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

Recent developments in remote sensing (RS) technology have made several sources of auxiliary data available to support forest inventories. Thus, a pertinent question is how different sources of RS data should be combined with field data to make inventories cost-efficient. Hierarchical model-based estimation has been proposed as a promising way of combining: (i) wall-to-wall optical data that are only weakly correlated with forest structure; (ii) a discontinuous sample of active RS data that are more strongly correlated with structure; and (iii) a sparse sample of field data. Model predictions based on the strongly correlated RS data source are used for estimating a model linking the target quantity with weakly correlated wall-to-wall RS data. Basing the inference on the latter model, uncertainties due to both modeling steps must be accounted for to obtain reliable variance estimates of estimated population parameters, such as totals or means. Here, we generalize previously existing estimators for hierarchical model-based estimation to cases with non-homogeneous error variance and cases with correlated errors, for example due to clustered sample data. This is an important generalization to take into account data from practical surveys. We apply the new estimation framework to case studies that mimic the data that will be available from the Global Ecosystem Dynamics Investigation (GEDI) mission and compare the proposed estimation framework with alternative methods. Aboveground biomass was the variable of interest, Landsat data were available wall-to-wall, and sample RS data were obtained from an airborne LiDAR campaign that produced simulated GEDI waveforms. The results show that generalized hierarchical model-based estimation has potential to yield more precise estimates than approaches utilizing only one source of RS data, such as conventional model-based and hybrid inferential approaches.

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Canadian national tree aboveground biomass equations

Canadian Journal of Forest Research ◽

10.1139/x05-112 ◽

2005 ◽

Vol 35 (8) ◽

pp. 1996-2018 ◽

Cited By ~ 311

Author(s):

M-C Lambert ◽

C-H Ung ◽

F Raulier

Keyword(s):

Aboveground Biomass ◽

National Level ◽

Estimation Method ◽

Seemingly Unrelated Regression ◽

Model Parameters ◽

Total Biomass ◽

Tree Biomass ◽

Biomass Density ◽

Biomass Equations ◽

Error Terms

The estimation of aboveground biomass density (organic dry mass per unit area) is required for balancing Canadian national forest carbon budgets. Tree biomass equations are the basic tool for converting inventory plot data into biomass density. New sets of national tree biomass equations have therefore been produced from archival biomass data collected at the beginning of the 1980s through the ENergy from the FORest research program (ENFOR) of the Canadian Forest Service. Since the sampling plan was not standardized among provinces and territories, data had to be harmonized before any biomass equation could be considered at the national level. Two features characterize the new equations: estimated biomass of the compartments (foliage, branch, wood, and bark) are constrained to equal the total biomass, and dependence among error terms for the considered compartments of the same tree is taken into account in the estimates of both the model parameters and the variance prediction. The estimation method known to economists as seemingly unrelated regression allowed the inclusion of dependencies among the error terms of the considered biomass compartments. Sets of equations based on diameter at breast height (dbh) and on dbh and height have been produced for 33 species, groups of hardwood and softwood, and for all species combined. Biomass predicted by the new equations was compared with that estimated from provincial equations to evaluate the loss of accuracy when scaling up from the regional to the national scale. Bias and error of prediction from the set of national equations based on dbh and height were generally more similar to those from provincial equations than to those of predictions from the set of equations based on dbh alone.

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Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

Remote Sensing ◽

10.3390/rs70403507 ◽

2015 ◽

Vol 7 (4) ◽

pp. 3507-3525 ◽

Cited By ~ 30

Author(s):

Stephen Medeiros ◽

Scott Hagen ◽

John Weishampel ◽

James Angelo

Keyword(s):

Aboveground Biomass ◽

Coastal Marshes ◽

Digital Terrain Models ◽

Biomass Density ◽

Digital Terrain ◽

Terrain Models

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Assessment of Slope-Adaptive Metrics of GEDI Waveforms for Estimations of Forest Aboveground Biomass over Mountainous Areas

Journal of Remote Sensing ◽

10.34133/2021/9805364 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Wenjian Ni ◽

Zhiyu Zhang ◽

Guoqing Sun

Keyword(s):

