The Ohio state university electroscience laboratory
In: IEEE Antennas and Propagation Society Newsletter, Band 25, Heft 1, S. 4-10
ISSN: 2168-0329
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In: IEEE Antennas and Propagation Society Newsletter, Band 25, Heft 1, S. 4-10
ISSN: 2168-0329
Aim: The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location: Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods: Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results: The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by >???25%, whereas regional uncertainties for the maps were reported to be ??5%. Main conclusions: Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.
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© 2014 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd. ; The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. ; Gordon and Betty Moore Foundation ; European Union's Seventh Framework Programme ; ERC ; NERC ; PRONEX -FAPEAM/CNPq. ; Hidroveg FAPESP/FAPEAM ; Universal/CNPq. ; INCT-CENBAM ; Investissement d'Avenir grants of the French ANR. ; Royal Society Fellowship ; Royal Society Wolfson Research Merit Award
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Open Acess journal ; While Amazonian forests are extraordinarily diverse, the abundance of trees is skewed strongly towards relatively few 'hyperdominant' species. In addition to their diversity, Amazonian trees are a key component of the global carbon cycle, assimilating and storing more carbon than any other ecosystem on Earth. Here we ask, using a unique data set of 530 forest plots, if the functions of storing and producing woody carbon are concentrated in a small number of tree species, whether the most abundant species also dominate carbon cycling, and whether dominant species are characterized by specific functional traits. We find that dominance of forest function is even more concentrated in a few species than is dominance of tree abundance, with only ≈1% of Amazon tree species responsible for 50% of carbon storage and productivity. Although those species that contribute most to biomass and productivity are often abundant, species maximum size is also influential, while the identity and ranking of dominant species varies by function and by region. ; Gordon and Betty Moore Foundation ; European Union Seventh Framework Programme ; ERC ; Natural Environment Research Council ; PRONEX—FAPEAM/CNPq ; Hidroveg FAPESP/FAPEAM ; Universal/CNPq ; INCT-CENBAM ; Fitogeografia da Transição Amazônia/Cerrado CNPq ; Transição Amazônia/Cerrado ; French ANR - Investissement d'Avenir grants ; CNPq ; Royal Society - Wolfson Research Merit Award ; Dutch Ministry of Economic Affairs
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Estimates of extinction risk for Amazonian plant and animal species are rare and not often incorporated into land-use policy and conservation planning. We overlay spatial distribution models with historical and projected deforestation to show that at least 36% and up to 57% of all Amazonian tree species are likely to qualify as globally threatened under International Union for Conservation of Nature (IUCN) Red List criteria. If confirmed, these results would increase the number of threatened plant species on Earth by 22%. We show that the trends observed in Amazonia apply to trees throughout the tropics, and we predict that most of the world's >40,000 tropical tree species now qualify as globally threatened. A gap analysis suggests that existing Amazonian protected areas and indigenous territories will protect viable populations of most threatened species if these areas suffer no further degradation, highlighting the key roles that protected areas, indigenous peoples, and improved governance can play in preventing large-scale extinctions in the tropics in this century. ; Alberta Mennega Stichting ; ALCOA Suriname ; Amazon Conservation Association ; Banco de la República ; CELOS Suriname ; CAPES (PNPG) ; Conselho Nacional de Desenvovimento Científico e Tecnológico of Brazil (CNPq) Projects CENBAM, PELD (558069/2009-6), PRONEX-FAPEAM (1600/2006), Áreas Úmidas, MAUA; PELD (403792/2012-6), PPBio, PVE 004/2012, Universal (479599/2008-4), and Universal 307807- 2009-6 ; FAPEAM projects DCR/2006, Hidroveg with FAPESP, and PRONEX with CNPq ; FAPESP ; Colciencias ; CONICIT ; Duke University ; Ecopetrol ; FEPIM 044/2003 ; The Field Museum ; Conservation International/DC (TEAM/Instituto Nacional de Pesquisas da Amazônia Manaus ; Gordon and Betty Moore Foundation ; Guyana Forestry Commission ; Investissement d'Avenir grant of the French ANR (CEBA: ANR-10-LABX-0025 ; IVIC ; Margaret Mee Amazon Trust ; Miquel fonds ; MCTI–Museu Paraense Emílio Goeldi–Proc. 407232/2013-3–PVE-MEC/MCTI/CAPES/CNPq; National Geographic Society (7754-04 and 8047-06 to P.M.J.; 6679-99, 7435-03, and 8481-08 to T.W.H.); NSF-0726797 to K.R.Y ; NSF Dissertation Improvement ; Netherlands Foundation for the Advancement of Tropical Research WOTRO (grants WB85-335 and W84-581) ; Primate Conservation Inc. ; Programme Ecosystèmes Tropicaux (French Ministry of Ecology and Sustainable Development) ; Shell Prospecting and Development Peru ; Smithsonian Institution's Biological Diversity of the Guiana Shield Program ; Stichting het van Eeden-fonds ; The Body Shop ; The Ministry of the Environment of Ecuador ; TROBIT ; Tropenbos International ; U.S. National Science Foundation (NSF-0743457 and NSF-0101775 to P.M.J.; NSF-0918591 to T.W.H.) ; USAID ; Variety Woods Guyana ; Wenner-Gren Foundation ; WWF-Brazi ; WWF-Guianas ; XIIéme Contrat de Plan Etat Région-Guyane (French Government and European Union) ; European Union ; UK Natural Environment Research Counci ; European Research Council ; Royal Society Wolfson Research Merit Award
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