Date of Award
Spring 5-19-2016
Document Type
Thesis
Degree Name
Master of Arts (MA)
Department
Geography
First Advisor
Dr. Wenge Ni-Meister
Second Advisor
Dr. Carsten Kessler
Academic Program Adviser
Dr. Hongmian Gong
Abstract
NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level.
Recommended Citation
Marrs, Julia K., "Exploring Data Mining Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data" (2016). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/75
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons, Other Forestry and Forest Sciences Commons