18 results on '"Fatoyinbo L"'
Search Results
2. Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- Author
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Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, Rüdiger, C, Duncanson, L, Duncanson, L, Kellner, JR, Armston, J, Dubayah, R, Minor, DM, Hancock, S, Healey, SP, Patterson, PL, Saarela, S, Marselis, S, Silva, CE, Bruening, J, Goetz, SJ, Tang, H, Hofton, M, Blair, B, Luthcke, S, Fatoyinbo, L, Abernethy, K, Alonso, A, Andersen, HE, Aplin, P, Baker, TR, Barbier, N, Bastin, JF, Biber, P, Boeckx, P, Bogaert, J, Boschetti, L, Boucher, PB, Boyd, DS, Burslem, DFRP, Calvo-Rodriguez, S, Chave, J, Chazdon, RL, Clark, DB, Clark, DA, Cohen, WB, Coomes, DA, Corona, P, Cushman, KC, Cutler, MEJ, Dalling, JW, Dalponte, M, Dash, J, de-Miguel, S, Deng, S, Ellis, PW, Erasmus, B, Fekety, PA, Fernandez-Landa, A, Ferraz, A, Fischer, R, Fisher, AG, García-Abril, A, Gobakken, T, Hacker, JM, Heurich, M, Hill, RA, Hopkinson, C, Huang, H, Hubbell, SP, Hudak, AT, Huth, A, Imbach, B, Jeffery, KJ, Katoh, M, Kearsley, E, Kenfack, D, Kljun, N, Knapp, N, Král, K, Krůček, M, Labrière, N, Lewis, SL, Longo, M, Lucas, RM, Main, R, Manzanera, JA, Martínez, RV, Mathieu, R, Memiaghe, H, Meyer, V, Mendoza, AM, Monerris, A, Montesano, P, Morsdorf, F, Næsset, E, Naidoo, L, Nilus, R, O'Brien, M, Orwig, DA, Papathanassiou, K, Parker, G, Philipson, C, Phillips, OL, Pisek, J, Poulsen, JR, Pretzsch, H, and Rüdiger, C
- 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 AG
- Published
- 2022
3. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- Author
-
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.-E., Aplin, P., Baker, T.R., Barbier, N., Bastin, J.F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Brehm Boucher, P., 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., Jeffery, K.J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, Nikolai, Král, K., Krůček, M., Labrière, N., Lewis, S.L., Longo, M., Lucas, R.M., Main, R., Manzanera, J.A., Vásquez Martínez, 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, 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., 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.-E., Aplin, P., Baker, T.R., Barbier, N., Bastin, J.F., Biber, P., Boeckx, P., Bogaert, J., Boschetti, L., Brehm Boucher, P., 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., Jeffery, K.J., Katoh, M., Kearsley, E., Kenfack, D., Kljun, N., Knapp, Nikolai, Král, K., Krůček, M., Labrière, N., Lewis, S.L., Longo, M., Lucas, R.M., Main, R., Manzanera, J.A., Vásquez Martínez, 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, 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., and Zgraggen, C.
- 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 AG
- Published
- 2022
4. Aboveground Woody Biomass Product Validation Good Practices Protocol
- Author
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Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, Kim, Carter, S., Chave, J., Herold, M., MacBean, N., McRoberts, R., Minor, D., Paul, K., Réjou-Méchain, M., Roxburgh, S., Williams, M., Albinet, C., Baker, T., Bartholomeus, H., Bastin, J. F., Coomes, D., Crowther, T., Davies, S., de Bruin, S., De Kauwe, M., Domke, G., Dubayah, R., Falkowski, M., Fatoyinbo, L., Goetz, S., Jantz, P., Jonckheere, I., Jucker, T., Kay, H., Kellner, J., Labriere, N., Lucas, R., Mitchard, E., Morsdorf, F., Næsset, E., Park, T., Phillips, O. L., Ploton, P., Puliti, S., Quegan, S., Saatchi, S., Schaaf, C., Schepaschenko, D., Scipal, K., Stovall, A., Thiel, C., Wulder, M. A., Camacho, F., Nickeson, J., Román, M., Margolis, H., Duncanson, Laura, Disney, Mat, Armston, John, Nickeson, JJaime, Minor, David, and Camacho, Fernando
- Subjects
Agriculture and Food Sciences ,cavelab - Published
- 2021
5. Aboveground Woody Biomass Product Validation Good Practices Protocol. Version 1.0
- Author
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Duncanson, L., Armston, J., Disney, M., Avitabile, V., Barbier, N., Calders, K., Carter, S., Chave, J., Herold, M., MacBean, N., McRoberts, R., Minor, D., Paul, K., Réjou-Méchain, M., Roxburgh, S., Williams, M., Albinet, C., Baker, T., Bartholomeus, H., Bastin, J.F., Coomes, D., Crowther, T., Davies, S., de Bruin, S., De Kauwe, M., Domke, G., Dubayah, R., Falkowski, M., Fatoyinbo, L., Goetz, S.
