Abstract:
The focus of this research will consider the application of Light detection and ranging (Lidar) to forestry and military terrain analysis. Lidar is a remote sensing technology that uses light in the form of a pulsed laser to measure ranges; it can provide a three dimensional image into structures, providing information extraction opportunities for use in civilian and military settings. Previous forestry Lidar research reports strong correlation and acceptable root mean squared error (RMSE) observations. Much of this research was conducted in simple forest conditions and have not been rigorously assessed in areas of more complex plant morphology. The primary objective of this thesis was to explore the suitability of an airborne, discrete return Lidar dataset to estimate tree heights in a dense, forested environment in Beltsville, MD using commercial software. Linear regression was used to relate field to Lidar tree height data with an R2 correlation of 0.0008. Results comparing the Lidar canopy height model to field data by human interpretation had an R2 correlation of 0.33 and an RMSE of 6.54 meters. The Lidar canopy height models explained little to none of the field-observed tree height variation. These results were unexpected considering previous research, but fall in line with recent discussions and efforts to address the complexities and sources of error associated with relating field data to airborne Lidar in dense forest canopies. Future research should include exploration of different software, recently published standards of government agencies and professional societies, and altering data collection parameters.
