Land cover study in Iowa: analysis of classification methodology and its impact on scale, accuracy, and landscape metrics

For landscapes dominated by agriculture, land cover plays an important role in the balance between anthropogenic and natural forces. Therefore, the objective of this thesis is to describe two different methodologies that have been implemented to create high-resolution land cover classifications in a dominant agricultural landscape. First, an object-based segmentation approach will be presented, which was applied to historic, high resolution, panchromatic aerial photography. Second, a traditional per-pixel technique was applied to multi-temporal, multispectral, high resolution aerial photography, in combination with light detection and ranging (LIDAR) and independent component analysis (ICA). A critical analysis of each approach will be discussed in detail, as well as the ability of each methodology to generate landscape metrics that can accurately characterize the quality of the landscape. This will be done through the comparison of various landscape metrics derived from the different classifications approaches, with a goal of enhancing the literature concerning how these metrics vary across methodologies and across scales. This is a familiar problem encountered when analyzing land cover datasets over time, which are often at different scales or generated using different methodologies. The diversity of remotely sensed imagery, including varying spatial resolutions, landscapes, and extents, as well as the wide range of spatial metrics that can be created, has generated concern about the integrity of these metrics when used to make inferences about landscape quality. Finally, inferences will be made about land cover and land cover change dynamics for the state of Iowa based on insight gained throughout the process.