Date of Degree
Earth & Environmental Sciences
Sean C. Ahearn
Geographic Information Sciences | Remote Sensing
forest canopy, lidar, map services, on-demand, simulation, web mapping
Current methods of mapping forest canopy structure often result in data products that are limited in resolution, coverage, or ease of access. On-demand processing introduces several new ways in which existing data products can be combined and re-purposed, mitigating some of these limitations. In this research, we investigate several methods of extending the spatial and temporal resolution, coverage, and accessibility of existing forest canopy datasets by processing them on demand. These methods include downscaling coarse-resolution canopy height data dynamically to estimate height at 30 m and 1 m resolution for any location within the contiguous United States. A related method involves sampling individual trees from field measurements on demand to estimate local forest canopy characteristics, using globally-available remotely sensed data and field data from across the United States. Canopy height profiles, which are highly sensitive to horizontal canopy variability, are generated on demand for any location within North America using new methods that account for this variability. Trends in canopy coverage and above-ground biomass are generated for any location globally using methods sensitive to local conditions. Each of the techniques developed as part of this research extends the resolution, coverage, or ease-of-access of existing remote sensing datasets, by combining multiple existing resources on demand.
Green, Gordon M., "Mapping Forest Canopy Structure with On-Demand Fusion of Remotely Sensed Data" (2014). CUNY Academic Works.