A Review of Regional and Global Gridded Forest Biomass Datasets

Abstract

Forest biomass quantification is essential to the global carbon cycle and climate studies. Many studies have estimated forest biomass from a variety of data sources, and consequently generated some regional and global maps. However, these forest biomass maps are not well known and evaluated. In this paper, we reviewed an extensive list of currently available forest biomass maps. For each map, we briefly introduced the data sources, the algorithms used, and the associated uncertainties. Large-scale biomass datasets were compared across Europe, the conterminous United States, Southeast Asia, tropical Africa and South America. Results showed that these forest biomass datasets were almost entirely inconsistent, particularly in woody savannas and savannas across these regions. The uncertainties in biomass maps could be from a variety of sources including the chosen allometric equations used to calculate field data, the choice and quality of remotely sensed data, as well as the algorithms to map forest biomass or extrapolation techniques, but these uncertainties have not been fully quantified. We suggested the future directions for generating more accurate large-scale forest biomass maps should concentrate on the compilation of field biomass data, novel approaches of forest biomass mapping, and comprehensively addressing the accuracy of generated biomass maps.

An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products

“…The validation result was somehow overoptimistic because some of the reference data were compiled from satellite-derived biomass datasets with spatial resolutions of 500 m and 1 km. Previous studies suggested that a global forest biomass map provided by Yang et al [23] was close to that from Hu et al [8], when validated with field reference AGB or evaluated in terms of spatial distribution [11,71]. Therefore, compared with previous studies, this study provided a feasible way to produce accurate forest AGB maps on a global scale.…”

“…Based on both types of data, many forest AGB maps were produced at local, regional, or global scales using various algorithms [2][3][4][5][6][7][8]. However, due to the uncertainties associated with the selection of allometric equations to calculate field biomass and the uncertainties in remote sensing datasets and the algorithms used for AGB prediction, substantial uncertainties remain in current AGB estimates [11][12][13]. To improve the accuracy of AGB estimation, some recent studies have proposed that efforts should be made to compile field biomass extensively, integrate multiple remote sensing datasets, explore novel approaches, and comprehensively address the uncertainty associated with biomass estimates [11,[14][15][16].…”

A New Method for Generating a Global Forest Aboveground Biomass Map From Multiple High-Level Satellite Products and Ancillary Information

“…We used other regional and global forest AGB maps (see Table III) to do the comparison with our map. The global forest AGB map produced by Zhang and Liang [55] (subsequently called the Z map) was not yet published and was obtained through contact with the author. We calculated the difference maps from the pixel value of our forest AGB estimated map minus the corresponding pixel value of the other forest AGB maps.…”

“…1) North America: For North America, we used forest AGB maps derived by Thurner et al [58] (the T map), Hu et al [57] (the H map), and Zhang and Liang [55] (the Z map) for comparison with our forest AGB map.…”

A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps

“…If current global forest cover maps have issues estimating existing tree cover and restoration potential in dry biomes, then what are the possible solutions? First, there are alternative maps of global forest cover and biomass that use microwave (SAR; Synthetic Aperture Radar) data instead of MODIS and Landsat optical data (Martone et al., 2018; Shimada et al., 2014; Zhang, Liang, & Yang, 2019). Because they can penetrate cloud cover and potentially map wet season tree cover, SAR‐derived maps could be more accurate in drylands and are a promising approach.…”