Date of Award:
Doctor of Philosophy (PhD)
Dr. Michael A. White
Terrestrial vegetation plays an important role in global carbon cycling and climate change by assimilating carbon into biomass during the growing season and releasing it due to natural or anthropogenic disturbances. Remote sensing and ecosystem models can help us extend our studies of vegetation phenology, aboveground biomass, and disturbances from field sites to regional or global scales. Nonetheless, remote sensing-derived variables may differ in fundamental and important ways from ground measurements. With the growth of remote sensing as a key tool in geoscience research, comparisons to ground data and intercomparisons among satellite products are needed. Here I conduct three separate but related analyses and show promising comparisons of key ecosystem states and processes derived from remote sensing and theoretical modeling to those observed on the ground. First, I show that the Moderate Resolution Imaging Spectroradiometer (MODIS) greenup product is significantly correlated with the earliest ground phenology event for North America. Spring greenup indices from different satellites demonstrate similar variability along latitudes, but the number of ground phenology observations in summer, fall, and winter is too limited to interpret the remote sensing-derived phenology products. Second, I estimate aboveground biomass (AGB) for California and show that it agrees with inventory-based regional biomass assessments. In this approach, I present a new remote sensing-based approach for mapping live forest AGB based on a simple parametric model that combines high-resolution estimates of Leaf Area Index derived from Landsat and canopy maximum height from the space-borne Geoscience Laser Altimeter System (GLAS) sensor. Third, I built a theoretical model to estimate stand age in primary forests by coupling a carbon accumulation function to the probability density of disturbance occurrences, and then ran the model with satellite-derived AGB and net primary production. The validated remote sensing data, integrated with ecosystem models, are particularly useful for large-region vegetation research in areas with sparse field measurements, and will help us to explore the long-term vegetation dynamics.
Zhang, Gong, "Integrating Remote Sensing and Ecosystem Models for Terrestrial Vegetation Analysis: Phenology, Biomass, and Stand Age" (2012). All Graduate Theses and Dissertations. 1316.
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .