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

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dc.contributor.author Duncanson, L
dc.contributor.author Kellner, JR
dc.contributor.author Armston, J
dc.contributor.author Dubayah, R
dc.contributor.author Minora, DM
dc.contributor.author Hancock, S
dc.contributor.author Healey, SP
dc.contributor.author Patterson, PL
dc.contributor.author Main, Russell S
dc.contributor.author Naidoo, Laven
dc.date.accessioned 2022-03-22T07:27:26Z
dc.date.available 2022-03-22T07:27:26Z
dc.date.issued 2022-03
dc.identifier.citation Duncanson, L., Kellner, J., Armston, J., Dubayah, R., Minora, D., Hancock, S., Healey, S. & Patterson, P. et al. 2022. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. <i>Remote Sensing of Environment, 270.</i> http://hdl.handle.net/10204/12333 en_ZA
dc.identifier.issn 0034-4257
dc.identifier.issn 1879-0704
dc.identifier.uri https://doi.org/10.1016/j.rse.2021.112845
dc.identifier.uri http://hdl.handle.net/10204/12333
dc.description.abstract 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 (~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. en_US
dc.format Fulltext en_US
dc.language.iso en en_US
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S0034425721005654 en_US
dc.source Remote Sensing of Environment, 270 en_US
dc.subject Aboveground biomass en_US
dc.subject Forest en_US
dc.subject Global Ecosystem Dynamics Investigation en_US
dc.subject GEDI en_US
dc.subject LiDAR en_US
dc.subject Modeling en_US
dc.subject Waveform en_US
dc.title Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission en_US
dc.type Article en_US
dc.description.pages 20 en_US
dc.description.note © 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license http://creativecommons.org/licenses/by/4.0/). en_US
dc.description.cluster Advanced Agriculture & Food en_US
dc.description.impactarea Precision Agriculture en_US
dc.identifier.apacitation Duncanson, L., Kellner, J., Armston, J., Dubayah, R., Minora, D., Hancock, S., ... Naidoo, L. (2022). Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. <i>Remote Sensing of Environment, 270</i>, http://hdl.handle.net/10204/12333 en_ZA
dc.identifier.chicagocitation Duncanson, L, JR Kellner, J Armston, R Dubayah, DM Minora, S Hancock, SP Healey, PL Patterson, Rusell S Main, and Laven Naidoo "Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission." <i>Remote Sensing of Environment, 270</i> (2022) http://hdl.handle.net/10204/12333 en_ZA
dc.identifier.vancouvercitation Duncanson L, Kellner J, Armston J, Dubayah R, Minora D, Hancock S, et al. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sensing of Environment, 270. 2022; http://hdl.handle.net/10204/12333. en_ZA
dc.identifier.ris TY - Article AU - Duncanson, L AU - Kellner, JR AU - Armston, J AU - Dubayah, R AU - Minora, DM AU - Hancock, S AU - Healey, SP AU - Patterson, PL AU - Main, Rusell S AU - Naidoo, Laven AB - 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 (~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. DA - 2022-03 DB - ResearchSpace DP - CSIR J1 - Remote Sensing of Environment, 270 KW - Aboveground biomass KW - Forest KW - Global Ecosystem Dynamics Investigation KW - GEDI KW - LiDAR KW - Modeling KW - Waveform LK - https://researchspace.csir.co.za PY - 2022 SM - 0034-4257 SM - 1879-0704 T1 - Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission TI - Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission UR - http://hdl.handle.net/10204/12333 ER - en_ZA
dc.identifier.worklist 36348 en_US


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