Dissertation Research: Biomass estimation and uncertainty analysis: Integrating Bayesian modeling and small-footprint waveform LiDAR data
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Remote sensing is used to characterize and map the spatial variation of Earth''s vegetation and its changes over time. A relatively new airborne laser sensor called LiDAR (Light Detection And Ranging) has been used to accurately characterize the three-dimensional vegetation structure and to measure plant biomass and carbon content. With increased LiDAR data availability, there is a need to develop open source tools for processing these data effectively to obtain the vegetation and terrain information. With current methods, substantial variation and uncertainty remain in estimating the amount and type of vegetation. Support from this Doctoral Dissertation Improvement Grant (DDIG) will be used to develop open source tools to process LiDAR data from the National Ecological Observatory Network (NEON) and to explore their potential for use in identifying tree species, estimating vegetation amounts, and determining the statistical uncertainty in the analysis. The methods developed from this project will facilitate the creation of 3D-vegetation structure and enable the understanding of ecosystem patterns and processes. The quantification of errors and uncertainties of vegetation structure and amount are also conducive to designing effective plans for sustainable forest management and providing accurate inputs for biogeochemical models to inform science-based policy. This research will be accomplished through the following steps: (1) Develop algorithms and open source tools for LiDAR data processing, (2) Segment individual trees and identify tree species with raw LiDAR data alone using Random forests and Bayesian machine learning method, (3) Build different models (step-wise, Random forests, and hierarchical Bayesian models) to estimate forest biomass and carbon stocks using LiDAR metrics and variables derived from point clouds, and (4) Quantify the uncertainty of estimations in different processing stages based on different approaches and model parameters. The algorithms and processing methodologies developed in this proposal will provide open source tools for LiDAR waveform processing and enhance the use and value of NEON data. Moreover, the products of this research will assist forest managers to better manage precious natural resources and make more informed decisions.
