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Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- Source :
- Duncanson, L, Kellner, J R, Armston, J, Dubayah, R, Minor, D M, Hancock, S, Healey, S P, Patterson, P L, Saarela, S, Marselis, S, Silva, C E, Bruening, J, Goetz, S J, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, H, Aplin, P, Baker, T R, Barbier, N, Bastin, J F, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, P B, Boyd, D S, Burslem, D F R P, Calvo-rodriguez, S, Chave, J, Chazdon, R L, Clark, D B, Clark, D A, Cohen, W B, Coomes, D A, Corona, P, Cushman, K C, Cutler, M E J, Dalling, J W, Dalponte, M, Dash, J, De-miguel, S, Deng, S, Ellis, P W, Erasmus, B, Fekety, P A, Fernandez-landa, A, Ferraz, A, Fischer, R, Fisher, A G, García-abril, A, Gobakken, T, Hacker, J M, Heurich, M, Hill, R A, Hopkinson, C, Huang, H, Hubbell, S P, Hudak, A T, Huth, A, Imbach, B, Jeffery, K J, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, S L, Longo, M, Lucas, R M, Main, R, Manzanera, J A, Martínez, R V, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, A M, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O’brien, M, Orwig, D A, Papathanassiou, K, Parker, G, Philipson, C, Phillips, O L, Pisek, J, Poulsen, J R, Pretzsch, H, Rüdiger, C, Saatchi, S, Sanchez-azofeifa, A, Sanchez-lopez, N, Scholes, R, Silva, C A, Simard, M, Skidmore, A, Stereńczak, K, Tanase, M, Torresan, C, Valbuena, R, Verbeeck, H, Vrska, T, Wessels, K, White, J C, White, L J T, Zahabu, E & Zgraggen, C 2022, ' Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission ', Remote Sensing of Environment, vol. 270, 112845 . https://doi.org/10.1016/j.rse.2021.112845, Remote Sensing of Environment, 270, Remote sensing of environment, 270:112845, 1-20. Elsevier, Remote Sensing of Environment, Remote Sensing of Environment, 2022, 270, pp.112845. ⟨10.1016/j.rse.2021.112845⟩, Remote sensing of environment 270 (2022): Article number 112845. doi:10.1016/j.rse.2021.112845, info:cnr-pdr/source/autori:Duncanson L., Kellner J.R., Armston J., Dubayah R., Minor D.M., Hancock S., Healey S.P., Patterson P.L., Saarela S., Marselis S., Silva E.C., Bruening J., Goetz J.S., Tang H., Hofton M., Blair B., Luthcke S., Fatoyinbo L., Abernethy K., Alonso A., Andersen H.E., Aplin P., Baker R.T., Barbier N., Bastin J.F., Biber P., Boeckx P., Bogaert J., Boschetti L., Brehm Boucher P., Boyd S.D., Burslem F.R.P.D., Calvo-Rodriguez S., Chave J., Chazdon L.R., Clark B.D., Clark A.D., Cohen B.W., Coomes A.D., Corona P., Cushman K.C., Cutler E.J.M., Dalling W.J., Dalponte M., Dash J., de-Miguel S., Deng S., Woods Ellis P., Erasmus B., Fekety A.P., Fernandez-Landa A., Ferraz A., Fischer R., Fisher G.A., García-Abril A., Gobakken T., Hacker M.J., Heurich M., Hill A.R., Hopkinson C., Huang H., Hubbell P.S., Hudak T.A., Huth A., Imbach B., Jeffery J.K., Katoh M., Kearsley E., Kenfack D., Kljun N., Knapp N., Král K., Kr??ek M., Labrière N., Lewis L.S., Longo M., Lucas M.R., Main R., Manzanera A.J., Martínez V.R., Mathieu R., Memiaghe H., Meyer V., Monteagudo Mendoza A., Monerris A., Montesano P., Morsdorf F., Næsset E., Naidoo L., Nilus R., O'Brien M., Orwig A.D., Papathanassiou K., Parker G., Philipson C., Phillips L.O., Pisek J., Poulsen R.J., Pretzsch h., Rüdiger C., Saatchi S., Sanchez-Azofeifa A., Sanchez-Lopez N., Scholes R., Silva A.C., Simard M., Skidmore A., Stere?czak K., Tanase M., Torresan C., Valbuena R., Verbeeck H., Vrska T., Wessels K., White C.J., White J.T.L., Zahabu E., Zgraggen C./titolo:Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission/doi:10.1016%2Fj.rse.2021.112845/rivista:Remote sensing of environment/anno:2022/pagina_da:Article number 112845/pagina_a:/intervallo_pagine:Article number 112845/volume:270, REMOTE SENSING OF ENVIRONMENT
- Publication Year :
- 2022
-
Abstract
- © 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/). 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.
