 2021;Ni et al., 2021). The degree to which these factors are limiting the use of GEDI for ecological applications in mountain areas remains unclear to date. ...
... Slope correction algorithms are available for addressing the slope bias of GEDI (e.g., Wang et al., 2019). However, implementing these algorithms is not straightforward and usually requires auxiliary data (e.g., high-quality digital elevation models; Fayad, Baghdadi, Alcarde Alvares, et al., 2021;Hancock et al., 2019;Ni et al., 2021). This limits the use of GEDI in data-poor regions and as a Figure 5. ALS and GEDI grid cells that fulfill the criteria for the provisioning of the ecosystem functions and services "avalanche protection", "habitat suitability" and "high carbon uptake". ...
The launch of NASA's Global Ecosystem Dynamics Investigation (GEDI) mission in 2018 opens new opportunities to quantitatively describe forest ecosystems across large scales. While GEDI's height-related metrics have already been extensively evaluated, the utility of GEDI for assessing the full spectrum of structural variability-particularly in topographically complex terrain-remains incompletely understood. Here, we quantified GEDI's potential to estimate forest structure in mountain landscapes at the plot and landscape level, with a focus on variables of high relevance in ecological applications. We compared five GEDI metrics including relative height percentiles, plant area index, cover and understory cover to airborne laser scanning (ALS) data in two contrasting mountain landscapes in the European Alps. At the plot level, we investigated the impact of leaf phenology and topography on GEDI's accuracy. At the landscape-scale, we evaluated the ability of GEDIs sample-based approach to characterize complex mountain landscapes by comparing it to wall-to-wall ALS estimates and evaluated the capacity of GEDI to quantify important indicators of ecosystem functions and services (i.e., avalanche protection, habitat provision , carbon storage). Our results revealed only weak to moderate agreement between GEDI and ALS at the plot level (R 2 from 0.03 to 0.61), with GEDI uncertainties increasing with slope. At the landscape-level, however, the agreement between GEDI and ALS was generally high, with R 2 values ranging between 0.51 and 0.79. Both GEDI and ALS agreed in identifying areas of high avalanche protection, habitat provision, and carbon storage, highlighting the potential of GEDI for landscape-scale analyses in the context of ecosystem dynamics and management.
... Thus, although GEDI and the GFCH provide great datasets for forest structure applications, topography, forest structure and the geolocation uncertainty must be taken into account when considering use of GEDI products (Fayad et al., 2021;Roy et al., 2021). For example, in spatially heterogeneous canopies (e.g., secondary forests), unreliable GEDI canopy height retrievals are likely to be associated with geolocation uncertainty because of their spatially fragmented and heterogeneous threedimensional structure. ...
Detailed maps of forest structure attributes are crucial for sustainable forest management, conservation, and forest ecosystem science at the landscape level. Mapping the structure of broad heterogeneous forests is challenging , but the integration of extensive field inventory plots with wall-to-wall metrics derived from synthetic aperture radar (SAR) and optical remote sensing offers a potential solution. Our goal was to map forest structure attributes (diameter at breast height, basal area, mean height, dominant height, wood volume and canopy cover) at 30-m resolution across the diverse 463,000 km 2 of native forests of Argentina based on SAR Sentinel-1, vegetation metrics from Sentinel-2 and geographic coordinates. We modelled the forest structure attributes based on the latest national forest inventory, generated uncertainty maps, quantified the contribution of the predictors, and compared our height predictions with those from GEDI (Global Ecosystem Dynamics Investigation) and GFCH (Global Forest Canopy Height). We analyzed 3788 forest inventory plots (1000 m 2 each) from Argentina's Second Native Forest Inventory (2015-2020) to develop predictive random forest regression models. From Sentinel-1, we included both VV (vertical transmitted and received) and VH (vertical transmitted and horizontal received) polarizations and calculated 1st and 2nd order textures within 3 × 3 pixels to match the size of the inventory plots. For Sentinel-2, we derived EVI (enhanced vegetation index), calculated DHIs (dynamic habitat indices (annual cumulative, minimum and variation) and the EVI median, then generated 1st and 2nd order textures within 3 × 3 pixels of these variables. Our models including metrics from Sentinel-1 and 2, plus latitude and longitude predicted forest structure attributes well with root mean square errors (RMSE) ranging from 23.8% to 70.3%. Mean and dominant height models had notably good performance presenting relatively low RMSE (24.5% and 23.8%, respectively). Metrics from VH polarization and longitude were overall the most important predictors, but optimal predictors differed among the different forest structure attributes. Height predictions (r = 0.89 and 0.85) outperformed those from GEDI (r = 0.81) and the GFCH (r = 0.66), suggesting that SAR Sentinel-1, DHIs from Sentinel-2 plus geographic coordinates provide great opportunities to map 2 multiple forest structure attributes for large areas. Based on our models, we generated spatially-explicit maps of multiple forest structure attributes as well as uncertainty maps at 30-m spatial resolution for all Argentina's native forest areas in support of forest management and conservation planning across the country.
