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.
Highlights
Forests cover about 30 percent of the Earth’s land surface, providing renewable materials and energy for humans, maintaining biodiversity, preventing soil erosion, and playing a major role in the global carbon cycle and climate system [1,2]
Liu et al [105] derived global forest biomass carbon estimates from 1993 to 2012 with a spatial resolution of 0.25 degrees, from the empirical relationship between the above-ground biomass (AGB) tropical regions map from Saatchi et al [19] and the vegetation optical depth (VOD) data estimated from a series of passive microwave data including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), FengYun-3B Microwave Radiometer Imager (MWRI) and Windsat
Several forest AGB maps might be available, but they may exhibit discrepancies in both magnitude and spatial distribution. Their agreements and discrepancies were assessed by metrics including the difference maps, the Fuzzy Numerical (FN) index, and variograms used in previous studies, for example, the comparison of available Uganda forest AGB maps in Avitabile et al [108], for the conterminous USA by Neeti and Kennedy [109], and for pan-tropical maps in Mitchard et al [110]
Summary
Forests cover about 30 percent of the Earth’s land surface, providing renewable materials and energy for humans, maintaining biodiversity, preventing soil erosion, and playing a major role in the global carbon cycle and climate system [1,2]. Countries with existing national forest inventory (NFI) data typically use field measurements together with biomass factors and biomass equations to estimate regional means of forest biomass and biomass change for the national forest resources report [7,8] These cannot provide spatially explicit forest biomass information either. To estimate forest AGB accurately on a regional scale, much effort is currently being made to integrate field data with remotely sensed data including optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) data using advanced methods [10,11,12]. Many scholars have integrated field measurements, LiDAR, optical and/or SAR data using advanced methods to generate maps of the spatial distribution of forest AGB on a regional scale [18,19]. The paper is organized as follows: Section 2 introduces the principles for using remote-sensingderived parameters for AGB estimation; Section 3 describes the currently available regional and global forest biomass maps; Section 4 compares the available forest biomass maps over several regions; Section 5 includes the limitations of current forest AGB maps and possible directions to improve their accuracy; and Section 6 is a brief summary
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