globalchange  > 影响、适应和脆弱性
DOI: 10.1111/gcb.13139
论文题名:
An integrated pan-tropical biomass map using multiple reference datasets
作者: Avitabile V.; Herold M.; Heuvelink G.B.M.; Lewis S.L.; Phillips O.L.; Asner G.P.; Armston J.; Ashton P.S.; Banin L.; Bayol N.; Berry N.J.; Boeckx P.; de Jong B.H.J.; Devries B.; Girardin C.A.J.; Kearsley E.; Lindsell J.A.; Lopez-Gonzalez G.; Lucas R.; Malhi Y.; Morel A.; Mitchard E.T.A.; Nagy L.; Qie L.; Quinones M.J.; Ryan C.M.; Ferry S.J.W.; Sunderland T.; Laurin G.V.; Gatti R.C.; Valentini R.; Verbeeck H.; Wijaya A.; Willcock S.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2016
卷: 22, 期:4
起始页码: 1406
结束页码: 1420
语种: 英语
英文关键词: Aboveground biomass ; Carbon cycle ; Forest inventory ; Forest plots ; REDD+ ; Remote sensing ; Satellite mapping ; Tropical forest
Scopus关键词: biomass ; carbon cycle ; climate change ; data set ; forest inventory ; mapping ; pollution control ; tropical forest ; Amazonia ; Central America ; Congo Basin ; Southeast Asia ; biomass ; information processing ; map ; theoretical model ; tree ; tropic climate ; Biomass ; Datasets as Topic ; Maps as Topic ; Models, Theoretical ; Trees ; Tropical Climate
英文摘要: We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha-1 vs. 21 and 28 Mg ha-1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets. © 2016 John Wiley & Sons Ltd.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/61422
Appears in Collections:影响、适应和脆弱性

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作者单位: Centre for Geo-Information, Wageningen University, Droevendaalsesteeg 3, Wageningen, Netherlands; School of Geography, University of Leeds, University Road, Leeds, West Yorkshire, United Kingdom; Department of Geography, University College London, Gower Street, London, United Kingdom; Carnegie Institution for Science, 260 Panama St., Stanford, CA, United States; The University of Queensland, Brisbane, QLD, Australia; Department of Science, Information Technology and Innovation, Remote Sensing Centre, GPO Box 5078, Brisbane, QLD, Australia; Organismic and Evolutionary Biology, Harvard University, 26 Oxford St, Cambridge, MA, United States; Royal Botanic Gardens, Kew Richmond Surrey, United Kingdom; Centre for Ecology and Hydrology, Bush Estate, Penicuik Midlothian, United Kingdom; FRM Ingenierie, 60 rue Henri Fabre, Mauguio - Grand Montpellier, France; Institute of Geography, The University of Edinburgh, Drummond Street, Edinburgh, United Kingdom; Isotope Bioscience Laboratory, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Gent, Belgium; ECOSUR-Campeche, Parque Industrial Lerma, Av. Rancho Polígono 2A, Campeche, Mexico; School of Geography and the Environment, University of Oxford, South Parks Road, Oxford, United Kingdom; Laboratory for Wood Biology and Xylarium, Royal Museum for Central Africa, Leuvensesteenweg 13, Tervuren, Belgium; The RSPB Centre for Conservation Science, The Lodge, Potton Road, Sandy, Bedfordshire, United Kingdom; Centre for Ecosystem Science, The University of New South Wales, Sydney, NSW, Australia; Universidade Estadual de Campinas, Rua Monteiro Lobato 255, Campinas, Brazil; SarVision, Agro Business Park 10, Wageningen, Netherlands; Universiti Brunei Darussalam, Jln Tungku Link, Gadong, Brunei Darussalam, Brunei Darussalam; Center for International Forestry Research, PO Box 0113 BOCBD, Bogor, Indonesia; Centro Euro-Mediterraneo sui Cambiamenti Climatici, Iafes Division, via Pacinotti 5, Viterbo, Italy; Department of Innovation of Biological Systems, Tuscia University, Via S. Camillo de Lellis, Viterbo, Italy; Centre for Biological Sciences, The University of Southampton, Highfield Campus, Southampton, United Kingdom

Recommended Citation:
Avitabile V.,Herold M.,Heuvelink G.B.M.,et al. An integrated pan-tropical biomass map using multiple reference datasets[J]. Global Change Biology,2016-01-01,22(4)
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