Evaluating state-of-the-art remotely sensed data and methods for mapping wetlands in Minnesota

Appropriate management of our natural resources requires constant improvement and update of natural resource inventories. Remote sensing data and techniques offer an effective way to map and estimate changes in our current natural resources. The research presented in this dissertation will demonstrate state-of-the art remote sensing based methods for mapping natural and man-made features, including wetlands, general land cover, and building footprints. High resolution remotely sensed data used in this research included: lidar (light detection and ranging) data (low and high lidar posting density) and multispectral (NIR, blue, green and red bands) leaf-off aerial imagery. This research examined high resolution lidar data through the evaluation of various lidar posting densities and their influence on the accuracy of building footprints and DEMs. The lidar DEM analysis was extended by creating a Compound Topographic Index (CTI) from the DEM to evaluate the potential of the CTI's information for identifying wetland's location. Finally, the results from the second chapter were integrated into the third chapter by combining CTI, high resolution imagery, Digital Surface Model (DSM) and lidar intensity for mapping four land cover classes, including: wetlands, urban, agricultural and forest. A state-of-the-art remote sensing technique known as Object-Based Image Analysis (OBIA) was used to integrate lidar derived products and high resolution imagery. Results and findings of this research are important in two ways: First, advancing the understanding of lidar and lidar derivatives for mapping natural and manmade landscape features. Second, providing needed information to the scientific and civilian community, particularly in the state of Minnesota, to help with the process of updating wetland inventories such as the NWI and increasing the accuracy of mapping wetlands efforts with state-of-the-art techniques.