... Forest canopy height mapping has been made using different remote-sensing technologies including SAR (Garestier et al., 2008;Kugler et al., 2014), oblique photography from satellites and drones (Li et al., 2020), and light detection and ranging (LiDAR) (Fayad et al., 2014(Fayad et al., , 2021(Fayad et al., , 2021bLefsky et al., 2005). In comparison, LiDAR has been the most powerful tool since it provides direct observation of forest canopy heights in the vertical plane (Li et al., 2020). ...
... Previous efforts of independently validating GEDI canopy height and terrain measurements indicated good agreement with airborne LiDAR scanning and other data sources (Fayad et al., 2014(Fayad et al., , 2021(Fayad et al., , 2021bLang et al., 2022a;Potapov et al., 2021). To increase the utility of such discrete sample canopy height and canopy cover estimates, extrapolating over continuous space is fundamental. ...
Global Ecosystem Dynamics Investigation (GEDI); a full waveform lidar sensor, acquires samples of the terrain and vegetation structures. To derive a spatially continuous estimate, the canopy height and cover metrics retrieved from GEDI L2A and L2B, respectively, were extrapolated using support vector regression models and explanatory variables derived from multiple remotely sensed datasets. Explanatory variables extrapolated GEDI canopy height with RMSE = 3.83 m (R2 = 0.84) as the performance was validated using a subset of GEDI data. However, the accuracy decreased to RMSE = 7.98 m (R2 = 0.65) as validated with field-measured canopy heights. The accuracy of the prediction of forest canopy covers retrieved from GEDI L2B generally remains poor; RMSE = 0.14 (R2 = 0.53).
... In areas with steep slopes or complex terrain, large footprint lidar systems like GEDI can experience reduced accuracy in canopy height estimations due to reasons such as augmented lidar waveform extent (>25 m diameter) in steep slopes (Hancock et al., 2019;Duncanson et al., 2020). Though the future versions of the data should have higher accuracy using more calibration, in the interim, we recommend comparing the vegetation metrics derived from different ground fitting algorithms and to use the one that best matches the local conditions in areas with complex terrain (Adam et al., 2020;Dubayah et al., 2020;Fayad et al., 2021). Selection of the ground fitting algorithms influences the values of the four metrics, RH metrics, canopy cover, PAI, and AGBD, as the algorithm calculates the ground elevation which influences the derivation of canopy height in each waveform. ...
