Mississippi State University
Garrett M. Street
Kristine O. Evans
Brandi B. Karisch
& Joby M. Czarnecki
Date of Degree
Original embargo terms
Visible to MSU only for 1 year
Graduate Thesis - Campus Access Only
Wildlife, Fisheries and Aquaculture
Master of Science
College of Forest Resources
Department of Wildlife, Fisheries and Aquaculture
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).
Norman, Durham Alexander, "Linking remotely-sensed UAS imagery to forage quality in an experimental grazing system" (2021). Theses and Dissertations. 5203.