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

Journal Article · · Remote Sensing of Environment
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  1. Univ. of Maryland, College Park, MD (United States)
  2. Brown Univ., Providence, RI (United States)
  3. Univ. of Edinburgh, Scotland (United Kingdom)
  4. USDA Forest Service, Ogden, UT (United States). Rocky Mountain Research Station
  5. Swedish Univ. of Agricultural Sciences, Umea (Sweden); Norwegian University of Life Sciences, As (Norway)
  6. North Arizona University, Flagstaff, AZ (United States)
  7. Univ. of Maryland, College Park, MD (United States); National Univ. of Singapore (Singapore)
  8. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States)
  9. University of Stirling (United Kingdom); Institute for Research in Tropical Ecology (IRET), Libreville (Gabon); National Center for Scientific and Technological Research (CENAREST), Libreville (Gabon)
  10. Smithsonian Conservation Biology Institute, Washington, DC (United States)
  11. USDA Forest Service, Seattle, WA (United States). Pacific Northwest Research Station; Univ. of Washington, Seattle, WA (United States)
  12. Edge Hill University, Ormskirk (United Kingdom)
  13. University of Leeds (United Kingdom)
  14. University Montpellier (France); Centre National de la Recherche Scientifique (CNRS) (France)
  15. Univ. of Liege, (Belgium)
  16. Technical Univ. of Munich (Germany)
  17. Ghent Univ. (Belgium)
  18. Univ. of Idaho, Moscow, ID (United States)
  19. Harvard Univ., Cambridge, MA (United States)
  20. Univ. of Nottingham (United Kingdom)
  21. Univ. of Aberdeen (United Kingdom)
  22. Univ. of Alberta, Edmonton, AB (Canada)
  23. Centre National de la Recherche Scientifique (CNRS) (France); Evolution and Biological Diversity Laboratory, Toulouse (France); Univ. of Toulouse (France)
  24. Univ. of Connecticut, Storrs, CT (United States); University of the Sunshine Coast, QLD (Australia)
  25. Univ. of Missouri, St. Louis, MO (United States)
  26. USDA Forest Service, Corvallis, OR (United States). Pacific Northwest Research Station
  27. Univ. of Cambridge (United Kingdom)
  28. Council for Agricultural Research and Economics, Arezzo (Italy); University of Tuscia, Viterbo (Italy)
  29. Brown Univ., Providence, RI (United States); Smithsonian Tropical Research Institute, Ancon (Panama)
  30. University of Dundee (United Kingdom)
  31. Smithsonian Tropical Research Institute, Ancon (Panama); Univ. of Illinois at Urbana-Champaign, IL (United States)
  32. Fondazione Edmund Mach, Trento (Italy)
  33. Scion New Zealand Forest Research Institute, Rotorua (New Zealand)
  34. University of Lleida (Spain); Forest Science and Technology Centre of Catalonia (CTFC), (Spain)
  35. Shinshu Univ., Matsumoto, Nagano (Japan)
  36. Nature Conservancy, Arlington, VA (United States)
  37. Univ. of the Witwatersrand, Johannesburg (South Africa); Univ. of Pretoria (South Africa)
  38. Colorado State Univ., Fort Collins, CO (United States)
  39. Agresta Sociedad Cooperativa, Soria (Spain)
  40. California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL); Univ. of California, Los Angeles, CA (United States)
  41. Helmholtz Center for Environmental Research, Leipzig (Germany)
  42. Univ. of New South Wales, Sydney, NSW (Australia); Univ. of Queensland, Brisbane, QLD (Australia)
  43. Polytechnic University of Madrid (UPM) (Spain)
  44. Norwegian University of Life Sciences, As (Norway)
  45. Airborne Research Australia, Parafield Airport (Australia); Flinders Univ., Adelaide, SA (Australia)
  46. Bavarian Forest National Park, Grafenau (Germany); Univ. of Freiburg (Germany); Inland Norway University of Applied Sciences, Koppang (Norway)
  47. Bournemouth University, Poole (United Kingdom)
  48. Univ. of Lethbridge, AB (Canada)
  49. Sun Yat-Sen Univ., Guangzhou (China)
