Mapping Global Live Woody Vegetation Biomass at Optimum Spatial Resolutions
Bibliographic record
Abstract
Mapping Global Live Woody Vegetation Biomass at Optimum Spatial Resolutions Yifan Yu1, Sassan Saatchi1,2, Yan Yang1,2, Liang Xu1, Victoria Meyer1, Esteban Álvarez-Dávila3 , Valerio Avitabile4, André Beaudoin5, Georges Boundzanga6 , Matt Bradford7, Jerome Chave8, David Clark9, Matoko K. Dabney10, Stuart J. Davies11, Grant Domke12, Alvaro Duque13, William Farfan-Rios14, Antonio Ferraz2, Alexander Fore1, Sangram Ganguly15, Mariano García16, Nancy Harris17, Martin Herold18, Michael Keller19, Nicolas Labrière8, Michael Lefsky20, Renato A.F. de Lima21, Destin Loge Lokegna11, Marcos Longo1, Richard Lucas22, Ronald McRoberts23, Manchiraju Murthy24, Erik Næsset25, Ramakrishna Nemani16, Jean Ometto26, John Poulsen27, Jon Ranson28, Juan Saldarriaga29, Aurelie Shapiro30, Herman Shugart31, Miles Silman14, Ferry Slik32, Guoqing Sun33, Rajesh B. Thapa34, Alexander Vibrans35, Lee White36 Christopher Woodall37, Ulrike Seibt38 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA 2 Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA 3Escuela de Ciencias Agrícolas, Pecuarias y del Medio Ambiente, National Open University and Distance, Bogotá, Colombia 4European Commission, Joint Research Centre, 21027 Ispra (VA), Italy 5Centre de foresterie des Laurentides / Laurentian Forestry Centre, Québec (Québec) G1V 4C7 6CN-REDD, Brazzaville, République du Congo 7Commonwealth Scientific and Industrial Research, Organization (CSIRO) Land and Water, Tropical Forest Research Centre, Atherton, Australia; 8CNRS Unité Evolution et Diversité Biologique, Université Paul Sabatier, 31062 Toulouse, France 9Department of Biology, University of Missouri-St. Louis, St. Louis, Missouri 63121 USA 10Ministère de l’Economie Forestière, Centre National d’Inventaire et Aménagement des Forêts (CNIAF) of the Republic of Congo 11Forest Global Earth Observatory, Smithsonian Tropical Research Institute, PO Box 37012, Washington, DC 20013, USA 12US Department of Agriculture, Forest Service, St. Paul MN, USA 13Socioecosistemas y Cambio Climatico, Fundacion con Vida, Medellín, Colombia. 14Department of Biology, Wake Forest University, Winston-Salem, NC USA 15NASA Ames Research Center, Moffett Field, California, CA 16Department of Geology, Geography and Environment, University of Alcalá, Madrid, Spain 17Research Director, Forest Program, World Resources Institute, Washington DC, USA 18Laboratory of Geo‐Information Science and Remote Sensing, Wageningen University and Research, Wageningen, The Netherlands 19USDA Forest Service, International Institute of Tropical Forestry, San Juan Puerto Rico, USA 20Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, USA 21 Departmento de Ecologia, Instituto de Biociências, Universidade de São Paulo, Rua do Matão, nº 321, 05508-090, São Paulo, SP, Brazil 22Centre for Ecosystem Science, The University of New South Wales, Sydney, Australia 23Department of Forest Resources, University of Minnesota, Saint Paul, Minnesota, USA 24International Centre for Integrated Mountain Development. GPO Box 3226, Kathmandu, Nepal. 25Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, NMBU, Norway 26Earth System Science Centre (CCST), National Institute for Space Research (INPE), José dos Campos, SP, Brazil 27Nicholas School of the Environment, Duke University,Durham, NC U.S.A 28NASA GSFC, Biospheric Sciences Laboratory, Greenbelt, MD, USA 29Carrera 5 No 14-05, Cundinamarca, Colombia 30World Wide Fund for Nature(WWF) Germany Biodiversity Unit, Berlin, Germany 31Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA 32Faculty of Science, Universiti Brunei Darussalam, Gadong, Brunei 33Department of Geographical Sciences, University of Maryland, College Park, MD, USA 34International Centre for Integrated Mountain Development, Khumaltar, Lalitpur, Kathmandu, Nepal 35Universidade Regional de Blumenau – Depto. de Engenharia Florestal, R. São Paulo, SC – Brasil. 36Agence Nationale des Parcs Nationaux, Libreville, Gabon, Ministère de la Forêt, de la Mer, de l'Environnement, Chargé du Plan Climat, Libreville, Gabon. 37United States Forest Service, Northern Research Station–Durham, Durham, NH, USA. 38Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, USA Classification: Physical sciences, environmental sciences Keywords: forest biomass, forest height, carbon cycle, microwave and optical imaging, error propagation, maximum entropy Corresponding Author: Sassan Saatchi Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena, CA 91109 Email: Saatchi@jpl.nasa.gov Tel: +1-818-354-1051 Abstract Carbon emissions from forest disturbance are estimated by the area of disturbance multiplied by the emission factors. The area of disturbance is routinely quantified by high-resolution satellite observations, while emission factors are inferred from forest inventory data at national or global scales. This discrepancy between the scale of disturbance and emission factors is hypothesized to introduce large bias in estimates of annual carbon emissions. To test the hypothesis, we used the 30-m global forest cover change to show that on average 80% of disturbances are less than 10-ha in size and 75% of the time occur within 1-km of past disturbances; together pointing to the optimum scale for quantifying emission factors. Using systematic inventory of forest structure from ground, air, and space and satellite imagery, we map global vegetation live biomass at 100-m spatial resolution (1-ha) with a combined model-based and spatial machine learning estimators. We found large spatial heterogeneity of carbon storage at 1-ha scale related to impacts of disturbance and recovery processes and natural edaphic variations amounting to 428±64 PgC (341±51 PgC above, 87±13 PgC below) partitioned into 328 PgC in forests and 100 PgC in savannas and shrublands. We verified the hypothesis by showing that there was up to 30% bias from underestimating emissions when the scale of emission factors increased and the bias varied geographically with forest types and disturbance regimes. Our results show that fine-scale mapping of biomass carbon density is essential in reducing the uncertainty of carbon emissions and removals from terrestrial ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".