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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.

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.

This paper is a product of the T-FORCES forest monitoring network (Tropical Forests in the Changing Earth System), supported by an ERC Advanced Grant to O.L.P. and by many institutions, NGOs, government agencies and local communities in Malaysia, Brunei, and Indonesia. We are grateful for historical plot data contributed by the Center for Tropical Forest Science (CTFS; two LAM plots and one BEL plot), the Global Ecosystem Monitoring network (GEM; two LAM plots), Institute for Biodiversity and Environmental Research, Universiti Brunei Darussalam (Brunei plots), Kagoshima University (KIS and KIU plots), Forest Department Sarawak (BKO, LAM, MER and GMU plots), Forest Research Centre, Sabah Forestry Department (SEP plots), the Tropenbos Kalimantan project (ITCI plots), Project Barito Ulu, supported by Indonesian Institute of Sciences (LIPI) (BUL plots), and the STREK project, supported by CIRAD, The Ministry of Forestry of Indonesia, and INHUTANI I (STR plots). We are indebted to a great many individuals who contributed to historical data collection. Contemporary fieldwork was supported by a grant from the ERC (T-FORCES) and from NERC (grants NER/A/S/2000/00532, NE/B503384/1, NE/N012542/1). L.Q. was supported by T-FORCES, CIFOR and NERC NE/P00363X/1. S.L.L. was supported by a Royal Society University Research Fellowship, T-FORCES and a Phillip Leverhulme Prize. O.L.P. is supported by T-FORCES and a Royal Society Wolfson Research Merit Award. M.J.P.S. is supported by T-FORCES and NERC NE/N012542/1. L.F.B. was supported by a NERC studentship to the University of Leeds and a RGS-IBG Henrietta Hutton grant. R.H. was supported by a University of Brunei Darussalam Research Fellowship (2011) and a long-term research project RVO 67985939 from the Czech Academy of Sciences. S.L. received additional support from Primate Conservation Inc. M.S. was supported by a Ministry of Education, Youth and Sports grant of the Czech Republic INGO II LG15051. R.R.E.V. was supported by the Netherlands Foundation for the Advancement of Tropical Research (WOTRO, grant No. W76-217). We thank Forest Department Sarawak, Sabah Biodiversity Centre, Sabah Forestry Department, Forest Department Brunei, Institute for Biodiversity and Environmental Research, University of Brunei Darussalam and Indonesia Ministry of Research, Technology, and Higher Education for research permissions. We thank Bako National Park, Lambir Hills National Park, Gunung Mulu National Park, Kuala Belalong Field Study Centre (KBFSC), Glen Reynolds (SEARRP), Danum Valley Conservation Area, Rainforest Discovery Centre Sepilok, Sepilok Laut Reception Centre, Borneo Orangutan Survival Foundation (BOSF), Sungai Wain Protection Forest Management Unit, WWF East Kalimantan and PT. ITCIKU East Kalimantan for logistical support for fieldwork. We thank Timothy Baker, Roel Brienen, Emanuel Gloor, Adriane Esquivel Muelbert and Nicolas Labrière for comments on the manuscript. We thank our deceased colleagues, John Proctor and Suriantata, for their invaluable contributions to both historical work and our wider understanding of tropical forest ecology. ; Peer reviewed ; Publisher PDF

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 ...