Digital elevation models (DEMs) derived from light detection and ranging (LiDAR) technology are becoming the standard in representing terrain surfaces. They have numerous applications in forestry, agriculture, and natural resources. Although elevation errors are much lower than those derived from traditional methods, accuracies have been reported to decrease with terrain slope and vegetation cover. In this study, we quantified the accuracy of airborne Li-DAR-derived DEM in deciduous eastern forests of the Cumberland Plateau. We measured relative elevation changes within field plots located across different slope and ruggedness classes to quantify DEM accuracy. We compared elevation change errors of DEMs derived from three LiDAR datasets: a low-density (~1.5 pts•m ), and a combined dataset. We also compared DEMs obtained by interpolating the ground points using four interpolation methods. Results indicate that mean elevation change error (MECE) increased with terrain slope and ruggedness with an average of 73.6 cm. MECE values ranged from 23.2 cm in areas with lowest slope (0% -39%) and ruggedness (0% -28%) classes to 145.5 cm in areas with highest slope (50% -103%) and ruggedness (46% -103%) classes. We found no significant differences among interpolation methods or LiDAR datasets; the latter of which indicates that similar accuracy levels can be achieved with the low-density datasets.

Aim of the study: Use LiDAR-derived vegetation and terrain characteristics to develop abundance and occupancy predictions for two terrestrial salamander species, Plethodon glutinosus and P. kentucki, and map abundance to identify vegetation and terrain characteristics affecting their distribution.Area of study: The 1,550-ha Clemons Fork watershed, part of the University of Kentucky’s Robinson Forest in southeastern Kentucky, USA.Materials and methods: We quantified the abundance of salamanders using 45 field transects, which were visited three times, placed across varying soil moisture and canopy cover conditions. We created several LiDAR-derived vegetation and terrain layers and used these layers as covariates in zero-inflated Poisson models to predict salamander abundance.  Model output was used to map abundance for each species across the study area.Main results: From the184 salamanders observed, 63 and 99 were identifdied as P. glutinosus and P. kentucki, respectively. LiDAR-derived vegetation height variation and flow accumulation were best predictors of P. glutinosus abundance while canopy cover predicted better the abundance of P. kentucki. Plethodon glutinosus was predicted to be more abundant in sites under dense, closed-canopy cover near streams (2.9 individuals per m2) while P. kentucki was predicted to be found across the study sites except in areas with no vegetation (0.58 individuals per m2).Research highlight: Although models estimates are within the range of values reported by other studies, we envision their application to map abundance across the landscape to help understand vegetation and terrain characteristics influencing salamander distribution and aid future sampling and management efforts.Keywords:  Zero-inflated Poisson model; Kentucky; Cumberland plateau; Plethodon glutinosus; Plethodon kentucki.