Understanding changes to aboveground biomass (AGB) in forests undergoing degradation is crucial for accurately and completely quantifying carbon emissions from forest loss and for environmental monitoring in the context of climate change. Monitoring forest degradation as compared to deforestation presents technical challenges because degradation involves widespread, low-intensity AGB removal under varying temporal dynamics. Charcoal production is a key driver for forest degradation in Africa and is projected to increase in the future years. In Sub-Saharan Africa (SSA), where charcoal production drives widespread ABG removal, the utility of optical remote sensing for degradation quantification is challenged by the large inter-seasonal variation and high complexities in ecosystem structure. Limited field measurements on tree structure and aboveground biomass density (AGBD) in many parts of the SSA also impose constraints. In this study, we present a novel data fusion approach combining 3D forest structure from NASA's GEDI Lidar with optical time-series data from Landsat to quantify biomass losses associated with charcoal-related forest degradation over a 10-year time period. We used machine learning models with Landsat spectral indices from the time period of limited hydric stress (LHS) as predictor variables. By applying the best performing Random Forest (RF) model to LandTrendr-stabilized annual LHS Landsat composites, we produced annual forest AGBD maps from 2007 to 2019 over the Mabalane district in southern Mozambique where the dry forest ecosystem was under active charcoal-related degradation since 2008. The RF model achieved an RMSE value of 7.05 Mg/ha (RMSE% = 42%) and R² value of 0.64 using a 10-fold cross-validation dataset. We quantified a total AGB loss of 2.12 ± 0.06 Megatons (Mt) over the 10-year period, which is only 6.35 ± 2.56% less than the total loss estimated using field-based data as previously published for the same area and time. In addition to quantifying biomass loss, we constructed annual AGBD maps that enabled the characterization of disturbance and recovery. Our framework demonstrates that fusing GEDI and Landsat data through predictive modeling can be used to quantify past forest AGBD dynamics in low biomass forests. This approach provides a satellite-based method to support REDD+ monitoring and evaluation activities in areas where field data is limited and has the potential to be extended to investigate a variety of different disturbance events.
... In areas with steep slopes or complex terrain, large footprint lidar systems like GEDI can experience reduced accuracy in canopy height estimations due to reasons such as augmented lidar waveform extent (>25 m diameter) in steep slopes (Hancock et al., 2019;Duncanson et al., 2020). Though the future versions of the data should have higher accuracy using more calibration, in the interim, we recommend comparing the vegetation metrics derived from different ground fitting algorithms and to use the one that best matches the local conditions in areas with complex terrain (Adam et al., 2020;Dubayah et al., 2020;Fayad et al., 2021). Selection of the ground fitting algorithms influences the values of the four metrics, RH metrics, canopy cover, PAI, and AGBD, as the algorithm calculates the ground elevation which influences the derivation of canopy height in each waveform. ...
Protected areas (PAs) serve as a critical strategy for protecting natural resources, conserving biodiversity, and mitigating climate change. While there is a critical need to guide area-based conservation efforts, a systematic assessment of PA effectiveness for storing carbon stocks has not been possible due to the lack of globally consistent forest biomass data. In this study, we present a new methodology utilizing forest structural information and aboveground biomass density (AGBD) obtained from the Global Ecosystem Dynamics Investigation (GEDI) mission. We compare PAs with similar, unprotected forests obtained through statistical matching to assess differences in carbon storage and forest structure. We also assess matching outcomes for a robust and minimally biased way to quantify PA efficacy. We find that all analyzed PAs in Tanzania possess higher biomass densities than their unprotected counterfactuals (24.4% higher on average). This is also true for other forest structure metrics, including tree height, canopy cover, and plant area index (PAI). We also find that community-governed PAs are the most effective category of PAs at preserving forest structure and AGBD – often outperforming those managed by international or national entities. In addition, PAs designated under more than one entity perform better than the PAs with a single designation, especially those with multiple international designations. Finally, our findings suggest that smaller PAs may be more effective for conservation, depending on levels of connectivity. Taken together, these findings support the designation of PAs as an effective means for forest management with considerable potential to protect forest ecosystems and achieve long-term climate goals.
... GEDI is optimized explicitly for retrieving vegetation vertical structure and can provide detailed information about canopy structure, biomass, and topography. Studies have shown that GEDI has the potential for estimating CHM with a high level of accuracy, even in complex forested areas ( [4], [5], [6]). GEDI data has been used to create global canopy height maps, and its high resolution and sampling rate makes it a valuable tool for studying tropical forests ( [7], [8]). ...
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 3D images through a technique called SAR tomography (TomoSAR). The paper 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 to GEDI, TomoSAR data has a better ground reflection for vegetation taller than 25 m. GEDI and TomoSAR data can accurately capture vertical information in the canopy levels (between 10-40 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 retrieval. Together, TomoSAR and GEDI are robust and comparable in studying tropical forests and support the BIOMASS mission for global biomass mapping.
