Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Duncanson, Laura; Kellner, James R.; Armston, John; Dubayah, Ralph; Minor, D.; Hancock, Steven; Healey, Sean P.; Patterson, Paul L.; Saarela, Svetlana; Marselis, S.; Silva, Carlos E.; Bruening, J. M.; Goetz, Scott J.; Tang, Hao; Hofton, M. A.; Blair, Bryan; Luthcke, S. B.; Fatoyinbo, Lola; Abernethy, Katharine; Alonso, Alfonso; Andersen, Hans‐Erik; Aplin, Paul; Baker, Timothy R.; Barbier, Nicolas; Bastin, Jean François; Biber, Peter; Boeckx, Pascal; Bogaert, Jan; Boschetti, Luigi; Boucher, Peter; Boyd, Doreen S.; Burslem, David F. R. P.; Calvo-Rodríguez, Sofía; Chave, Jérôme; Chazdon, Robin L.; Clark, David B.; Clark, Deborah A.; Cohen, Warren B.; Coomes, David A.; Corona, Piermaria; Cushman, K. C.; Cutler, Mark E. J.; Dalling, James W.; Dalponte, Michele; Dash, Jonathan P.; de‐Miguel, Sergio; Deng, Songqiu; Ellis, Peter W.; Erasmus, Barend; Fekety, Patrick A.; Fernández-Landa, Alfredo; Ferraz, António; Fischer, Rico; Fisher, Adrian; García‐Abril, Antonio; Gobakken, Terje; Hacker, Jörg; Heurich, Marco; Hill, Ross A.; Hopkinson, Chris; Huang, Huabing; Hubbell, Stephen P.; Hudak, Andrew T.; Huth, Andreas; Imbach, Benedikt; Jeffery, Kathryn J.; Katoh, Masato; Kearsley, Elizabeth; Kenfack, David; Kljun, Natascha; Knapp, Nikolai; Král, Kamil; Krůček, Martin; Labrière, Nicolas; Lewis, Simon L.; Longo, Marcos; Lucas, Richard; Main, Russell; Manzanera, J. A.; Martínez, Rodolfo Vásquez; Mathieu, Renaud; Memiaghe, Herve R.; Meyer, Victoria; Mendoza, Abel Monteagudo; Monerris, A.; Montesano, P.; Morsdorf, Felix; Næsset, Erik; Naidoo, Laven; Nilus, Reuben; O’Brien, Michael J.; Orwig, David A.; Papathanassiou, Konstantinos; Parker, Geoffrey G.; Philipson, Christopher D.; Phillips, Oliver L.; Písek, Jan; Poulsen, John R.; Pretzsch, Hans; Rüdiger, Christoph; Saatchi, Sassan; Sánchez‐Azofeifa, Arturo; Sánchez‐López, Nuria; Scholes, Robert J.; Silva, Carlos Antônio da; Simard, Marc; Skidmore, A.K.; Stereńczak, Krzysztof; Tanase, Mihai A.; Torresan, Chiara; Valbuena, Rubén; Verbeeck, Hans; Vrška, Tomáš; Wessels, Konrad J; White, Joanne C.; White, Lee; Zahabu, Eliakimu; Zgraggen, Carlo

doi:10.1016/j.rse.2021.112845

Cited by 206 publications

(116 citation statements)

References 102 publications

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“…The GEDI sensor is installed on the International Space Station and is equipped with a geodetic-class laser altimeter/waveform lidar composed of three lasers that produce eight transects of structural information, providing 25-m resolution measurements of forest height in temperate and tropical forests (between 51.6 • N and 51.6 • S latitude). GEDI data are released with different products corresponding to different preprocessing levels [36]. In this study, we used the highest level of GEDI products, Level 4A, which predicts-in tons per hectare (t ha −1 )-the aboveground biomass density (AGBD) for each sampled geolocated laser pulse [35].…”

Section: Gedi Level 4a Datamentioning

confidence: 99%

“…With each footprint are associated a set of ancillary data such as uncertainty metrics, quality flags, and model inputs (land cover input data including PFTs and the world region identifiers). For details on the procedure used to generate Level 4A data, please refer to Duncanson et al [36]. The correlation between AGBD and CHM data was assessed using all GEDI pulses acquired in Italy over areas where ALS data were available, resulting in 3,107,244 pulses.…”

Section: Gedi Level 4a Datamentioning

confidence: 99%

“…While the potential of the GEDI sensor still needs to be further explored, an additional GEDI product was recently released (Level 4) consisting of aboveground biomass density (AGBD) estimates for the period 18 April 2019 to 2 September 2020 [35,36]. To simplify the usage of GEDI AGBD data, an R package (GEDI4R) was recently developed [37] that provides functions for downloading, reading, clipping, exporting, and visualizing GEDI Level 4A data.…”

Section: Introductionmentioning

confidence: 99%

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Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy

Francini

D’Amico

Vangi

et al. 2022

Sensors

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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.

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Section: Gedi Level 4a Datamentioning

confidence: 99%

Section: Gedi Level 4a Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy

Francini

D’Amico

Vangi

et al. 2022

Sensors

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“…Unlike multispectral and hyperspectral, LiDAR sensors acquire three-dimensional point cloud information and vertical structure information of the forest by emitting highfrequency pulses from laser transmitters [13]. Researches on forestry applications of LiDAR data have focused on individual tree segmentation [14][15][16] and forest structure parameter extraction such as stand height, crown width, and biomass [17][18][19]. Some researchers have also used height as well as intensity information from LiDAR data for tree species classification [20,21].…”

Section: Introductionmentioning

confidence: 99%

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

Chai

Lei

et al. 2022

Remote Sensing

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Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment.

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“…The use of field measurements to estimate canopy height is not feasible at regional or global scales, as it is often too expensive and time-consuming. Active remote sensing methods such as Light Detection and Ranging (lidar) and Synthetic Aperture Radar (SAR) combined with optical remote sensing data [5][6][7][8][9][10][11][12] have shown their effectiveness in measuring vegetation height and estimating AGB at the regional scale. Airborne and terrestrial lidar are widely used as a reliable technique to accurately map canopy height over small-to-medium scales (10 s of km 2 ) [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

2022

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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.

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Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission

Cited by 206 publications

References 102 publications

Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy

Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars

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