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dc.contributor.authorAl-Doasari, Ahmad E.en_US
dc.date.accessioned2019-05-28T12:44:08Z
dc.date.available2019-05-28T12:44:08Z
dc.date.issued2001
dc.date.submitted2001
dc.identifier.otherb23928244
dc.identifier.urihttps://hdl.handle.net/2144/35677
dc.descriptionThesis (Ph.D.)--Boston Universityen_US
dc.description.abstractThe 1991 Gulf War caused massive environmental damage in Kuwait. Deposition of oil and soot droplets from hundreds of burning oil-wells created a layer of tarcrete on the desert surface covering over 900 km'. This research investigates the spatial change in the tarcrete extent from 1991 to 1998 using Landsat Thematic Mapper (TM) imagery and statistical modeling techniques. The pixel structure ofTM data allows the spatial analysis of the change in tarcrete extent to be conducted at the pixel (cell) level within a geographical information system (GIS). There are two components to this research. The first is a comparison of three remote sensing classification techniques used to map the tarcrete layer. The second is a spatial-temporal analysis and simulation of tarcrete changes through time. The analysis focuses on an area of 389 km' located south of the Al-Burgan oil field. Five TM images acquired in 1991, 1993, 1994, 1995, and 1998 were geometrically and atmospherically corrected. These images were classified into six classes: oil lakes; heavy, intermediate, light, and traces of tarcrete; and sand. The classification methods tested were unsupervised, supervised, and neural network supervised (fuzzy ARTMAP). Field data of tarcrete characteristics were collected to support the classification process and to evaluate the classification accuracies. Overall, the neural network method is more accurate (60 percent) than the other two methods; both the unsupervised and the supervised classification accuracy assessments resulted in 46 percent accuracy. The five classifications were used in a lagged autologistic model to analyze the spatial changes of the tarcrete through time. The autologistic model correctly identified overall tarcrete contraction between 1991-1993 and 1995-1998. However, tarcrete contraction between 1993-1994 and 1994-1995 was less well marked, in part because of classification errors in the maps from these time periods. Initial simulations of tarcrete contraction with a cellular automaton model were not very successful. However, more accurate classifications could improve the simulations. This study illustrates how an empirical investigation using satellite images, field data, GIS, and spatial statistics can simulate dynamic land-cover change through the use of a discrete statistical and cellular automaton model.en_US
dc.language.isoen_US
dc.publisherBoston Universityen_US
dc.rightsThis work is being made available in OpenBU by permission of its author, and is available for research purposes only. All rights are reserved to the author.en_US
dc.subjectGulf Waren_US
dc.subjectKuwaiten_US
dc.subjectMiddle Easten_US
dc.subjectDesert surfacesen_US
dc.subjectRemote sensingen_US
dc.subjectGeographyen_US
dc.subjectTarcreteen_US
dc.titleAnalysis of the changes in the tarcrete layer on the desert surface of Kuwait using satellite imagery and cell-based modelingen_US
dc.typeThesis/Dissertationen_US
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineGeographyen_US
etd.degree.grantorBoston Universityen_US
dc.identifier.barcode11719022832036
dc.identifier.mmsid99188889060001161


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