- Subjects :
- 0106 biological sciences
CANOPY STRUCTURE
LiDAR
010504 meteorology & atmospheric sciences
Soil Science
[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy
Geological & Geomatics Engineering
010603 evolutionary biology
01 natural sciences
Physical Geography and Environmental Geoscience
CARBON
Remote Sensing
ITC-HYBRID
BOREAL FOREST
HEIGHT
[SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems
Settore BIO/07 - ECOLOGIA
GEDI
Waveform
Forest
Aboveground biomass
Modeling
ddc:630
INVERSION
Computers in Earth Sciences
0105 earth and related environmental sciences
GROUND BIOMASS
Geology
VDP::Matematikk og Naturvitenskap: 400
15. Life on land
[SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics
REGIONS
ddc
Geomatic Engineering
13. Climate action
AIRBORNE LIDAR
Earth and Environmental Sciences
ITC-ISI-JOURNAL-ARTICLE
TROPICAL FOREST BIOMASS
cavelab
VEGETATION
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
Subjects
Details
- Language :
- English
- ISSN :
- 00344257 and 18790704
- Database :
- OpenAIRE
- Journal :
- Duncanson, L, Kellner, J R, Armston, J, Dubayah, R, Minor, D M, Hancock, S, Healey, S P, Patterson, P L, Saarela, S, Marselis, S, Silva, C E, Bruening, J, Goetz, S J, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, H, Aplin, P, Baker, T R, Barbier, N, Bastin, J F, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, P B, Boyd, D S, Burslem, D F R P, Calvo-rodriguez, S, Chave, J, Chazdon, R L, Clark, D B, Clark, D A, Cohen, W B, Coomes, D A, Corona, P, Cushman, K C, Cutler, M E J, Dalling, J W, Dalponte, M, Dash, J, De-miguel, S, Deng, S, Ellis, P W, Erasmus, B, Fekety, P A, Fernandez-landa, A, Ferraz, A, Fischer, R, Fisher, A G, García-abril, A, Gobakken, T, Hacker, J M, Heurich, M, Hill, R A, Hopkinson, C, Huang, H, Hubbell, S P, Hudak, A T, Huth, A, Imbach, B, Jeffery, K J, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, S L, Longo, M, Lucas, R M, Main, R, Manzanera, J A, Martínez, R V, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, A M, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O’brien, M, Orwig, D A, Papathanassiou, K, Parker, G, Philipson, C, Phillips, O L, Pisek, J, Poulsen, J R, Pretzsch, H, Rüdiger, C, Saatchi, S, Sanchez-azofeifa, A, Sanchez-lopez, N, Scholes, R, Silva, C A, Simard, M, Skidmore, A, Stereńczak, K, Tanase, M, Torresan, C, Valbuena, R, Verbeeck, H, Vrska, T, Wessels, K, White, J C, White, L J T, Zahabu, E & Zgraggen, C 2022, ' Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission ', Remote Sensing of Environment, vol. 270, 112845 . https://doi.org/10.1016/j.rse.2021.112845, Remote Sensing of Environment, 270, Remote sensing of environment, 270:112845, 1-20. Elsevier, Remote Sensing of Environment, Remote Sensing of Environment, 2022, 270, pp.112845. ⟨10.1016/j.rse.2021.112845⟩, Remote sensing of environment 270 (2022): Article number 112845. doi:10.1016/j.rse.2021.112845, info:cnr-pdr/source/autori:Duncanson L., Kellner J.R., Armston J., Dubayah R., Minor D.M., Hancock S., Healey S.P., Patterson P.L., Saarela S., Marselis S., Silva E.C., Bruening J., Goetz J.S., Tang H., Hofton M., Blair B., Luthcke S., Fatoyinbo L., Abernethy K., Alonso A., Andersen H.E., Aplin P., Baker R.T., Barbier N., Bastin J.F., Biber P., Boeckx P., Bogaert J., Boschetti L., Brehm Boucher P., Boyd S.D., Burslem F.R.P.D., Calvo-Rodriguez S., Chave J., Chazdon L.R., Clark B.D., Clark A.D., Cohen B.W., Coomes A.D., Corona P., Cushman K.C., Cutler E.J.M., Dalling W.J., Dalponte M., Dash J., de-Miguel S., Deng S., Woods Ellis P., Erasmus B., Fekety A.P., Fernandez-Landa A., Ferraz A., Fischer R., Fisher G.A., García-Abril A., Gobakken T., Hacker M.J., Heurich M., Hill A.R., Hopkinson C., Huang H., Hubbell P.S., Hudak T.A., Huth A., Imbach B., Jeffery J.K., Katoh M., Kearsley E., Kenfack D., Kljun N., Knapp N., Král K., Kr??ek M., Labrière N., Lewis L.S., Longo M., Lucas M.R., Main R., Manzanera A.J., Martínez V.R., Mathieu R., Memiaghe H., Meyer V., Monteagudo Mendoza A., Monerris A., Montesano P., Morsdorf F., Næsset E., Naidoo L., Nilus R., O'Brien M., Orwig A.D., Papathanassiou K., Parker G., Philipson C., Phillips L.O., Pisek J., Poulsen R.J., Pretzsch h., Rüdiger C., Saatchi S., Sanchez-Azofeifa A., Sanchez-Lopez N., Scholes R., Silva A.C., Simard M., Skidmore A., Stere?czak K., Tanase M., Torresan C., Valbuena R., Verbeeck H., Vrska T., Wessels K., White C.J., White J.T.L., Zahabu E., Zgraggen C./titolo:Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission/doi:10.1016%2Fj.rse.2021.112845/rivista:Remote sensing of environment/anno:2022/pagina_da:Article number 112845/pagina_a:/intervallo_pagine:Article number 112845/volume:270, REMOTE SENSING OF ENVIRONMENT
- Accession number :
- edsair.doi.dedup.....19d0576946c561982b642f75920f5935
- Full Text :
- https://doi.org/10.1016/j.rse.2021.112845