Document Type

Dissertation - Open Access

Award Date


Degree Name

Doctor of Philosophy (PhD)

Department / School

Geospatial Science and Engineering

First Advisor

Geoffrey M. Henebry


This dissertation seeks to investigate hyperspectral and waveform LiDAR datasets through a new analytical framework called Moment Distance method that uses a metric derived from the shape of the curve (spectral or waveform). In the case of hyperspectral data, the shape of the reflectance curve should unmask fine points of the spectra usually not considered by existing band-specific indices. To explore the advantages and shortcomings of this new approach, leaf and canopy hyperspectral reflectance samples were simulated using the physicallybased models PROSPECT (a leaf model) and SAIL (a canopy model). Sensitivity analysis was conducted with the goal of understanding the sensitivity of the new framework to leaf and canopy parameters relative to other existing and widely-used vegetation indices. The analysis evaluated the efficiency of the new approach to overcome, for instance, the decreased sensitivity of the NDVI at moderate to high Chl content or LAI. With waveform LiDAR data, the new approach was tested through the characterization of the canopy height without the typical step of fitting a series of Gaussian curves to the waveform to identify key peaks. Furthermore, the effects of noise and smoothing procedures on the metrics framework were assessed by introducing different types and levels of noise and various smoothing window sizes.

Library of Congress Subject Headings

Plant canopies -- Remote sensing
Spectral imaging
Spectral reflectance


Includes bibliographical references



Number of Pages



South Dakota State University


Copyright © 2014 Eric Ariel L. Salas