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Update Frequency: Not Planned
Status: Completed
Online Status: ONLINE
Publication State: Published
Publication Date: 2017-06-07
Download Stats: last 12 months
Dataset Size: 3 Files | 444MB


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.

Citable as:  Rodriguez-Veiga, P.; Balzter, H.; Tansey, K. (2017): AGB-MEX Forrest BIOMASS map for Mexico V1.0. Centre for Environmental Data Analysis, date of citation.
Abbreviation: Not defined
Keywords: Forest biomass, Uncertainty, Forest probability, MODIS, ALOS PALSAR, SRTM, Carbon, MaxEnt, REDD +


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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.

File Format:
GeoTiff, 16 Bit

Process overview

This dataset was generated by the computation detailed below.



The map was generated by means of a combination of the probabilistic outputs from a Maximum Entropy (MaxEnt) algorithm. Approximately 16.000 field inventory plots CONAFOR, INFyS) were used in combination with SAR (JAXA, ALOS PALSAR) and optical data (NASA, MODIS VI), as well as a digital elevation model (NASA, SRTM).

Input Description


Output Description

AGB-MEX output

Software Reference


  • long_name: Biomass
  • gcmd_url:
  • gcmd_keyword: EARTH SCIENCE > Biosphere > Ecological Dynamics > Biomass
  • names: EARTH SCIENCE > Biosphere > Ecological Dynamics > Biomass,

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