Thesis (M.A.)--Boston University / This study is a part of an environmental impact assessment of the Gulf War on the desert and the coastal zones of Kuwait. Due to the appearance of many new surface features, a study was necessary to characterize their spectral signatures as detected by Landsat Thematic Mapper (TM) data. A sophisticated image analysis was applied to the Landsat TM scene. An unsupervised classification technique produced a thematic map of the area. Data was collected on the ground at eighty sites in southeastern Kuwait. A radiometer (SE-590) was used to identify the spectral reflectance of desert surface features. A Global Positioning System (GPS) reading on each site was also recorded to register accurately the field observations on a specific pixel from over 72 million pixels in the lower right scene of Kuwait. Field data were collected on surface feature color, soil grain stze, vegetation types and density, and the amount of oil or soot contamination. Statistical correlation's and companson of Landsat and the SE-590 measurements in the visible and near-infrared bands describe the interaction between radiation and different desert surfaces. The oil lakes class was identified to have the lowest reflectance of all the classes. Brightness values gradually increase as less oil, soot or desert vegetation is found. The highest brightness value belongs to the class which represents active sand.

Thesis (Ph.D.)--Boston University / The 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.