Abstract
Abstract Existing forest biomass stock maps show discrepancies with in-situ observations in Mexico. 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 R 2 = 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 , R 2 = 0.75 and RMSE = ± 2.1 t C ha − 1 , R 2 = 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.
Highlights
Forests sequester carbon through photosynthesis and store it primarily as living aboveground biomass of trees (AGB)
The most strongly correlated remote sensing data layer to biomass is the ALOS PALSAR HVpolarized backscatter followed by the moderate resolution imaging spectrometer (MODIS) MIR reflectance (Fig. 4)
The current study presents a feasible approach to estimate forest probability, AGB and its associated uncertainty using in-situ data and a combination of freely available Earth observation datasets
Summary
Forests sequester carbon through photosynthesis and store it primarily as living aboveground biomass of trees (AGB). AGB is defined as the mass of living organic material for a given area, and approximately 50% of AGB is carbon (IPCC, 2003). Because of the slow turnover time of AGB, it is a key quantity when estimating terrestrial carbon stocks. Deforestation and forest degradation are considered to be the largest source of greenhouse gas emissions in many tropical countries (Gibbs, Brown, Niles, & Foley, 2007). Monitoring and reporting the AGB of forests is a requirement of international policies to mitigate climate change through the reduction of greenhouse gas emissions from deforestation and forest degradation, as well as the enhancement of existing forest carbon stocks (REDD+, Reduction of Emissions form Deforestation and Forest Degradation). The implementation of REDD+ includes an element of measurement, reporting and verification (MRV) for which appropriate systems have to be developed at national, sub-national or project levels
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