An integrated pan-tropical biomass map using multiple reference datasets
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 f...
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/95388 |
| _version_ | 1855532508251160576 |
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| author | Avitabile, Valerio Herold, Martin Heuvelink, G. B. M. Lewis, S.L. Phillips, Oliver L. Asner, G.P. Armston, J. Asthon, P. Banin, Lindsay F. Bayol, N. Berry, N.J. Boeckx, P. Jong, B.H.J. de Vries, B. de Girardin, C. Kearsley, E. Lindsell, J. López Gonzalez, G. Lucas, R. Malhi, Y. Morel, A. Mitchard, E.T.A. Nagy, L. Qie, L. Quinones, M. Ryan, C.M. Slik, F. Sunderland, Terry C.H. Vaglio Laurin, G. Valentini, R. Verbeeck, H. Wijaya, A. Willcock, S. |
| author_browse | Armston, J. Asner, G.P. Asthon, P. Avitabile, Valerio Banin, Lindsay F. Bayol, N. Berry, N.J. Boeckx, P. Girardin, C. Herold, Martin Heuvelink, G. B. M. Jong, B.H.J. de Kearsley, E. Lewis, S.L. Lindsell, J. Lucas, R. López Gonzalez, G. Malhi, Y. Mitchard, E.T.A. Morel, A. Nagy, L. Phillips, Oliver L. Qie, L. Quinones, M. Ryan, C.M. Slik, F. Sunderland, Terry C.H. Vaglio Laurin, G. Valentini, R. Verbeeck, H. Vries, B. de Wijaya, A. Willcock, S. |
| author_facet | Avitabile, Valerio Herold, Martin Heuvelink, G. B. M. Lewis, S.L. Phillips, Oliver L. Asner, G.P. Armston, J. Asthon, P. Banin, Lindsay F. Bayol, N. Berry, N.J. Boeckx, P. Jong, B.H.J. de Vries, B. de Girardin, C. Kearsley, E. Lindsell, J. López Gonzalez, G. Lucas, R. Malhi, Y. Morel, A. Mitchard, E.T.A. Nagy, L. Qie, L. Quinones, M. Ryan, C.M. Slik, F. Sunderland, Terry C.H. Vaglio Laurin, G. Valentini, R. Verbeeck, H. Wijaya, A. Willcock, S. |
| author_sort | Avitabile, Valerio |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | 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. |
| format | Journal Article |
| id | CGSpace95388 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace953882025-06-17T08:24:00Z An integrated pan-tropical biomass map using multiple reference datasets Avitabile, Valerio Herold, Martin Heuvelink, G. B. M. Lewis, S.L. Phillips, Oliver L. Asner, G.P. Armston, J. Asthon, P. Banin, Lindsay F. Bayol, N. Berry, N.J. Boeckx, P. Jong, B.H.J. de Vries, B. de Girardin, C. Kearsley, E. Lindsell, J. López Gonzalez, G. Lucas, R. Malhi, Y. Morel, A. Mitchard, E.T.A. Nagy, L. Qie, L. Quinones, M. Ryan, C.M. Slik, F. Sunderland, Terry C.H. Vaglio Laurin, G. Valentini, R. Verbeeck, H. Wijaya, A. Willcock, S. biomass carbon balance tropical forests forest inventories satellite imagery remote sensing 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. 2016-04 2018-07-03T11:02:53Z 2018-07-03T11:02:53Z Journal Article https://hdl.handle.net/10568/95388 en Limited Access Wiley Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S.L., Phillips, O.L., Asner, G.P., Armston, J., Asthon, P., Banin, L., Bayol, N., Berry, N.J., Boeckx, P., de Jong, B.H.J., DeVries, B., Girardin, C., Kearsley, E., Lindsell, J., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E.T.A., Nagy, L., Qie, L., Quinones, M., Ryan, C.M., Slik, F., Sunderland, T.C.H., Vaglio Laurin, G., Valentini, R., Verbeeck, H., Wijaya, A., Willcock, S.. 2016. An integrated pan-tropical biomass map using multiple reference datasets Global Change Biology, 22 (4) : 1406-1420. https://doi.org/10.1111/gcb.13139 |
| spellingShingle | biomass carbon balance tropical forests forest inventories satellite imagery remote sensing Avitabile, Valerio Herold, Martin Heuvelink, G. B. M. Lewis, S.L. Phillips, Oliver L. Asner, G.P. Armston, J. Asthon, P. Banin, Lindsay F. Bayol, N. Berry, N.J. Boeckx, P. Jong, B.H.J. de Vries, B. de Girardin, C. Kearsley, E. Lindsell, J. López Gonzalez, G. Lucas, R. Malhi, Y. Morel, A. Mitchard, E.T.A. Nagy, L. Qie, L. Quinones, M. Ryan, C.M. Slik, F. Sunderland, Terry C.H. Vaglio Laurin, G. Valentini, R. Verbeeck, H. Wijaya, A. Willcock, S. An integrated pan-tropical biomass map using multiple reference datasets |
| title | An integrated pan-tropical biomass map using multiple reference datasets |
| title_full | An integrated pan-tropical biomass map using multiple reference datasets |
| title_fullStr | An integrated pan-tropical biomass map using multiple reference datasets |
| title_full_unstemmed | An integrated pan-tropical biomass map using multiple reference datasets |
| title_short | An integrated pan-tropical biomass map using multiple reference datasets |
| title_sort | integrated pan tropical biomass map using multiple reference datasets |
| topic | biomass carbon balance tropical forests forest inventories satellite imagery remote sensing |
| url | https://hdl.handle.net/10568/95388 |
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