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Open AccessReview

A Review of Regional and Global Gridded Forest Biomass Datasets

Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Department of Geographical Sciences, University of Maryland, College Park, MD 20740, USA
College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2744;
Received: 26 October 2019 / Revised: 15 November 2019 / Accepted: 19 November 2019 / Published: 22 November 2019
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. View Full-Text
Keywords: forest biomass maps; large-scale mapping; field biomass; remotely sensed data; uncertainty analysis forest biomass maps; large-scale mapping; field biomass; remotely sensed data; uncertainty analysis
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MDPI and ACS Style

Zhang, Y.; Liang, S.; Yang, L. A Review of Regional and Global Gridded Forest Biomass Datasets. Remote Sens. 2019, 11, 2744.

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