Abstract
Many advanced satellite estimation methods have been developed, but global forest aboveground biomass (AGB) products remain largely uncertain. In this study, we explored data fusion techniques to generate a global forest AGB map for the 2000s at 0.01-degree resolution with improved accuracy by integrating ten existing local or global maps. The error removal and simple averaging algorithm, which is efficient and makes no assumption about the data and associated errors, was proposed to integrate these ten forest AGB maps. We first compiled the global reference AGB from in situ measurements and high-resolution AGB data that were originally derived from field data and airborne lidar data and determined the errors of each forest AGB map at the pixels with corresponding reference AGB values. Based on the errors determined from reference AGB data, the pixel-by-pixel errors associated with each of the ten AGB datasets were estimated from multiple predictors (e.g., leaf area index, forest canopy height, forest cover, land surface elevation, slope, temperature, and precipitation) using the random forest algorithm. The estimated pixel-by-pixel errors were then removed from the corresponding forest AGB datasets, and finally, global forest AGB maps were generated by combining the calibrated existing forest AGB datasets using the simple averaging algorithm. Cross-validation using reference AGB data showed that the accuracy of the fused global forest AGB map had an R-squared of 0.61 and a root mean square error (RMSE) of 53.68 Mg/ha, which is better than the reported accuracies (R-squared of 0.56 and RMSE larger than 80 Mg/ha) in the literature. Intercomparison with previous studies also suggested that the fused AGB estimates were much closer to the reference AGB values. This study attempted to integrate existing forest AGB datasets for generating a global forest AGB map with better accuracy and moved one step forward for our understanding of the global terrestrial carbon cycle by providing improved benchmarks of global forest carbon stocks.
Highlights
Forest aboveground biomass (AGB) is considered an essential climate variable and plays an important role in the climate system and the global carbon cycle [1]
We extracted the AGB values with a corresponding tree cover of no less than 10% and forest types, including deciduous broadleaf forests (DBF), deciduous needleleaf forests (DNF), evergreen needleleaf forests (ENF), evergreen broadleaf forests (EBF), mixed forests (MF), open shrublands (OSH), closed shrublands (CSH), woody savannas (WSA), and savannas (SAV), and performed statistical analysis of the number of pixels within 20 Mg/ha bins
Pixel-level errors associated with each source AGB map were modeled using the random forests (RF) algorithms, and the results showed that the modeled errors were close to the observation errors, which were calculated from the difference between the reference AGB data and the corresponding gridded AGB extracted from the source map (Figure 2)
Summary
Forest aboveground biomass (AGB) is considered an essential climate variable and plays an important role in the climate system and the global carbon cycle [1]. Ruesch and Gibbs [7] provided the first spatially explicit estimate of vegetation biomass and carbon stocks at a 1-km resolution for the year 2000. They compiled a total of 124 carbon zones or regions with unique carbon stock values using the IPCC (International Panel on Climate Change) Tier-1 method and mapped these unique carbon zones with spatial datasets, including land cover maps, ecoregions zones, and forest age, to generate the gridded carbon stock dataset. Some studies have adopted more advanced approaches for generating global forest AGB maps by integrating field data with multiple satellite datasets and ancillary data using machine learning algorithms. A large number of forest AGB maps have been generated from multisource data using diverse algorithms [14], which offers the possibility of improving the accuracy of AGB mapping by combining this complementary information and advantages of each individual AGB map using data integration algorithms
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
