| 项目编号: | 1702008
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| 项目名称: | DISSERTATION RESEARCH: Biomass estimation and uncertainty analysis: Integrating Bayesian modeling and small-footprint waveform LiDAR data |
| 作者: | Sorin Popescu
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| 承担单位: | Texas A&M AgriLife Research
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| 批准年: | 2017
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| 开始日期: | 2017-06-01
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| 结束日期: | 2018-05-31
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| 资助金额: | 19213
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Biological Sciences - Environmental Biology
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| 英文关键词: | uncertainty
; research
; open source tool
; lidar waveform processing
; doctoral dissertation improvement grant
; plant biomass
; lidar metric
; vegetation
; statistical uncertainty
; neon datum
; lidar datum processing
; forest biomass
; raw lidar datum
; process lidar datum
; lidar datum availability
|
| 英文摘要: | 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/90095
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Sorin Popescu. DISSERTATION RESEARCH: Biomass estimation and uncertainty analysis: Integrating Bayesian modeling and small-footprint waveform LiDAR data. 2017-01-01.
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