A Study on Determining the Sample Size in Geostatistics

Masters Thesis, Department of Mathematical and Statistical Sciences, University of Alberta, Fall 2010

Ying Ming Or

“After the scientific problem of interest is defined, collecting data is the first stage of any statistical analyses. The question of how large the sample should be is thus of great interest. In this thesis we demonstrate that in a geostatistical experiment determining the minimum sample size to achieve a certain precision of an estimator is often not possible due to inconsistencies of the estimators. This thesis is an empirical work extended from a manuscript (Gombay, 2010), which shows that the laws of large numbers may not hold under the spatial setting. It is demonstrated by a simulation study that the variance of the kriged mean converges to a non-zero constant as the sample size keeps increasing. It then followed by further investigations on the simple and ordinary kriging estimators. The conclusions arrived in this thesis lead for further research on the topic.”

Automatic Identification Methods of Linear and Circular Archaeological Structures via Satellite Imagery Processing

International Aerial Archaeology Conference – AARG 2010, Bucharest, Romania, 15 – 18 September 2010

Dorel Micle, Daniela Zaharie, and Oana Borlea

“Very wide spaces, rough terrain or the lack of visual perspective are the most invoked motives because of which wide areas of a country’s territory are not archaeologically investigated, thus creating a multitude of archaeological white spots.

“The usage of satellite images to identify archaeological sites represents a common practice of this scientific community nowadays, and, more and more often, the problem of automatic processing appears, of finding new methods and techniques of automatic identification of archaeological structures through satellite imagery processing.

“Finding the best solutions means to eliminate modern structures and study only the historic ones during the process. Benefitting of an almost total coverage with very good quality satellite images offered by Google Earth of the Timis County (Romania), and also of the richness and variety of noticeable archaeological sites on these images, our team tried to identify some work patterns which are accessible to archaeologists.

“Remote sensing techniques proved to be useful in non-intrusive investigation of archaeological sites by providing information on buried archaeological remains. The presence of different remains in the ground can generate different marks identifiable in high resolution panchromatic and/or multispectral images: crop marks, soil marks, shadow marks and damp marks.

“Automatic identification of archaeological sites from digital images is a difficult task, since the small anomalies induced by the buried remains are usually hidden by stronger marks corresponding to the structures currently existing on the ground (roads, constructions, trees, rocks etc). Therefore the final identification and interpretation of the marks should be made by the expert by visually inspecting the enhanced image and by corroborating his observations with additional information (e.g. historical maps, current roads network etc).

“In order to prepare the image for visual inspection we first applied a flow of basic image processing operations: gray scale conversion, histogram equalization, edge detection (Sobel filter), thresholding, inversion and erosion. Having the aim of developing a semi-automatic tool for identification of linear and circular shapes we also investigated some more sophisticated operations. One of these operations is the Hough transform which we applied in order to identify linear structures (e.g. wave like roman fortifications) and circular structures (e.g. burial mounds).

“The main problem we encountered in identifying the ancient marks is the fact that they are somewhat obscured by the marks of current land division, roads, contemporary buildings etc. In order to deal with this problem we applied both a supplementary pre-processing and a post-processing step. As pre-processing operation we used the singular value decomposition of the image. By ignoring the components corresponding to the highest singular value(s) (which contain the most important features in the image) we obtained an image where the ancient marks are more visible. In the post-processing step we tried to eliminate the lines detected by the Hough transform which correspond to the current land division by using the remark that this lines are mainly parallel while the ancient mark (e.g. a linear fortification) has a different orientation. Using such operations we successfully identified the location of a linear „roman“ fortification.

“The perspectives appear to be promising, so we also want to identify work methods for automatic identification of irregular structures and colors.”

Middleware-Based Sensor Web Integration

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Volume PP, Issue 99, 2010-06-21

Tian, Y.; Geiger, J. V.; Su, H.; Kumar, S. V.; Houser, P. R.

“The Earth observation sensor web enables multiple-way interaction between earth observing sensors, sensor networks, Earth science models, and decision support systems. To achieve this goal, flexible and reliable integration between these disparate components is needed. In this study, a middleware-based, message-driven integration paradigm is proposed and implemented with the Land Information Sensor Web (LISW), to link a high-performance land surface modeling system with sensor simulators and other sensor web components, under a service-oriented architecture. OGC Sensor Web Enablement standard is adopted for interoperability. The middleware played a key role in enabling an integrated real-time sensor web with demonstrated simplicity, resilience and flexibility. We recommend that middleware-based integration should be adopted as a standard model in a wide range of sensor web applications, to replace the conventional point-to-point, client-server model.”