Remote Sensing and Land Use Classification: Supervised vs. Unsupervised Classification

In this time of large-scale planning and land management on public lands, managers are increasingly looking for faster and less expensive methods of data collection. In efforts to make better decisions, planners need to be able to look at changes over time to assess trends. Policy makers are also looking to assess the effects of policies such as prescribed fire or fire suppression. All of these needs can be filled with data gathered from remote sensors. It is the purpose of this paper to assess the possibilities of using remote sensing for the detection of regional land-use change by developing a land cover classification system. Both supervised classification and unsupervised classification will be tested on a 2000 Landsat image of the spectrally diverse Salt Lake City area. This research will then compare the accuracy of two classification systems at diving the landscape into three classes, water, developed, and undeveloped.