This workshop focuses on various forms of lidar technology and data-driven decisions related to transportation project delivery and maintenance. Presentation topics will cover cultural resources, environmental resources, maintenance, as well as construction and materials. It is intended to inform a broad base of attendees, covering various scales of study and cross-applications, and demonstrate how our peers are harnessing this technology for practical applications.
Our ongoing research investigates the impacts of human disturbance on the habitat use of bats in central hardwood forests. We are comparing responses of bat activity (assessed through acoustic surveys) across a gradient of disturbance (prescribed fire) at Mammoth Cave National Park in Kentucky. Principle findings from this research include: 1) forest canopy structure, as described using airborne laser scanning data, shows promise for predicting bat activity, especially for high-frequency echolocators; and 2) high-frequency bats showed a varied response to changes in flight space and canopy structure with prescribed fire as compared to low-frequency echolocators. A better understanding of the impacts of human disturbance on these predators of insects is merited, as bats are imperiled due to a number of factors across North America.
Wetland distribution has significant impacts on ecological functions. Reproducible mapping of different wetland types plays important roles in transportation planning, ecological studies, and conservation planning. This mapping process for large-scale areas requires a consistent dataset and robust classification methods. In order to facilitate this process, two machine learning algorithms are applied for wetland type classification in an automatic manner. Taking advantage of high-resolution LiDAR data, we emphasize topographical information as well as vegetation strata for wetland model construction. Ultimately, we calibrate the models for a specific study area located in North Carolina. We further compare these algorithms in terms of classification accuracy to provide insights for method selection. The results show that both methods perform well and provide similar results, and the overall accuracies are both above 95%.