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• W2097376076 abstract "ABSTRACT As population increases, the anthropogenic effects that accompany development also increase. Rapidly expanding urban centers compete with environmentally sensitive wetlands and other biosystems for space and resources. Rural timbering and changes in agricultural patterns may impact water quality in both surface-water and ground water systems. The extent and degree of these changes are very important to planners and resource managers. Digital image analysis of satellite data is a tool that can be used to detect, monitor, and analyze these changes in land use patterns. The Geological Survey of Alabama is using Landsat Thematic Mapper (TM) imagery to develop a wetlands land cover inventory map and attached data set. The study is supported by the U.S. Environmental Protection Agency (EPA), and administered through the Alabama Department of Environmental Management (ADEM). The maps and data set are intended to provide federal, state, and local officials and planners with a set of baseline land use/land cover information to aid in designing and implementing programs to mitigate the adverse impacts of development on the environment. In the first phase of this study, three Landsat TM scenes, covering central and southwest Alabama and including approximately half of the state's land area, are being classified. The study area includes the watersheds of the Tombigbee, Black Warrior, and Mobile River systems, including the majority of the Mobile River delta; part of the Alabama River system, including most of the Cahaba River watershed; and the wetland areas associated with these major river systems. In addition, the rapidly urbanizing areas around the cities of Birmingham, Tuscaloosa, and Anniston are included. Geometrically, radiometrically, and terrain-corrected mosaic-quality Landsat TM data was chosen for its ability to separate vegetative cover types. Winter leaf-off imagery was ordered immediately because an excellent set of temporally close imagery was available and is better at detecting wetlands and urban areas; however, good quality summer or leaf-on coverage was sporadic and was ordered later. ERDAS Imagine 8.3.1 remote-image processing software was used for classification. A preliminary classification system was devised for this study. This classification was loosely based on Anderson, et al. (1976). Field identification of selected training sites 10 acres or more is size was initiated for each classification. The two main methods of grouping satellite data into useful information classes are supervised and unsupervised classification. In an unsupervised classification the computer identifies naturally occurring spectral groups or clusters within the multispectral data (Lillesand and Kiefer, 1994). The analyst must then assign an information class to the spectral class the computer has isolated. When an initial unsupervised classification was made, it was obvious that several very different cover types, as identified from fieldwork, were generating the same spectral signatures and were therefore being assigned to the same classification. For example, strip mines, plowed farmland, construction sites, and overgrown pasture were being assigned by the unsupervised classification to the same class as heavily urbanized areas. Although several efforts were made using the available leaf-off imagery to break each cover type out and assign it to the proper class, it became obvious that this would require classification from leaf-on imagery. This problem illustrates the importance of using images from both winter and summer temporal periods. For the supervised classification, the image analyst assigned a certain area on the image to a particular land cover type. Samples of the different land cover types and locations within the scene were known prior to classification, and these sites were used to train the computer software to classify the entire scene. Training sites must be homogeneous and large enough to be detected on the imagery. The computer then analyzed each pixel in the scene to compare it to the statistics calculated for the trained area. If the pixel fell into this category it was classified as that land cover type. Aerial photographs, National Wetlands Inventory maps, topographic maps, and hardcopy false-color infrared prints from the images were used by field investigators to check training areas and to verify cover types. The Maximum Likelihood Classifier algorithm was chosen for this study. This algorithm uses training data to estimate the means and variances of the categories and then estimates the probability for membership in each category (Jensen 1995). Supervised classification is best used when there are relatively few classes to be identified. The unsupervised method has an advantage when many classes are desired, and when the spectral distinctions inherent in the data are to be used. In some cases, a combination of supervised and unsupervised classification may give the best results (ERDAS 1997). The Geological Survey of Alabama has developed digital image processing techniques that identify various land use/land cover patterns in west central Alabama, using supervised and unsupervised classification methods and combinations of the two techniques. The results of both classification methods and their combinations were visually inspected and a decision was made on which method provided the most reliable information for each land-cover class. The most reliable information classifications were then combined into a separate raster image for each method using a combination of vendor software recode and overlay commands. Visually, the results appear to be good, but statistical analyses of error for the classified scenes are not yet complete and confused classes must be better separated by using a multitemporal approach." @default.
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• W2097376076 title "Abstract: Land-use / Land-cover Classification of West Central Alabama using Landsat Thematic Mapper Data" @default.
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