- Published
- 2021
- Full Text
- View/download PDF
6. Aboveground Woody Biomass Product Validation Good Practices Protocol. Version 1.0
- Author
-
Duncanson, Laura, Armston, J., Disney, M., Avitabile, V., Barbier, Nicolas, Calders, K., Carter, S., Chave, Jérôme, Herold, M., MacBean, N., McRoberts, R., Minor, D., Paul, K., Réjou-Méchain, M., Roxburgh, S., Williams, M., Albinet, C., Baker, T., Bartholomeus, H., Bastin, Jean-Francois, Coomes, D., Crowther, T., Davies, S., de Bruin, S., De Kauwe, Martin, Domke, G., Dubayah, Ralph, Falkowski, M., Fatoyinbo, L., Goetz, S., Jantz, P., Jonckheere, I., Jucker, T., Kay, H., Kellner, J., Labrière, Nicolas, Lucas, R., Mitchard, E., Morsdorf, F., Næsset, E., Park, T., Philipps, O., Ploton, Pierre, Puliti, S., Quegan, S., Saatchi, S., Schaaf, C., Schepaschenko, D, Scipal, K., Stovall, A., Thiel, C., Wulder, Michael A., Camacho, F., Nickeson, J., Román, M., Margolis, H., University of Maryland [Baltimore], University College of London [London] (UCL), European Commission - Joint Research Centre [Ispra] (JRC), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Computational & Applied Vegetation Ecology (CAVElab), Universiteit Gent = Ghent University [Belgium] (UGENT), Faculty of Bioscience Engineering [Ghent], Wageningen University and Research [Wageningen] (WUR), Evolution et Diversité Biologique (EDB), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, University of Minnesota [Morris], University of Minnesota System, NERC National Centre for Earth Observation (NCEO), Natural Environment Research Council (NERC), European Space Agency (ESA), Université de Liège - Gembloux, University of New South Wales [Sydney] (UNSW), USDA Forest Service Rocky Mountain Forest and Range Experiment Station, United States Department of Agriculture (USDA), Universität Zürich [Zürich] = University of Zurich (UZH), German Aerospace Center (DLR), NASA Headquarters, Duncanson, L, Disney, M., Armston, J., Nickeson, J., Minor, D., and Camacho, F.
- Subjects
Biomass mapping ,Lidar ,Allometry ,[SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems ,Woody Biomass ,Good Practices Protocol ,Satellite data ,[SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,Terrestrial laser ,[SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2021
7. Space‐Borne Cloud‐Native Satellite‐Derived Bathymetry (SDB) Models Using ICESat‐2 And Sentinel‐2
- Author
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Thomas, N., primary, Pertiwi, A. P., additional, Traganos, D., additional, Lagomasino, D., additional, Poursanidis, D., additional, Moreno, S., additional, and Fatoyinbo, L., additional
- Published
- 2021
- Full Text
- View/download PDF
8. Future carbon emissions from global mangrove forest loss
- Author
-
Adame, M.F., primary, Connolly, R.M., additional, Turschwell, M.P., additional, Lovelock, C.E., additional, Fatoyinbo, L., additional, Lagomasino, D., additional, Goldberg, L.A., additional, Holdorf, J., additional, Friess, D.A., additional, Sasmito, SD., additional, Sanderman, J., additional, Sievers, M., additional, Buelow, C., additional, Kauffman, B.J., additional, Bryan-Brown, D., additional, and Brown, C.J., additional
- Published
- 2020
- Full Text
- View/download PDF
9. Foreword to the Special Issue on Forest Structure Estimation in Remote Sensing
- Author
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Dubois-Fernandez, P., primary, Fatoyinbo, L., additional, Hajnsek, I., additional, Saatchi, S., additional, and Scipal, K., additional
- Published
- 2018
- Full Text
- View/download PDF
10. Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission
- Author
-
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.E., 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. R., 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., 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.E., 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. R., 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., and Rüdiger, C.
