A Karst Feature Prediction Model For Prince Of Wales Island, Alaska Based On High Resolution Lidar Imagery

• Main Author: Lyles, Alexander
• Format: Text
• Language: unknown
• Published: FHSU Scholars Repository 2021
• Subjects: Geomorphology; Lanscape Evolution; Geologic Hazards; Remote Sensing; GIS; Environmental Education; Environmental Indicators and Impact Assessment; Geology; Hydrology; Natural Resources and Conservation; Natural Resources Management and Policy; Other Earth Sciences; Physical and Environmental Geography; Spatial Science; Speleology; Tectonics and Structure; Water Resource Management; Prince of Wales Island; Alaska
• Online Access: https://scholars.fhsu.edu/theses/3172; https://scholars.fhsu.edu/cgi/viewcontent.cgi?article=4203&context=theses

Investigation into surface karst formation is significant to hazard prediction, hydrogeologic drainage, and land management. Southeast Alaska contains over 600,000 acres of mapped carbonate bedrock, and some of the fastest recorded karst dissolution in the world. The objectives of this study are to develop and compare multiple semi-automated models to map and delineate karst features from bare-earth LiDAR imagery using ArcGIS Desktop 10.7, and to apply a preliminary geostatistical analysis of sinkhole morphometric parameters to highlight potential spatial patterns of karst evolution on Prince of Wales Island, Alaska. A semi-automated approach of mapping karst features provides a dataset that minimizes error from noise while maintaining accurate depression location and catchment boundaries. Several semi-automated models with different size parameters were compared against field-validated data using vulnerability as a proxy to determine the most accurate size threshold model. The model with the most overlap agreement was used to determine the morphometrics of karst features identified. This study conducted preliminary analysis of morphometric properties derived from the semi-automated karst feature prediction model to provide context for the geologic controls that allow for such large, rapid karstification observed in the region. Although beyond the scope of this study, morphometric analysis utilizing this semi-automated approach should be the focus of future studies to determine formation mechanisms and factors of karst landscape evolution through time.