[pccgrads] David Diaz General Exam April 1st - Mining Public Data to Enhance Forest Assessment, Monitoring, and Modeling

David D. Diaz diazsefs at uw.edu
Tue Mar 12 12:36:31 PDT 2019

David Diaz General Exam

Monday, April 1st, 11:00 AM

Data Science Studio, eScience Center, 6th Floor of Physics/Astronomy Tower

Chair: Greg Ettl

Committee Members: David Beck, Beth Gardner, Eric Turnblom, Andrew Gray

Title: Mining Public Data to Enhance Forest Assessment, Monitoring, and

Abstract: The rapidly-increasing availability of data from remote sensing
and field-based tree and forest inventory observations is offering
unprecedented opportunities for new information about forests to be gleaned
and learned from. Three new interventions for data acquisition, processing,
and predictive modeling in Pacific Northwest forests will be presented: (1)
probabilistic co-registration of point clouds with forest inventory plots;
(2) automated delineation and labeling of forest conditions; and
(3) assimilating inventory and tree ring data to bring climatic effects to
the Forest Vegetation Simulator (FVS) growth-and-yield model. These
research efforts employ and integrate diverse methods ranging from
three-dimensional geometric modeling and visualization, cloud-based
processing of satellite imagery and lidar point clouds, hierarchical
Bayesian modeling, and the application of computer vision and machine
learning algorithms. These activities are pursued to develop, document, and
demonstrate reproducible open-source tools and workflows for using
publicly-available data to assess forest conditions, monitor how they
change over time, and generate stochastic growth-and-yield projections that
reflect potential climate impacts and forest management options at
operational management scales. To maximize potential transferability and
utility, new growth-and-yield approaches involve the targeted development
of new features for a model (FVS) employed at every state and federal
agency that manages forestland in the Pacific Northwest.
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