GEDI L4A Footprint Level Aboveground Biomass Density, Version 1

Dubayah, Ralph; Armston, John; Kellner, James R.; Duncanson, Laura; Healey, Sean P.; Patterson, Paul L.; Hancock, Steven; Tang, Hao; Hofton,; Blair, J. B.; Luthcke, S. B.

doi:10.3334/ornldaac/1907

Cited by 8 publications

(5 citation statements)

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“…Our models relied on different configurations of relative height metrics to explain complexity associated with different canopy vertical layers, which had not been quantified over large scales. While these RH metrics are correlated amongst themselves 33 , these linkages decrease as the vertical distance among them increase, and thus our three, broad strata of upper, middle and lower waveform returns are likely discriminating on actual differences in structure and not artifacts of the modeling process or the data used to derive it.…”

Section: Discussionmentioning

confidence: 89%

“…We assessed the relationship between WSCI estimates and other relevant forest structural metrics from GEDI on a global scale, using only shots where RH98 was greater than 5 meters, and where tree cover was expected from the European Space Agency (ESA) WorldCover v200 product at 10 m resolution 50 . The GEDI metrics compared with the WSCI were as follows: canopy cover fraction, canopy height (RH98), plant area index (PAI), foliage height diversity (FHD) and above ground biomass density (AGBD), extracted from the GEDI L2A 19 , L2B 32 and L4A 33 data products.…”

Section: Gedi Datamentioning

confidence: 99%

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Characterizing the structural complexity of the Earth’s forests with spaceborne lidar

de Conto,

Dubayah,

Armston

2024

Preprint

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Forest structural complexity is a key element of ecosystem functioning, impacting light environments, nutrient cycling, biodiversity, and habitat quality. Addressing the need for a comprehensive global assessment we derive the first, near-global map of 3D canopy complexity using data from the GEDI spaceborne lidar mission. These data show that tropical forests harbor most of the high complexity observations, while less than 20% of temperate forests reached median levels of tropical complexity. Structural complexity in tropical forests is more strongly related to canopy attributes from lower and middle waveform layers, whereas in temperate forests upper and middle layers are more influential. Globally, forests exhibit robust scaling relationships between complexity and canopy height, but these vary geographically and by biome. Our results offer insights into the spatial distribution of forest structural complexity and emphasize the importance of considering biome-specific and fine-scale variations for ecological research and management applications.

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Section: Discussionmentioning

confidence: 89%

Section: Gedi Datamentioning

confidence: 99%

Characterizing the structural complexity of the Earth’s forests with spaceborne lidar

de Conto,

Dubayah,

Armston

2024

Preprint

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“…This ensemble of modelling approaches can help to identify the extent of agreement and uncertainty across modelling approaches, enabling a comprehensive understanding of carbon potential at a global scale. As new satellite technologies, such as the Global Ecosystem Dynamics Investigation (GEDI) project 37 , begin to reveal high-resolution information about forest structure, it will be increasingly important to refine the spatial and temporal resolution of these carbon stock models. Our multimodel and multidata comparison pinpoints regional variation in the main sources of uncertainty in forest carbon potential, highlighting the need for improved aboveground data-sampling efforts in the tropics and soil carbon sampling at high latitudes (Fig.…”

Section: Discussionmentioning

confidence: 99%

Integrated global assessment of the natural forest carbon potential

Mo,

Zohner,

Crowther

et al. 2024

Preprint

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Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system. Remote-sensing estimates to quantify carbon losses from global forests are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced and satellite-derived approaches to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

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“…We used an ordinary least squares (OLS) linear regression model to predict AGBD from GEDI waveform RH metrics in 10% increments from RH10 to RH90, with the addition of RH98 (a more stable indicator of top of canopy height than RH100). We developed a set of 18 candidate models based on relevant literature, using a square root transform on the response [5,28]. We trained each model on half of the FF database (n = 448 310) so that the AGBD models were derived from the same simulated forest stands used in the WFM approach (section 2.4.2).…”

Section: Ols Regression Modelingmentioning

confidence: 99%

“…Relative height (RH) metrics are variables derived from the waveform that give the height above the ground at which a certain quantile of returned energy is reached. These metrics are correlated with AGBD [4] and are used as predictors in GEDI's biomass models [3,5].…”

Section: Introductionmentioning

confidence: 99%

Challenges to aboveground biomass prediction from waveform lidar

Bruening

Fischer

Bohn

et al. 2021

Environ. Res. Lett.

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Accurate accounting of aboveground biomass density (AGBD) is crucial for carbon cycle, biodiversity, and climate change science. The Global Ecosystem Dynamics Investigation (GEDI), which maps global AGBD from waveform lidar, is the first of a new generation of Earth observation missions designed to improve carbon accounting. This paper explores the possibility that lidar waveforms may not be unique to AGBD —that forest stands with different AGBD may produce highly similar waveforms —and we hypothesize that non-uniqueness may contribute to the large uncertainties in AGBD predictions. Our analysis integrates simulated GEDI waveforms from 428 in situ stem maps with output from an individual-based forest gap model, which we use to generate a database of potential forest stands and simulate GEDI waveforms from those stands. We use this database to predict the AGBD of the 428 in situ stem maps via two different methods: a linear regression from waveform metrics, and a waveform-matching approach that accounts for waveform-AGBD non- uniqueness. We find that some in situ waveforms are more unique to AGBD than others, which notably impacts AGBD prediction uncertainty (7-411 Mg ha−1, average of 167 Mg ha−1). We also find that forest structure complexity may influence the non-uniqueness effect; stands with low structural complexity are more unique to AGBD than more mature stands with multiple cohorts and canopy layers. These findings suggest that the non-uniqueness phenomena may be introduced by the measuring characteristics of waveform lidar in combination with how forest structure manifests at small scales, and we discuss how this complexity may complicate uncertainty estimation in AGBD prediction. This analysis suggests a limit to the accuracy and precision of AGBD predictions from lidar waveforms seen in empirical studies, and underscores the need for further exploration of the relationships between lidar remote sensing measurements, forest structure, and AGBD.

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GEDI L4A Footprint Level Aboveground Biomass Density, Version 1

Cited by 8 publications

References 0 publications

Characterizing the structural complexity of the Earth’s forests with spaceborne lidar

Characterizing the structural complexity of the Earth’s forests with spaceborne lidar

Integrated global assessment of the natural forest carbon potential

Challenges to aboveground biomass prediction from waveform lidar

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