Issuing Body

Mississippi State University

Advisor

Evans, David L.

Committee Member

Renninger, Heidi J.

Committee Member

Sabatia, Charles O.

Committee Member

Vilella, Francisco J.

Date of Degree

1-1-2017

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

College

College of Forest Resources

Department

Department of Forestry

Abstract

In previous research, longleaf pine was shown to be spectrally separable from loblolly pine when using high-resolution multispectral data from the WorldView-2 imaging satellite. However, analysis of such high-resolution datasets would be computationally inefficient over a large landscape such as the southeastern United States. Therefore, the objective of this thesis was to approximate the minimum spatial resolution required to separate these two southern pine species. A pan-sharpened, spectrally subset (NIR bands only) WorldView-2 dataset was spatially resampled from 0.46m to 0.5m, 1.0m, 2.0m, 4.0m, 8.0m, and 16.0m. Supervised classification was performed on each of these resampled resolutions. The results of the overall accuracies of these classifications showed that 2.0m is the approximate minimum spatial resolution required to accurately separate these species. Classification accuracy drops between 2.0m and 4.0m as pixel sizes more closely approximate tree crown sizes and spectral variance increases.

URI

https://hdl.handle.net/11668/19215

Comments

forestry||remote sensing

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