| Summary: | 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.
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