MAPPING SPATIOTEMPORAL PATTERNS OF VEGETATION CHANGE WITH REMOTE SENSING

This research will address three main questions:Q1: How important are vertical and horizontal heterogeneity in predicting forest productivity?Long-term objective: Develop a robust, reproducible modeling workflow that combines a three dimensional radiative transfer model with a variety of carbon cycle models to predict carbon uptake at each voxel in a forest stand, then connect this stand scale model to larger areas using emerging remote sensing tools.Sub-objective: The research performed within this scope will set the stage for future research testing the importance of vertical and horizontal heterogeneity in forest under future climate and disturbance scenarios. This modeling workflow will allow users to test theoretical ideas about forest productivity under a range of conditions, from realistic to hypothetical.Q2: Does knowing land use and disturbance history improve estimates of forest productivity?Long-term objective: Use the long-term (40 year) satellite record to map disturbance and land use history across a range of forest types, then test whether these spatiotemporal patterns of disturbance influence patterns of plant physiological and structural traits and, therefore, productivity and carbon uptake.Sub-objective: The research performed within this scope will widen the applicability of this work to areas that are less well studied by relying on the long term satellite record and advanced cloud-based computing platforms like Google Earth Engine.Q3: Can imaging spectroscopy be used across systems and spatiotemporal scales to map taxonomic and functional plant diversity?Long-term objective: Use proximal (hand-held), near Earth (drone and airplane), and satellite-based sensors to understand the relationship between spectral heterogeneity and biodiversity, with a particular focus on the importance of intra-annual variation.Sub-objective: The research performed within this scope will include both forest and pasture measurements, directly tying imaging spectroscopy and biodiversity research to agricultural objectives.