e5fb0e74b8104302a43e8a24dc45e038 English 8-bit variable size UCS Transfer Format, based on ISO/IEC 10646 82b29f96b8c94db28ecc51a479f8c9c6 dataset Centre for Environmental Data Analysis (CEDA) 01235446432 Rutherford Appleton Laboratory Harwell Oxon OX11 0QX United Kingdom support@ceda.ac.uk pointOfContact 2026-05-06T02:16:31 UK GEMINI 2.3 WGS 84 AGB-MEX Forrest BIOMASS map for Mexico V1.0 2017-06-07T11:12:52 publication 2017-06-07T11:12:52 creation https://catalogue.ceda.ac.uk/uuid/e5fb0e74b8104302a43e8a24dc45e038 e5fb0e74b8104302a43e8a24dc45e038 Centre for Environmental Data Analysis (CEDA) Ground data from the National Forest and Soil Inventory of Mexico (INFyS) were used to calibrate a maximum entropy (MaxEnt) algorithm to generate forest biomass (AGB), its associated uncertainty, and forest probability maps. The input predictor layers for the MaxEnt algorithm were extracted from the moderate resolution imaging spectrometer (MODIS) vegetation index (VI) products, ALOS PALSAR L-band dual-polarization backscatter coefficient images, and the Shuttle Radar Topography Mission (SRTM) digital elevation model. A Jackknife analysis of the model accuracy indicated that the ALOS PALSAR layers have the highest relative contribution (50.9%) to the estimation of AGB, followed by MODIS-VI (32.9%) and SRTM (16.2%). The forest cover mask derived from the forest probability map showed higher accuracy (κ = 0.83) than alternative masks derived from ALOS PALSAR (κ = 0.72–0.78) or MODIS vegetation continuous fields (VCF) with a 10% tree cover threshold (κ = 0.66). The use of different forest cover masks yielded differences of about 30 million ha in forest cover extent and 0.45 Gt C in total carbon stocks. The AGB map showed a root mean square error (RMSE) of 17.3 t C ha− 1 and R2 = 0.31 when validated at the 250 m pixel scale with inventory plots. The error and accuracy at municipality and state levels were RMSE = ± 4.4 t C ha− 1, R2 = 0.75 and RMSE = ± 2.1 t C ha− 1, R2 = 0.94 respectively. We estimate the total carbon stored in the aboveground live biomass of forests of Mexico to be 1.69 Gt C ± 1% (mean carbon density of 21.8 t C ha− 1), which agrees with the total carbon estimated by FAO for the FRA 2010 (1.68 Gt C). The new map, derived directly from the biomass estimates of the national inventory, proved to have similar accuracy as existing forest biomass maps of Mexico, but is more representative of the shape of the probability distribution function of AGB in the national forest inventory data. Our results suggest that the use of a non-parametric maximum entropy model trained with forest inventory plots, even at the sub-pixel size, can provide accurate spatial maps for national or regional REDD + applications and MRV systems. Rodriguez-Veiga, Pedro Unavailable author Unavailable author Balzter, Heiko Unavailable author Unavailable author Tansey, Kevin Unavailable author Unavailable author Centre for Environmental Data Analysis (CEDA) custodian 01235446432 Rutherford Appleton Laboratory Harwell Oxon OX11 0QX United Kingdom support@ceda.ac.uk custodian Centre for Environmental Data Analysis (CEDA) distributor 01235446432 Rutherford Appleton Laboratory Harwell Oxon OX11 0QX United Kingdom support@ceda.ac.uk distributor Natural Environment Research Council (NERC) principal_investigator 01793 411500 Polaris House North Star Avenue Swindon SN2 1EU United Kingdom unknown principalInvestigator Centre for Environmental Data Analysis (CEDA) point_of_contact 01235446432 Rutherford Appleton Laboratory Harwell Oxon OX11 0QX United Kingdom support@ceda.ac.uk pointofContact Rodriguez-Veiga, Pedro Unavailable point_of_contact Unavailable pointofContact Centre for Environmental Data Analysis (CEDA) publisher 01235446432 Rutherford Appleton Laboratory Harwell Oxon OX11 0QX United Kingdom support@ceda.ac.uk publisher notPlanned dataset Forest biomass Uncertainty Forest probability MODIS ALOS PALSAR SRTM Carbon MaxEnt REDD + orthoimagery theme GEMET - INSPIRE themes, version 1.0 2008-06-01 publication otherRestrictions Public data: access to these data is available to both registered and non-registered users. otherRestrictions Under the following licence http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/, appropriate use of these data may fall under any use. This message is intended as guidance, always read the full licence. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. grid English imageryBaseMapsEarthCover -120.97411104 -84.7086110403 13.7652723077 34.2672723077 2004-01-01T00:00:00 2012-12-31T23:59:59 GeoTiff, 16 Bit Centre for Environmental Data Analysis (CEDA) Data Center Contact 01235446432 Rutherford Appleton Laboratory Harwell Oxon OX11 0QX United Kingdom support@ceda.ac.uk distributor https://catalogue.ceda.ac.uk/uuid/e5fb0e74b8104302a43e8a24dc45e038 CEDA Data Catalogue Page Detail and access information for the resource information http://data.ceda.ac.uk/neodc/nceo_biomass_maps/data/AGB-MEX/ DOWNLOAD Download Data download http://dx.doi.org/10.1016/j.rse.2016.06.004 Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico.data, algorithm, methods, uncertainty characterization and validation) No further details. information dataset Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 Data provided by Pedro Rodriguez-Veiga of University of Leicester as part of NCEO Terrestrial Carbon and Vegetation workplan area . This work was supported by Copernicus Initial Operations - Network for Earth Observation Research Training (GIONET). GIONET was funded by the European Commission, Marie Curie Programme, Initial Training Networks, Grant Agreement number PITN-GA-2010-264509. Pedro Rodriguez-Veiga and Heiko Balzter were supported by the NERC National Centre for Earth Observation (NCEO). Heiko Balzter was also supported by the Royal Society Wolfson Research Merit Award, 2011/R3.