Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Frey, Brent R.

Committee Member

Renninger, Heidi J.

Committee Member

Evans, David L.

Date of Degree

1-1-2019

Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science

College

College of Forest Resources

Department

Department of Forestry

Abstract

Light Detection and Ranging (LiDAR) is increasingly common in forestry applications, yet relatively little research has evaluated its use in quantifying carbon stocks in afforested bottomland hardwood forests. This study relates forest structural field measurements to metrics derived from low pulse density LiDAR data to assess the use of LiDAR in characterization of planted bottomland hardwood oak stands. Univariate and multivariate linear regressions were performed with field and LiDAR variables to determine relationships. The height-related field dependent variables average height, maximum height, and individual tree volume had the highest adjusted R-squared values of 0.5-0.6 (P<0.0001) for the univariate models and adjusted R-squared values of 0.70-0.79 for the multivariate models. These findings suggest that low-density LiDAR is capable of assessing forest structure and suggests that further research evaluating LiDAR quantification of bottomland hardwood carbon stocks is warranted.

URI

https://hdl.handle.net/11668/21119

Comments

carbon stocks||low-pulse density||forest structure||broad-leaf||floodplain forest||oaks||Quercus

Share

COinS