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No. de sistema: 000001630

LDR _ _ 00000nab^^22^^^^^za^4500
008 _ _ 160308m20169999xx^^r^p^^^^^^z0^^^a0eng^d
040 _ _ a| ECO
c| ECO
044 _ _ a| xx
245 0 3 a| An integrated pan-tropical biomass map using multiple reference datasets
506 _ _ a| Acceso electrónico sólo para usuarios de ECOSUR
520 1 _ a| 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.
538 _ _ a| Adobe Acrobat profesional 6.0 o superior
610 2 4 a| Reducción de Emisiones por Deforestación y Degradación Forestal
650 _ 4 a| Biomasa aérea
650 _ 4 a| Ciclo del carbono (Biogeoquímica)
650 _ 4 a| Inventarios forestales
650 _ 4 a| Sensores remotos
650 _ 4 a| Bosques tropicales
700 1 _ a| Avitabile, Valerio
700 1 _ a| Herold, Martin
e| coaut.
700 1 _ a| Heuvelink, Gerard B. M.
e| coaut.
700 1 _ a| Lewis, Simon L.
e| coaut.
700 1 _ a| Phillips, Oliver L.
e| coaut.
700 1 _ a| Asner, Gregory P .
e| coaut.
700 1 _ a| Armston, John
e| coaut.
700 1 _ a| Ashton, Peter S.
e| coaut.
700 1 _ a| Banin, Lindsay
e| coaut.
700 1 _ a| Bayol, Nicolás
e| coaut.
700 1 _ a| Berry, Nicholas J.
e| coaut.
700 1 _ a| Boeckx, Pascal
e| coaut.
700 1 _ a| De Jong, Bernardus Hendricus Jozeph
e| coaut.
700 1 _ a| Devri E. S., Ben
e| coaut.
700 1 _ a| Girardin, Cecile A. J.
e| coaut.
700 1 _ a| Kearsley, Elizabeth
e| coaut.
700 1 _ a| Lindsell, Jeremy A.
e| coaut.
700 1 _ a| López González, Gabriela
c| Mtra.
e| coaut.
700 1 _ a| Lucas, Richard
e| coaut.
700 1 _ a| Malhi, Yadvinder
e| coaut.
700 1 _ a| Morel, Alexandra
e| coaut.
700 1 _ a| Mitchard, Edward T. A.
e| coaut.
700 1 _ a| Nagy, Laszlo
e| coaut.
700 1 _ a| Qie, Lan
e| coaut.
700 1 _ a| Quiñones, Marcela J .
e| coaut.
700 1 _ a| Ryan, Casey M.
e| coaut.
700 1 _ a| Ferry, Slik J. W.
e| coaut.
700 1 _ a| Sunderland, Terry
e| coaut.
700 1 _ a| Laurin, Gaia Vaglio
e| coaut.
700 1 _ a| Cazzolla Gatti, Roberto
e| coaut.
700 1 _ a| Valentini, Riccardo
e| coaut.
700 1 _ a| Verbeeck, Hans
e| coaut.
700 1 _ a| Wijaya, Arief
e| coaut.
700 1 _ a| Willcock, Simon
e| coaut.
773 0 _
t| Global Change Biology
g| Vol. 22, no. 4 (April 2016), p. 1406–1420
x| 1365-2486
901 _ _ a| Artículo con arbitraje
902 _ _ a| GOG / MM
904 _ _ a| Marzo 2016
905 _ _ a| Artecosur
905 _ _ a| Servibosques
905 _ _ a| Biblioelectrónica
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Inglés

"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."


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