Integrating decision analytical techniques in geographic information systems (GIS) can help remove the two primary obstacles in spatial decision making: inaccessibility to required geographic data and difficulties in synthesizing various criteria. I developed a GIS model to assist people seeking optimal residential locations. Fuzzy set theory was used to codify criteria for each factor used in evaluating residential locations, and weighted linear combination (WLC) was employed to simulate users' preferences in decision making. Three examples were used to demonstrate the applications in the study area. The results from the examples were analyzed. The model and the ArcGIS Extension can be used in other geographic areas for residential location selection, or in other applications of spatial decision making.

In this research, hyperspectral and multispectral images were utilized for vegetation studies in the greenbelt corridor near Denton. EO-1 Hyperion was the hyperspectral image and Landsat Thematic Mapper (TM) was the multispectral image used for this research. In the first part of the research, both the images were classified for land cover mapping (after necessary atmospheric correction and geometric registration) using supervised classification method with maximum likelihood algorithm and accuracy of the classification was also assessed for comparison. Hyperspectral image was preprocessed for classification through principal component analysis (PCA), segmented principal component analysis and minimum noise fraction (MNF) transform. Three different images were achieved after these pre-processing of the hyperspectral image. Therefore, a total of four images were classified and assessed the accuracy. In the second part, a more precise and improved land cover study was done on hyperspectral image using linear spectral unmixing method. Finally, several vegetation constituents like chlorophyll a, chlorophyll b, caroteoids were distinguished from the hyperspectral image using feature-oriented principal component analysis (FOPCA) method and which component dominates which type of land cover particularly vegetation were correlated.