Aboveground Biomass ◽

Laser Scanner ◽

Shuttle Radar Topography Mission ◽

Ecosystem Dynamics ◽

Estimation Accuracy ◽

Srtm Dem ◽

Mountainous Areas ◽

Using Data ◽

Estimation Models ◽

Forest Aboveground Biomass

Waveform broadening effects of large-footprint lidar caused by terrain slopes are still a great challenge limiting the estimation accuracy of forest aboveground biomass (AGB) over mountainous areas. Slope-adaptive metrics of waveforms were proposed in our previous studies. However, its validation was limited by the unavailability of enough reference data. This study made full validation of slope-adaptive metrics using data acquired by the Global Ecosystem Dynamics Investigation (GEDI) mission, meanwhile exploring GEDI waveforms on estimations of forest AGB. Three types of waveform metrics were employed, including slope-adaptive metrics (RHT), typical height metrics relative to ground peaks (RH), and waveform parameters (WP). In addition to terrain slopes, two other factors were also explored including the geolocation issue and signal start and ending points of waveforms. Results showed that footprint geolocations in the first version GEDI data products were shifted to the left forward of nominal geolocations with a distance of about 24 m~30 m and were substantially corrected in the second version; the fourth and fifth groups of signal start and ending points of waveforms had worse performance than the rest of the four groups because they used the maximum and minimum signal thresholds, respectively. Taking airborne laser scanner (ALS) data as reference, the root mean square error (RMSE) of terrain slopes extracted from the digital elevation model of the shuttle radar topography mission (SRTM DEM) was about 3°. The coefficients of determination (R2) of estimation models of forest AGB based on RH metrics were improved from 0.48 to 0.68 with RMSE decreased from 19.7 Mg/ha to 15.4 Mg/ha by the second version geolocations. The RHT and WP metrics gave the best and the worst estimation accuracy, respectively. RHT further improved R2 to 0.77 and decreased RMSE to 13.0 Mg/ha using terrain slopes extracted from SRTM DEM with a resolution of 1 arc second. R2 of estimation models based on RHT was finally improved to 0.8 with RMSE decreased to 11.7 Mg/ha using exact terrain slopes from ALS data. This study demonstrated the great potential of slope-adaptive metrics of GEDI waveforms on estimations of forest aboveground biomass over mountainous areas.

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Beyond trees: Mapping total aboveground biomass density in the Brazilian savanna using high-density UAV-lidar data

Forest Ecology and Management ◽

10.1016/j.foreco.2021.119155 ◽

2021 ◽

Vol 491 ◽

pp. 119155

Author(s):

Máira Beatriz Teixeira da Costa ◽

Carlos Alberto Silva ◽

Eben North Broadbent ◽

Rodrigo Vieira Leite ◽

Midhun Mohan ◽

...

Keyword(s):

Aboveground Biomass ◽

High Density ◽

Brazilian Savanna ◽

Lidar Data ◽

Biomass Density

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Challenges to aboveground biomass prediction from waveform lidar

Environmental Research Letters ◽

10.1088/1748-9326/ac3cec ◽

2021 ◽

Author(s):

Jamis M Bruening ◽

Rico Fischer ◽

Friedrich J. Bohn ◽

John Armston ◽

Amanda H. Armstrong ◽

...

Keyword(s):

Forest Structure ◽

Aboveground Biomass ◽

Empirical Studies ◽

Structural Complexity ◽

Ecosystem Dynamics ◽

Carbon Accounting ◽

Forest Stands ◽

Waveform Lidar ◽

Structure Complexity

Abstract 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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The Responses of the Quantitative Characteristics of a Ramet Population of the Ephemeroid Rhizomatous SedgeCarex physodesto the Moisture Content of the Soil in Various Locations on Sand Dunes

The Scientific World JOURNAL ◽

10.1155/2014/120186 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10

Author(s):

Buhailiqiemu Abudureheman ◽

Huiliang Liu ◽

Daoyuan Zhang ◽

Kaiyun Guan ◽

Yongkuan Zhang

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Aboveground Biomass ◽

Soil Moisture Content ◽

Soil Depth ◽

Sand Dunes ◽

Morphological Plasticity ◽

Biomass Density ◽

Quantitative Characteristics ◽

Underground Biomass

In this study, the soil moisture content was measured, and the quantitative characteristics of this sedge species were compared. The phenotypic plasticity of each parameter and the linear regression relationships were analyzed. The results showed that the soil moisture content was significantly affected by location, soil depth, and sampling date. The aboveground biomass, underground biomass, biomass density, and population density at the peak were significantly higher than elsewhere on the dune. However, the morphological plasticity index of the quantitative characteristics was higher at the base and middle of the dune. When the soil moisture content decreased, the underground biomass and ramet biomass density increased. The aboveground and underground biomasses were strongly negatively correlated, but the ramet height and aboveground biomass were strongly positively correlated. These results indicated that the soil water content significantly affected the clonal growth ofC. physodes. The responsiveness ofC. physodesmay be adaptive when the soil resource supply is low. The strong morphological plasticity of the species appears to be ecologically important for the maintenance and dominance of this species in the dune habitat.

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Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests

Remote Sensing ◽

10.3390/rs13122279 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2279

Author(s):

Iván Dorado-Roda ◽

Adrián Pascual ◽

Sergio Godinho ◽

Carlos A. Silva ◽

Brigite Botequim ◽

...

Keyword(s):

Aboveground Biomass ◽

Laser Scanning ◽

Large Scale ◽

Forest Type ◽

Coniferous Forest ◽

Canopy Height ◽

Ecosystem Dynamics ◽

Mediterranean Forests ◽

Satellite Mission ◽

The Impact

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

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