The Gulf of California and its surrounding land areas provide a classic example of recently rifted continental lithosphere. The recent tectonic history of eastern Baja California has been dominated by oblique rifting that began at ~12 Ma. Thus, extensional tectonics, bedrock lithology, long-term climatic changes, and evolving surface processes have controlled the tectono-geomorphological evolution of the eastern part of the peninsula since that time. In this study, digital elevation data from the Shuttle Radar Topography Mission (SRTM) from Baja California were corrected and enhanced by replacing artifacts with real values that were derived using a series of geostatistical techniques. The next step was to generate accurate thematic geologic maps with high resolution (15-m) for the entire eastern coast of Baja California. The main approach that we used to clearly represent all the lithological units in the investigated area was objectoriented classification based on fuzzy logic theory. The area of study was divided into twenty-two blocks; each was classified independently on the basis of its own defined membership function. Overall accuracies were 89.6 %, indicating that this approach was highly recommended over the most conventional classification techniques. The third step of this study was to assess the factors that affected the geomorphologic development along the eastern side of Baja California, where thirty-four drainage basins were extracted from a 15-m-resolution absolute digital elevation model (DEM). Thirty morphometric parameters were extracted; these parameters were then reduced using principal component analysis (PCA). Cluster analysis classification defined four major groups of basins. We extracted stream length-gradient indices, which highlight the differential rock uplift that has occurred along fault escarpments bounding the basins. Also, steepness and concavity indices were extracted for bedrock channels within the thirty-four drainage basins. The results were highly correlated with stream length-gradient indices for each basin. Nine basins, exhibiting steepness index values greater than 0.07, indicated a strong tectonic signature and possible higher uplift rates in these basins. Further, our results indicated that drainage basins in the eastern rift province of Baja California could be classified according to the dominant geomorphologic controlling factors (i.e., faultcontrolled, lithology-controlled, or hybrid basins).

Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with dataset from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We also investigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors.

This dissertation consists of three studies dealing with particulate organic carbon (POC). The first study describes the temporal and spatial variability of particulate matter (PM) and POC, and physical processes that affect the distribution of PM and POC with synchronous remote sensing data. The purpose of the second study is to develop POC algorithms in the Gulf of Mexico based on satellite data using numerical methods and to compare POC estimates with spectral radiance. The purpose of the third study is to investigate climatological variations from the temporal and spatial POC estimates based on SeaWiFS spectral radiance and physical processes, and to determine the physical mechanisms that affect the distribution of POC in the Gulf of Mexico. For the first and second studies, hydrographic data from the Northeastern Gulf of Mexico (NEGOM) study were collected on each of 9 cruises from November 1997 to August 2000 across 11 lines. Remotely sensed data sets were obtained from NASA and NOAA using algorithms that have been developed for interpretation of ocean color data from various satellite sensors. For the third study, we use the time-series of POC estimates, sea surface temperature (SST), sea surface height anomaly (SSHA), sea surface wind (SSW), and precipitation rate (PR) that might cause climatological variability and physical processes. The distribution of surface PM and POC concentrations were affected by one or more factors such as river discharge, wind stress, stratification, and the Loop Current/Eddies. To estimate POC concentration, empirical and model-based approaches were used using regression and principal component analysis (PCA) methods. We tested simulated data for reasonable and suitable algorithms in Case 1 and Case 2 waters. Monthly mean values of POC concentrations calculated with PCA algorithms. The spatial and temporal variations of POC and physical forcing data were analyzed with the empirical orthogonal function (EOF) method. The results showed variations in the Gulf of Mexico on both annual and inter-annual time scales.

Thesis (M.S.)--Kent State University, 2008. / Title from PDF t.p. (viewed Sept. 28, 2009). Advisor: Donna Witter. Keywords: chlorophyll; Lake Erie; remote sensing; algorithm; atmospheric correction. Includes bibliographical references (p. 58-66).

Thesis (M.S. in Operations Analysis)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Arnold H. Buss. Includes bibliographical references (p. 123-124). Also available online.

Thesis (M.S. in Applied Science)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Richard Harkins, D. Scott Davis. Includes bibliographical references (p. 61). Also available online.

Thesis (M.S. in Applied Science (Robotics))--Naval Postgraduate School, September 2003. / Thesis advisor(s): Don Brutzman, Anthony Healey, Douglas P. Horner. Includes bibliographical references (p. 177-181). Also available online.

Thesis (M.S.)--Middle East Technical University, 2003. / Keywords: Remote sensing, alteration mapping, spectral library, Cappadocian Volcanic Province.

Thesis (Ph.D.)--York University, 2008. Graduate Programme in Earth and Space Science. / Typescript. Includes bibliographical references (leaves 218-238). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:NR39016

Conventional land use change detections with remote sensing use annual remote sensing images because of the limitations of optical sensors that cannot collect data in bad weather and cloudy conditions. This limits its applications in rapidly developing areas which are cloudy, such as the Pearl River Delta in China. These areas also need to detect land use changes in short intervals, such as on a monthly basis, in order to monitor illegal land use changes and prevent irreversible land use changes that may damage the environment. The objective of the thesis is to examine short-interval land use change detection, especially the change from agriculture to built-up areas, using RADARSAT-1 images which can go through clouds. This thesis firstly examines the classification of RADARSAT-1 images with pixel-based and object-based classification methods respectively. Based on the classification results, post-classification change detection method are conducted in order to obtain the detailed information of land use changes for the analysis of short-interval land use change. Land use change detection accuracies can be improved as the number of the RADARSAT-1 images used in land use change detection increased. More images, which represent longer monitoring period, can obtain better results of land use change detection. For short-interval land use changes detection, four time periods is the maximum otherwise the period of monitoring will be too long. Agricultural activities such as planting and harvesting have significant effects on the monitoring of land use changes. In planting and harvesting months, the accuracies of the land use change detection are lower than other months because its land cover is often confused with other land uses, such as water and bare soils. The process of construction can be considered as a three-stage process and a combination of two land uses. However, construction sites are often confused with vegetation and bare soil in RADARSAT-1 images because the values of backscatter coefficients of construction sites and the two land uses are very similar. The land cover changes during the planting and harvest seasons are often confused with the process of construction. It is found that construction sites can be identified with their two stages of low values of backscatter coefficients, which is not found in the pattern curves of backscatter coefficients of other land uses. By the comparison of the accuracies of identifying construction sites using two, three and four RADARSAT-1 images, it is found that using three time periods can get better accuracies which is different from the result of general land use change detection. This thesis does not try to evaluate land use change detection methods or find the best method for monitoring land use changes. Instead, it focused on the analysis of confusions caused by the time periods of land use change detection and seasonal variation of vegetations. The main contributions of this study are as follows: 1) it explores the use of multi-temporal RADARSAT-1 images into the land use change detection to overcome the problems of cloudy conditions, making short-interval land use change detection possible for areas which are often covered by clouds; 2) pixel-based maximum likelihood method and the object-based classification method were compared for their accuracies in land use classification of RADARSAT-1 images; 3) it examines the optimal time periods for land use change detection; and 4) it examines the appropriate number of images that are needed for monitoring land use changes in different seasons in order to obtain the best accuracies. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy