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Predicting Biomass Distribution in Forests Using Metabolic Theory and EEMT Model

This study by Tyson L. Swetnam from the School of Natural Resources and Environment explores how biomass distribution varies across different environmental gradients. The research integrates metabolic theory, spatial patterns, and the Effective Energy to Mass Transfer (EEMT) model to predict forest productivity based on mean annual temperature and precipitation data. Utilizing LiDAR images processed in USFS FUSION software, the study correlates EEMT with tree height and slope to better understand forest ecosystems.

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Predicting Biomass Distribution in Forests Using Metabolic Theory and EEMT Model

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Presentation Transcript

  1. Seeing the forest for the trees Tyson L. Swetnam School of Natural Resources and Environment

  2. Biomass distribution across gradients • Metabolic theory shows that metabolic rate scales to mass and temperature; and that temperature regulates production. • Process based understanding of spatial pattern allows prediction. • A pedogenic energy model, Effective Energy to Mass Transfer (EEMT) (Rasmussen and Tabor 2007, Durcik and Rasmussen in prep) for mean annual temperature and precipitation might help explain maximum productivity across climatic gradients. Data for Mean Annual Temperature (MAT), and Mean Annual Precipitation (MAP) from the PRISM Climate Group.

  3. Aerial Light Detection and Range (LiDAR) Orthophoto Height Above Ground Images displayed in USFS FUSION software (McGaughey 2008)

  4. EEMT vsTree Height ¾ slope added

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