Dissertation Title: Remote sensing of vegetation structure and composition
using computer vision
My research focuses on the development and use of a low-cost
remote sensing system comprised of off-the-shelf digital cameras,
hobbyist remote controlled aircraft and computer vision
for measuring tree and forest canopy 3D structure and
composition at fine spatial scale. Broadly, I am
interested in using remote sensing for studying patterns of forest structure and composition
across different types of natural and anthropogenic landscapes.
For my dissertation research, I am focused on
understanding how forest canopies are measured by a computer
vision structure from motion system and also in understanding
how we can use this new combination of existing technologies to
improve understanding of forest ecosystems. For example, a
key research interest that could be advanced by Ecosynth would
be in improving understanding of the spatial and temporal
patterns of canopy structure and spectral traits at fine-spatial scale throughout the growing
season. My research is primarily based on the UMBC campus
in Baltimore MD, but I also study the forest at the
Smithsonian Environmental Research Center (SERC) in
Dandois, J.P. and E.C. Ellis (2010). Remote Sensing of Vegetation Structure and Using Computer Vision.
Remote Sensing, 2(4): 1157-1176.
GES497 -- GES Undergraduate Internship: I am currently the instructor for a GES internship
credit for undergraduate students. Our interns are helping
out with total station surveying and forest inventory of our
forest sites on campus, working on Python code development for
georeferencing and data processing, and helping to maintain
current RC aircraft (Hexakopters) and develop new flying systems.