Using airborne lidar to map habitat structure and connectivity across Alberta's managed forest for biodiversity conservation

Vegetation structure is an important biodiversity indicator providing biological and physical environment that supports and maintains forest biodiversity. The airborne lidar (Light Detection and Ranging) systems have the advantage of directly measuring three-dimensional vegetation structure, and have been widely used in wildlife habitat mapping and species distribution modeling at the local scales. As lidar data are increasingly compiled into broad spatial coverage, the development of structural inventory and indicators to categorize habitat types and identify important patches would be beneficial to regional-level conservation planning and biodiversity monitoring. However, this area of research has not been adequately explored. Large-area mapping of critical habitat patches is also a fundamental step towards modeling habitat connectivity. Quantification and dynamic modeling of habitat connectivity under long-term influence of land cover change events provide insights into forest management and conservation planning, and including climate change constraints into the modeling framework also helps maintain ecosystem integrity and improve conservation effectiveness. Therefore, the objectives of this thesis are to 1) characterize vegetation structure and identify important habitat patches with critical structural traits using regional lidar dataset, and 2) build habitat networks to model connectivity dynamics under land cover change events. To do this, first, a novel approach using cluster analysis to process large-area lidar data into categorical classes representing natural groupings of habitat structure was applied to derive eight unique structure classes in the managed forested area in Alberta, Canada. Second, the structure classes indicating high levels of structure complexity combined with Landsat-derived forest cover types were used to identify important habitat patches to develop habitat networks. Lastly, spatial prioritization schemes based on different aspects of connectivity and climate constraints were generated and implemented through scenario-based simulations of land cover change events. Connectivity dynamics through the simulations were assessed and compared between scenarios. The result showed that the conservation strategies considering both habitat area and habitat spatial configuration were best at maintaining habitat connectivity, and taking climate constraints into consideration didn’t affect overall connectivity. Overall, this research provides an integrated approach to characterize habitat structure using large-area lidar data for dynamic connectivity modeling following land cover change simulations.Forestry, Faculty ofGraduat