- 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 AG
11. Author Correction: Global hotspots of salt marsh change and carbon emissions.
- Author
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Campbell AD, Fatoyinbo L, Goldberg L, and Lagomasino D
- Published
- 2023
- Full Text
- View/download PDF
12. Global hotspots of salt marsh change and carbon emissions.
- Author
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Campbell AD, Fatoyinbo L, Goldberg L, and Lagomasino D
- Subjects
- Sea Level Rise, Satellite Imagery, United States, Russia, Cyclonic Storms, Soil Erosion, Carbon analysis, Carbon Sequestration, Wetlands, Internationality, Geographic Mapping
- Abstract
Salt marshes provide ecosystem services such as carbon sequestration
1 , coastal protection2 , sea-level-rise (SLR) adaptation3 and recreation4 . SLR5 , storm events6 , drainage7 and mangrove encroachment8 are known drivers of salt marsh loss. However, the global magnitude and location of changes in salt marsh extent remains uncertain. Here we conduct a global and systematic change analysis of Landsat satellite imagery from the years 2000-2019 to quantify the loss, gain and recovery of salt marsh ecosystems and then estimate the impact of these changes on blue carbon stocks. We show a net salt marsh loss globally, equivalent to an area double the size of Singapore (719 km2 ), with a loss rate of 0.28% year-1 from 2000 to 2019. Net global losses resulted in 16.3 (0.4-33.2, 90% confidence interval) Tg CO2 e year-1 emissions from 2000 to 2019 and a 0.045 (-0.14-0.115) Tg CO2 e year-1 reduction of carbon burial. Russia and the USA accounted for 64% of salt marsh losses, driven by hurricanes and coastal erosion. Our findings highlight the vulnerability of salt marsh systems to climatic changes such as SLR and intensification of storms and cyclones., (© 2022. The Author(s).)- Published
- 2022
- Full Text
- View/download PDF
13. Integrating remote sensing with ecology and evolution to advance biodiversity conservation.
- Author
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Cavender-Bares J, Schneider FD, Santos MJ, Armstrong A, Carnaval A, Dahlin KM, Fatoyinbo L, Hurtt GC, Schimel D, Townsend PA, Ustin SL, Wang Z, and Wilson AM
- Subjects
- Biodiversity, Ecology, Ecosystem, Remote Sensing Technology
- Abstract
Remote sensing has transformed the monitoring of life on Earth by revealing spatial and temporal dimensions of biological diversity through structural, compositional and functional measurements of ecosystems. Yet, many aspects of Earth's biodiversity are not directly quantified by reflected or emitted photons. Inclusive integration of remote sensing with field-based ecology and evolution is needed to fully understand and preserve Earth's biodiversity. In this Perspective, we argue that multiple data types are necessary for almost all draft targets set by the Convention on Biological Diversity. We examine five key topics in biodiversity science that can be advanced by integrating remote sensing with in situ data collection from field sampling, experiments and laboratory studies to benefit conservation. Lowering the barriers for bringing these approaches together will require global-scale collaboration., (© 2022. Springer Nature Limited.)
- Published
- 2022
- Full Text
- View/download PDF
14. TLSLeAF: automatic leaf angle estimates from single-scan terrestrial laser scanning.
- Author
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Stovall AEL, Masters B, Fatoyinbo L, and Yang X
- Subjects
- Forests, Lasers, Photosynthesis, Plant Leaves, Ecosystem, Trees
- Abstract
Leaf angle distribution (LAD) in forest canopies affects estimates of leaf area, light interception, and global-scale photosynthesis, but is often simplified to a single theoretical value. Here, we present TLSLeAF (Terrestrial Laser Scanning Leaf Angle Function), an automated open-source method of deriving LADs from terrestrial laser scanning. TLSLeAF produces canopy-scale leaf angle and LADs by relying on gridded laser scanning data. The approach increases processing speed, improves angle estimates, and requires minimal user input. Key features are automation, leaf-wood classification, beta parameter output, and implementation in R to increase accessibility for the ecology community. TLSLeAF precisely estimates leaf angle with minimal distance effects on angular estimates while rapidly producing LADs on a consumer-grade machine. We challenge the popular spherical LAD assumption, showing sensitivity to ecosystem type in plant area index and foliage profile estimates that translate to c. 25% and c. 11% increases in canopy net photosynthesis (c. 25%) and solar-induced chlorophyll fluorescence (c. 11%). TLSLeAF can now be applied to the vast catalog of laser scanning data already available from ecosystems around the globe. The ease of use will enable widespread adoption of the method outside of remote-sensing experts, allowing greater accessibility for addressing ecological hypotheses and large-scale ecosystem modeling efforts., (© 2021 The Authors. New Phytologist © 2021 New Phytologist Foundation.)
