Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Garrett M. Street

Committee Member

Kristine O. Evans

Committee Member

Brandi B. Karisch

Committee Member

& Joby M. Czarnecki

Date of Degree

8-6-2021

Original embargo terms

Visible to MSU only for 1 year

Document Type

Graduate Thesis - Open Access

Major

Wildlife, Fisheries and Aquaculture

Degree Name

Master of Science

Degree Name

Master of Science (M.S.)

College

College of Forest Resources

College

College of Forest Resources

Department

Department of Wildlife, Fisheries and Aquaculture

Department

Department of Wildlife, Fisheries and Aquaculture

Abstract

Forage quality is a principal factor in managing both herbivores and the landscapes they use. Nutrition varies across the landscape, and in turn, so do the distributions of these populations. With the rise of remote sensing technologies (i.e. satellites, unmanned aerial vehicles, and multi/hyperspectral sensors), comes the ability to index forage health and nutrition swiftly. However, no methodology has been developed which allows managers to use unmanned aerial systems to the fullest capacity. The following methodologies produce compelling evidence for predicting forage quality metrics (such as fiber, carbohydrates, and digestibility) using 5 measured bands of reflectance (Blue, Green, Red, Red Edge, and NIR), 3 derived vegetation indices (NDVI, EVI and VARI), and a variety of environmental factors (i.e. time and sun angles) in a LASSO framework. Fiber content, carbohydrates, and digestibility showed promising model performance in terms of goodness-of-fit (R2= 0.624, 0.637, and 0.639 respectively).

Sponsorship

MAFES

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