Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
Autor(en): | Duncanson, Laura Kellner, James R. Armston, John Dubayah, Ralph Minor, David M. Hancock, Steven Healey, Sean P. Patterson, Paul L. Saarela, Svetlana Marselis, Suzanne Silva, Carlos E. Bruening, Jamis Goetz, Scott J. Tang, Hao Hofton, Michelle Blair, Bryan Luthcke, Scott Fatoyinbo, Lola Abernethy, Katharine Alonso, Alfonso Andersen, Hans-Erik Aplin, Paul Baker, Timothy R. Barbier, Nicolas Bastin, Jean Francois Biber, Peter Boeckx, Pascal Bogaert, Jan Boschetti, Luigi Boucher, Peter Brehm Boyd, Doreen S. Burslem, David F. R. P. Calvo-Rodriguez, Sofia Chave, Jerome 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 de-Miguel, Sergio Deng, Songqiu Ellis, Peter Woods Erasmus, Barend Fekety, Patrick A. Fernandez-Landa, Alfredo Ferraz, Antonio Fischer, Rico Fisher, Adrian G. Garcia-Abril, Antonio Gobakken, Terje Hacker, Jorg M. 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 Kral, Kamil Krucek, Martin Labriere, Nicolas Lewis, Simon L. Longo, Marcos Lucas, Richard M. Main, Russell Manzanera, Jose A. Martinez, Rodolfo Vasquez Mathieu, Renaud Memiaghe, Herve Meyer, Victoria Mendoza, Abel Monteagudo Monerris, Alessandra Montesano, Paul Morsdorf, Felix Naesset, Erik Naidoo, Laven Nilus, Reuben O'Brien, Michael Orwig, David A. Papathanassiou, Konstantinos Parker, Geoffrey Philipson, Christopher Phillips, Oliver L. Pisek, Jan Poulsen, John R. Pretzsch, Hans Rudiger, Christoph Saatchi, Sassan Sanchez-Azofeifa, Arturo Sanchez-Lopez, Nuria Scholes, Robert Silva, Carlos A. Simard, Marc Skidmore, Andrew Sterenczak, Krzysztof Tanase, Mihai Torresan, Chiara Valbuena, Ruben Verbeeck, Hans Vrska, Tomas Wessels, Konrad White, Joanne C. White, Lee J. T. Zahabu, Eliakimu Zgraggen, Carlo |
Stichwörter: | Aboveground biomass; AIRBORNE LIDAR; BOREAL FOREST; CANOPY STRUCTURE; CARBON; Environmental Sciences; Environmental Sciences & Ecology; Forest; GEDI; GROUND BIOMASS; HEIGHT; Imaging Science & Photographic Technology; INVERSION; LiDAR; Modeling; REGIONS; Remote Sensing; TROPICAL FOREST BIOMASS; VEGETATION; Waveform | Erscheinungsdatum: | 2022 | Herausgeber: | ELSEVIER SCIENCE INC | Journal: | REMOTE SENSING OF ENVIRONMENT | Volumen: | 270 | Zusammenfassung: | NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available. |
ISSN: | 0034-4257 | DOI: | 10.1016/j.rse.2021.112845 |
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geprüft am 19.05.2024