This research focuses on estimation and analysis of stream sinuosity using GIS. Fifty-five streams including 13 streams of order 0, 17 streams of order 1, 15 streams of order 2 and 10 streams of order 3 in Virginia were considered. Several GIS datasets from various sources, including the Virginia Base Mapping Program (VBMP) and United States Geological Survey (USGS), were used to generate stream networks using GIS. Sinuosity was computed using GIS based on a technique comparable to the approach used in an Environmental Monitoring and Assessment Program's (EMAP's) field survey report. Field sinuosity data from EMAP report were used as reference data for analyzing the accuracy of sinuosity values from different GIS data sources and resolutions. The GIS technique was implemented for computing sinuosity for 55 streams in Virginia using vector data including the VBMP Hydro44 and National Hydrography Data (NHD). Insufficient statistical evidence was found to support the hypothesis that the computed sinuosity values using Hydro44 and NHD data are different from EMAP field data for all 55 streams. Sinuosity values computed using Hydro44 and NHD were found to increase with the increase in EMAP sinuosity (positive correlation) for all 55 streams. EMAP data on sinuosity, however, did not predict sinuosity values computed using Hydro44 (R² = 27%) and NHD (R² = 10%) sources well. It was found that the GIS technique of computing sinuosity using digital data such as Hydro44 (VBMP source) and NHD (USGS source 1:24,000) is better suited for stream orders 2 and 3. Insufficient statistical evidence was found that computed sinuosity values for streams derived using various resolutions (i.e., DTM 3m, DTM 10m, DTM 30m, DEM 10m and DEM 30m) are different from EMAP field data. Positive correlation was observed between sinuosity values for streams derived in all resolutions with EMAP field data. DTM 10m resolution data yielded best correlation value (75%) with EMAP field data. / Master of Science

Ground Moving Target Indication (GMTI) operation relies on clutter suppression techniques for the detection of slow moving ground targets in the presence of strong radar returns from the ground. Space Time Adaptive Processing (STAP) techniques provide a means to achieve this goal by adaptively forming the clutter suppression filter, whose parameters are obtained using an estimated covariance matrix of the clutter data. Therefore, the performance of the GMTI operation is directly aected by the performance of the estimation process mentioned above. Knowledge based techniques are applicable in applications such as the parametric estimation of the clutter covariance matrix and the estimation of the clutter covariance matrix in a nonhomogeneous clutter environment. In this study, a knowledge based approach which makes use of both a priori and instantaneous data is proposed for the mentioned estimation process. The proposed approach makes use of Shuttle Radar Topography Mission (SRTM) data as well as instantaneous platform ownship data in order to determine distributed homogeneous regions present in the region of interest / and afterwards employs Doppler Beam Sharpening (DBS) maps along with the colored loading technique for the blending process of the a priori data and the instantaneous data corresponding to the obtained homogeneous regions. A nonhomogeneity detector (NHD) is also implemented for the elimination of discrete clutter and target-like signals which may contaminate the STAP training data. Simulation results are presented for both the knowledge aided and the traditional cases. Finally, the performance of the STAP algorithm will be evaluated and compared for both cases. Results indicate that by using the developed processing approach, detection of previously undetectable targets become possible, and the overall number of false alarms is reduced.

The National Wetlands Inventory (NWI) is the definitive source for wetland resources in the United States. The NWI production unit in Hadley, MA has begun to upgrade their digital map database, integrating descriptors for assessment of wetland functions. Updating is conducted manually and some automation is needed to increase production and efficiency. This study assigned landscape position descriptor codes to NWI wetland polygons and correlated polygon environmental properties with public domain terrain, soils, hydrology, and vegetation data within the Coastal Plain of Virginia. Environmental properties were applied to a non-metric multidimensional scaling technique to identify similarities within individual landscape positions based on wetland plant indicators, primary and secondary hydrology indicators, and field indicators of hydric soils. Individual NWI landscape position classes were linked to field-validated environmental properties. Measures provided by this analysis indicated that wetland plant occurrence and wetland plant status obtained a stress value of 0.136 (Kruskal's stress measure = poor), which is a poor indicator when determining correlation among wetland environmental properties. This is due principally to the highly-variable plant distribution and wetland plant status found among the field-validated sites. Primary and secondary hydrology indicators obtained a stress rating of 0.097 (Kruskal's stress measure = good) for correlation. The hydrology indicators measured in this analysis had a high level of correlation with all NWI landscape position classes due the common occurrence of at least one primary hydrology indicator in all field validated wetlands. The secondary indicators had an increased accuracy in landscape position discrimination over the primary indicators because they were less ubiquitous. Hydric soil characteristics listed in the 1987 Manual and NTCHS field indicators of hydric soils proved to be a relatively poor indicator, based on Kruskal's stress measure of 0.117, for contrasting landscape position classes because the same values occurred across all classes. The six NWI field–validated landscape position classes used in this study were then further applied in a public domain digital data analysis. Mean pixel attribute values extracted from the 180 field-validated wetlands were analyzed using cluster analysis. The percent hydric soil component displayed the greatest variance when compared to elevation and slope curvature, streamflow and waterbody, Cowardin classification, and wetland vegetation type. Limitations of the soil survey data included: variable date of acquisition, small scale compared to wetland size, and variable quality. Flow had limitations related to its linear attributes, therefore is often found insignificant when evaluating pixel values that are mean of selected pixels across of wetland landscape position polygons. NLCD data limitations included poor quality resolution (large pixel size) and variable classification of cover types. The three sources of information that would improve wetland mapping and modeling the subtle changes in elevation and slope curvature that characterize wetland landscapes are: recent high resolution leaf-off aerial photography, high-quality soil survey data, and high-resolution elevation data. Due to the data limitations and the choice of variables used in this study, development of models and rules that clearly separate the six different landscape positions was not possible, and thus automation of coding could not be attempted. / Master of Science