A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems |
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| Authors: | Dengsheng Lu Qi Chen Guangxing Wang Lijuan Liu Guiying Li Emilio Moran |
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| Institution: | 1. Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, School of Environmental &2. Resource Sciences, Zhejiang A&3. F University, Lin'An, China;4. Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA;5. Department of Geography, University of Hawaii at Mānoa, Honolulu, HI, USA;6. Department of Geography and Environmental Resources, Southern Illinois University at Carbondale, Carbondale, IL, USA;7. Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA |
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| Abstract: | Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling. |
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| Keywords: | aboveground biomass forest ecosystems parametric vs nonparametric algorithms remote sensing uncertainty |
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