Land cover change impacts ecosystem function across the globe. The use of land cover data is vital in the detection of these changes over time; however, most available land cover products, such as the National Land Cover Dataset (NLCD), are produced relatively infrequently. The most recent NLCD at the time of this research was produced in 2006 and does not adequately reflect the impact of land cover changes that have occurred since, including the occurrence of two large wildfires in 2008 in our study area. Therefore, there is a need for the classification of historical remotely sensed data, such as Landsat scenes, through replicable methods. While it is possible to collect field data coinciding with current or future Landsat acquisitions, it is impossible to retrospectively collect data for previous years; thus, fewer studies have focused on the classification of historical scenes. Using a single year of field reference and multi-year aerial photography data, we applied a simple decision tree classifier to accurately classify historic satellite data and produced maps of land cover to incorporate the effects of 2008 wildfires occurring between NLCD production dates. Overall accuracy ranged from 76 to 90 percent and was assessed using conventional error matrices.
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