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Abstract
This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products.
Acknowledgements
The authors thank the two anonymous reviewers who provided comments to improve this manuscript. This work was supported by funding from a NASA Gulf of Mexico Initiative ROSES grant (# NNX10AC57G, PI: Ellis). Participation in this work by Computer Sciences Corporation, Inc., was supported by NASA at the John C. Stennis Space Center, Mississippi, under Task Order NNS10AA35C. NASA’s Applied Science and Technology Program Office (ASTPO) at Stennis Space Center (SSC) supported initial participation by Spruce, Smoot, and Hilbert prior to the ROSES project under Task Order NNS04AB54T. NASA ASTPO supported a portion of Ellis’ initial participation on this project while she was working at SSC under the Intergovernmental Personnel Act.