The purpose of this research is to quantify and assess geospatial land-use and land-cover (LULC) changes in the coastal counties of Mobile and Baldwin, Alabama using nine Landsat images from 1974-2008. A studyspecific classification scheme was devised comprising upland herbaceous, upland forest, non-woody and woody wetlands, open water, and urban categories. Upland forest was the most dominant terrestrial cover type. Wetlands averaged 17% and urban averaged 7%.
This paper discusses the development and implementation of a geospatial data processing method and multi-decadal Landsat time series for computing general coastal U.S. land-use and land-cover (LULC) classifications and change products consisting of seven classes (water, barren, upland herbaceous, nonwoody wetland, woody upland, woody wetland, and urban). Use of this approach extends the observational period of the NOAA-generated Coastal Change and Analysis Program (C-CAP) products by almost two decades, assuming the availability of one cloud free Landsat scene from any season for each targeted year. The Mobile Bay region in Alabama was used as a study area to develop, demonstrate, and validate the method that was applied to derive LULC products for nine dates at approximate five year intervals across a 34-year time span, using single dates of data for each classification in which forests were either leaf-on, leaf-off, or mixed senescent conditions. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and C-CAP valueadded products. Each classification's overall accuracy was assessed by comparing stratified random locations to available reference data, including higher spatial resolution satellite and aerial imagery, field survey data, and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall Kappa statistics ranging from 0.78 to 0.89. The accuracies are comparable to those from similar, generalized LULC products derived from C-CAP data. The Landsat MSS-based LULC product accuracies are similar to those from Landsat TM or ETM+ data. Accurate classifications were computed for all nine dates, yielding effective results regardless of season. This classification method yielded products that were used to compute LULC change products via additive GIS overlay techniques.
The Mobile Bay region has experienced noteworthy land use and land cover (LULC) change in the latter half of the 20th century. Accompanying this change has been urban expansion and a reduction of rural land uses. Much of this LULC change has reportedly occurred since the landfall of Hurricane Frederic in 1979. The Mobile Bay region provides great economic and ecologic benefits to the Nation, including important coastal habitat for a broad diversity of fisheries and wildlife. Regional urbanization threatens the estuary's water quality and aquatic-habitat dependent biota, including commercial fisheries and avian wildlife. Coastal conservation and urban land use planners require additional information on historical LULC change to support coastal habitat restoration and resiliency management efforts. This presentation discusses results of a Gulf of Mexico Application Pilot project that was
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.