Theses and Dissertations
ORCID
0009-0006-9273-5144
Advisor
Renninger, Heidi
Committee Member
Bhattarai, Rajeev
Committee Member
Paudel, Krishna
Committee Member
Du, Qian (Jenny)
Date of Degree
8-12-2026
Original embargo terms
Immediate Worldwide Access
Document Type
Graduate Thesis - Open Access
Major
Forestry
Degree Name
Master of Science (M.S.)
College
College of Forest Resources
Department
Department of Forestry
Abstract
Eastern cottonwood (Populus deltoides) and its hybrids are high-yield, fast-growing short-rotation woody crops with strong potential in bioenergy and industrial applications. Given their genetic diversity, these species require non-invasive assessment of photosynthetic, leaf biochemical, and water-use efficiency traits. These traits reflect plant physiological status, carbon cycling, productivity, as well as nutrient cycling, and hyperspectral remote sensing provides high-resolution spectral information essential for high-throughput phenotyping. Therefore, this study develops a robust framework to predict physiological traits using Unmanned Aerial Vehicle (UAV)-acquired canopy-level hyperspectral reflectance (~400 nm - 1000 nm, with 2.2 nm band spacing) by integrating linear and non-linear machine learning models. This study identified important spectral indices and bands from the visible, red-edge, and near-infrared region that can be used in forestry applications for rapid estimation of physiological traits beyond the limitations of traditional lab-based methods.
Sponsorship (Optional)
US Department of Energy grant # (DE-EE0009280)
Recommended Citation
Paudel, Bipin, "Estimating leaf physiological traits in eastern cottonwood and its hybrids by integrating UAV hyperspectral images with statistical and machine learning approaches" (2026). Theses and Dissertations. 7033.
https://scholarsjunction.msstate.edu/td/7033