- Published
- 2021
- Full Text
- View/download PDF
15. The large footprint of small-scale artisanal gold mining in Ghana.
- Author
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Barenblitt A, Payton A, Lagomasino D, Fatoyinbo L, Asare K, Aidoo K, Pigott H, Som CK, Smeets L, Seidu O, and Wood D
- Abstract
Gold mining has played a significant role in Ghana's economy for centuries. Regulation of this industry has varied over time and while industrial mining is prevalent in the country, the expansion of artisanal mining, or Galamsey has escalated in recent years. Many of these artisanal mines are not only harmful to human health due to the use of Mercury (Hg) in the amalgamation process, but also leave a significant footprint on terrestrial ecosystems, degrading and destroying forested ecosystems in the region. In this study, the Landsat image archive available through Google Earth Engine was used to quantify the total footprint of vegetation loss due to artisanal gold mines in Ghana from 2005 to 2019 and understand how conversion of forested regions to mining has changed over a decadal period from 2007 to 2017. A combination of machine learning and change detection algorithms were used to calculate different land cover conversions and the timing of conversion annually. Within the study area of southwestern Ghana, our results indicate that approximately 47,000 ha (⨦2218 ha) of vegetation were converted to mining at an average rate of ~2600 ha yr
-1 . The results indicate that a high percentage (~50%) of this mining occurred between 2014 and 2017. Around 700 ha of this mining occurred within protected areas as mapped by the World Database of Protected Areas. In addition to deforestation, increased artisanal mining activity in recent years has the potential to affect human health, access to drinking water resources and food security. This work expands upon limited research into the spatial footprint of Galamsey in Ghana, complements mapping efforts by local geographers, and will support efforts by the government of Ghana to monitor deforestation caused by artisanal mining., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)- Published
- 2021
- Full Text
- View/download PDF
16. Trees outside forests are an underestimated resource in a country with low forest cover.
- Author
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Thomas N, Baltezar P, Lagomasino D, Stovall A, Iqbal Z, and Fatoyinbo L
- Abstract
Trees outside forests (TOF) are an underrepresented resource in forest poor nations. As a result of their frequent omission from national forest resource assessments and a lack of readily available very-high-resolution remotely sensed imagery, TOF status and characterization has until now, been unknown. Here, we assess the capacity of openly available 10 m ESA Sentinel constellation satellite imagery for mapping TOF extent at the national level in Bangladesh. In addition, we estimate canopy height for TOF using a TanDEM-X DEM. We map 2,233,578 ha of TOF in Bangladesh with a mean canopy height of 7.3 m. We map 31 and 53% more TOF than existing estimates of TOF and forest, respectively. We find TOF in Bangladesh is nationally fragmented as a consequence of agricultural activity, yet is capable of maintaining connectedness between remaining stands. Now, TOF accounting is feasible at the national scale using readily available datasets, enabling the mainstream inclusion of TOF in national forest resource assessments for other countries.
- Published
- 2021
- Full Text
- View/download PDF
17. Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in Liberia and Gabon.
- Author
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de Sousa C, Fatoyinbo L, Neigh C, Boucka F, Angoue V, and Larsen T
- Subjects
- Datasets as Topic, Ecosystem, Gabon, Liberia, Cloud Computing, Machine Learning, Maps as Topic
- Abstract
Liberia and Gabon joined the Gaborone Declaration for Sustainability in Africa (GDSA), established in 2012, with the goal of incorporating the value of nature into national decision making by estimating the multiple services obtained from ecosystems using the natural capital accounting framework. In this study, we produced 30-m resolution 10 classes land cover maps for the 2015 epoch for Liberia and Gabon using the Google Earth Engine (GEE) cloud platform to support the ongoing natural capital accounting efforts in these nations. We propose an integrated method of pixel-based classification using Landsat 8 data, the Random Forest (RF) classifier and ancillary data to produce high quality land cover products to fit a broad range of applications, including natural capital accounting. Our approach focuses on a pre-classification filtering (Masking Phase) based on spectral signature and ancillary data to reduce the number of pixels prone to be misclassified; therefore, increasing the quality of the final product. The proposed approach yields an overall accuracy of 83% and 81% for Liberia and Gabon, respectively, outperforming prior land cover products for these countries in both thematic content and accuracy. Our approach, while relatively simple and highly replicable, was able to produce high quality land cover products to fill an observational gap in up to date land cover data at national scale for Liberia and Gabon., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2020
- Full Text
- View/download PDF
18. Ecology: Vast peatlands found in the Congo Basin.
- Author
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Fatoyinbo L
- Subjects
- Congo, Ecology
- Published
- 2017
- Full Text
- View/download PDF
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