Year of Publication


Degree Name

Master of Science (MS)

Document Type

Master's Thesis


Agriculture, Food and Environment



First Advisor

Dr. Marco A. Contreras


The use of Light Detection and Ranging (LiDAR) information is gaining popularity, however its use has been limited in deciduous forests. This thesis describes two studies using LiDAR data in an Eastern Kentucky deciduous forest. The first study quantifies vertical error of LiDAR derived digital elevation models (DEMs) which describe the forests terrain. The study uses a new method which eliminates Global Positioning System (GPS) error. The study found that slope and slope variability both significantly affect DEM error and should be taken in to account when using LiDAR derived DEMs. The second study uses LiDAR derived forest vegetation and terrain metrics to predict terrestrial Plethodontid salamander abundance across the forest. This study used night time visual encounter surveys coupled with zero-inflation modeling to predict salamander abundance based on environmental covariates. We focused on two salamander species, Plethodon glutinosus and Plethodon kentucki. Our methods produced two different best fit models for the two species. Plethodon glutinosus included vegetation height standard deviation and water flow accumulation covariates, while Plethodon kentucki included only canopy cover as a covariate. These methods are applicable to many different species and can be very useful for focusing management efforts and understanding species distributions across the landscape.