Characterizing habitats associated with fisher den structures in the Southern Sierra Nevada, California using discrete return lidar

Lidar data provide information on the vertical and horizontal structure of forests, and lidar remote sensing can be effective in characterizing the forest habitats of mammals. This study explored the ability of lidar-derived metrics to capture topography and forest structure surrounding denning trees used by the Pacific fisher (Martes pennanti) as a case study to illustrate the utility of lidar remote sensing in studying mammal-habitat associations. Lidar data were collected in September 2007 across conifer dominated forests in the Sierra Nevada Mountains that include a full range of forest density. We used Classification and Regression Trees (CART) to statistically compare the slope and lidar-derived forest height and structure metrics in the circular area (with radius of 10–50 m) surrounding denning trees and randomly selected non-denning trees. We accessed our model accuracy using resubstitution and cross-validation methods. Our results show that there is a strong association between fisher denning activity and its surrounding forested environment across scales, with high classification accuracy (overall accuracies above 80% and cross-validation accuracies above 70%) at 20, 30 and 50 m ranges. The best classification accuracies were found at 20 m (optimal resubstitution accuracy 86.2% and cross-validation accuracy 78%). Tree height and slope were important variables in classifying the area immediately surrounding denning trees; at scales larger than 20 m, forest structure and complexity became more important. The variables identified were also consistent with those identified from previous studies describing fisher resting tree structure. Based on these findings, we conclude that lidar remote sensing has utility in forest characterization for fishers and has great potential for mammal studies. 2012 Elsevier B.V. All rights reserved.