  50. Smithsonian Tropical Research Institute, Ancon (Panama); Univ. of California, Los Angeles, CA (United States)
  51. USDA Forest Service, Moscow, ID (United States). Rocky Mountain Research Station
  52. Helmholtz Centre for Environmental Research, Leipzig (Germany)
  53. Aeroscout, Hochdorf (Switzerland)
  54. University of Stirling (United Kingdom)
  55. Smithsonian Tropical Research Institute, Ancon (Panama)
  56. Lund Univ. (Sweden)
  57. Helmholtz Centre for Environmental Research, Leipzig (Germany); Thunen Institute of Forest Ecosystems, Eberswalde (Germany)
  58. Silva Tarouca Research Institute, Brno (Czech Republic)
  59. Evolution and Biological Diversity Laboratory, Toulouse (France)
  60. University of Leeds (United Kingdom); Univ. College London (United Kingdom)
  61. California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL); Embrapa Agricultural Informatics, Campinas, SP (Brazil)
  62. Aberystwyth University (United Kingdom)
  63. Univ. of Pretoria (South Africa); Council for Scientific and Industrial Research, Pretoria (South Africa)
  64. Jardin Botanico de Missouri, Oxapampa (Peru)
  65. Univ. of Pretoria, Los Banos (South Africa); International Rice Research Institute, Los Banos (Philippines)
  66. Univ. of Oregon, Eugene, OR (United States); Evolution and Biological Diversity Laboratory, Toulouse (France)
  67. California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL); Terraformation, Kamuela, HI (United States)
  68. Jardin Botanico de Missouri, Oxapampa (Peru); Universidad Nacional de San Antonio Abad del Cusco (Peru)
  69. Univ. of Melbourne, Parkville, VIC (Australia)
  70. NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Science Systems and Applications, Lanham, MD (United States)
  71. Univ. of Zurich (Switzerland)
  72. Council for Scientific and Industrial Research, Pretoria (South Africa)
  73. Sabah Forestry Department (Malaysia)
  74. Universidad Rey Juan Carlos, Mostoles (Spain)
  75. Harvard Univ., Petersham, MA (United States)
  76. German Aerospace Center (DLR), Bonn (Germany)
  77. Smithsonian Environmental Research Center, Edgewater, MD (United States)
  78. Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland); Permian Global, London (United Kingdom)
  79. University of Tartu, Toravere(Estonia)
  80. Duke Univ., Durham, NC (United States)
  81. Monash Univ., Clayton, VIC (Australia); Dept. of the Environment, Docklands, VIC (Australia). Bureau of Metereology
  82. California Institute of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL)
  83. Univ. of the Witwatersrand, Johannesburg (South Africa)
  84. Univ. of Florida, Gainesville, FL (United States)
  85. Univ. of Twente, Enschede (Netherlands)
  86. Forest Research Institute, Raszyn (Poland)
  87. Council for Agricultural Research and Economics, Arezzo (Italy); National Research Council (CNR), Trento (Italy)
  88. Swedish Univ. of Agricultural Sciences (SLU), Umea (Sweden); Bangor University (United Kingdom)
  89. George Mason Univ., Fairfax, VA (United States)
  90. Canadian Forest Service, VIC, BC (Canada)
  91. University of Stirling (United Kingdom); Ministry of Forests, Sea, the Environment and Climate Change, Libreville (Gabon)
  92. Sokoine University of Agriculture, Morogoro (Tanzania)
+ Show Author Affiliations

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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP); National Aeronautics and Space Administration (NASA); National Science Foundation (NSF)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1893116
Journal Information:
Remote Sensing of Environment, Journal Name: Remote Sensing of Environment Vol. 270; ISSN 0034-4257
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

54 ENVIRONMENTAL SCIENCES
GEDI
LiDAR
aboveground biomass
forest
modeling
waveform