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Quantifying Forest Aboveground Carbon Pools and Fluxes Using Multi-temporal LIDAR

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Sound policy recommendations relating to the role of forest management in mitigating atmospheric carbon dioxide (CO2) depend upon establishing accurate methodologies for quantifying forest carbon pools for large tracts of land that can be dynamically updated over time. Light Detection and Ranging (LiDAR) remote sensing is a promising technology for achieving accurate estimates of aboveground biomass and thereby carbon pools; however, not much is known about the accuracy of estimating biomass change and carbon flux from repeat LiDAR acquisitions containing different data sampling characteristics. In this study, discrete return airborne LiDAR data was collected in 2003 and 2009 across ~20,000 hectares (ha) of an actively managed, mixed conifer forest landscape in northern Idaho, USA. Forest inventory plots, established via a random stratified sampling design, were established and sampled in 2003 and 2009. The Random Forest machine learning algorithm was used to establish statistical relationships between inventory data and forest structural metrics derived from the LiDAR acquisitions. Aboveground biomass maps were created for the study area based on statistical relationships developed at the plot level.


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Citation Vierling, L., Stand, E.K., Hudak, A., Eitel, J.U.H. and Martinuzzi, S., Quantifying Forest Aboveground Carbon Pools and Fluxes Using Multi-Temporal Lidar: A report on field monitoring, remote sensing MMV, GIS integration, and modeling results for forestry field validation test to quantify aboveground tree biomass and carbon. Deliverable Td15. 2011, Montana State University: Bozeman, MT. p. 33
Is NETL associated Yes
NETL Point of Contact William Aljoe
NETL Point of Contact's Email William.Aljoe@NETL.DOE.GOV
NETL program or project DE-FC26-05NT42587 Big Sky Carbon Sequestration Partnership Phase II