Twenty years of progress: GIScience in 2010

It is 20 years since the term “geographic information science” was suggested to encompass the set of fundamental research issues that surround GIS. Two decades of GIScience have produced a range of accomplishments, in an expanding literature of research results as well as in the infrastructure of research. Several themes are suggested for future research, based both on gaps in what has been accomplished thus far, and on technology trends that will themselves raise research questions

Updating Cadastral Maps using GIS Techniques

The Cadastral maps are very important since they have technical and materialist specification of the property borders. However, these maps despite their use as land registration in world in general but in Iraq; the old maps are unfit for use. Therefore, updating and digitizing the cadastral maps are very pivotal. In the present work, we have an old agricultural cadastral map since thirties as a hardcopy which was digitized then updated using control points and modern satellite image (QuickBird 2009) for the same area. In this research, we upgraded the methodology for updating of the agricultural cadastral maps of Iraq based on the use of Differential Global Position system (DGPS), Total Station, and Satellite Imagery, in addition to the cadastral editor extension in ArcGIS software to produce new agricultural maps. The tolerance of this approach was tested by root mean square errors in addition the parcel points were compared with land records and QuickBird image. The motivation of current work was due to there are no modern cadastral maps for the study area, which is located in province of Wassit South-East of Baghdad. The results can be used as a basis for the decision makers in addition; this methodology can be utilized to solve problems relating to land property in study area and can be extrapolated to other datasets

The Influence of Measurement Scale and Uncertainty on Interpretations of River Migration

Environmental scientists increasingly use remotely-sensed images to measure how rivers develop over time and respond to upstream changes in environmental drivers such as land use, urbanization, deforestation and agricultural practices. These measurements are subject to uncertainty that can bias conclusions. The first step towards accurate interpretation of river channel change is properly quantifying and accounting for uncertainty involved in measuring changes in river morphology. In Chapter 2 we develop a comprehensive framework for quantifying uncertainty in measurements of river change derived from aerial images. The framework builds upon previous uncertainty research by describing best practices and context-specific strategies, comparing each approach and outlining how to best handle measurements that fall below the minimum level of detection. We use this framework in subsequent chapters to reduce the impact of erroneous measurements. Chapter 3 evaluates how the time interval between aerial images influences the rates at which river channels appear to laterally migrate across their floodplains. Multiple lines of evidence indicate that river migration measurements obtained over longer time intervals (20+ years) will underestimate the ‘true’ rate because the river channel is more likely to have reversed the direction of migration, which erases part of the record of gross erosion as seen from aerial images. If the images don’t capture channel reversals and periodic episodes of fast erosion, the river appears to have migrated a shorter distance (which corresponds to a slower rate) than reality. Obtaining multiple measurements over shorter time intervals (\u3c 5 years) and limiting direct comparisons to similar time intervals can reduce bias when inferring how river migration rates may have changed over time. Chapter 4 explores the physical processes governing the relationship between river curvature and the rate of river migration along a series of meander bends. We used fine-scale empirical measurements and geospatial analyses to confirm theory and models indicating that migration and curvature exhibit a monotonic relationship. The results will improve models seeking to emulate river meander migration patterns

Modeling Boundaries of Influence among Positional Uncertainty Fields

Within a CIS environment, the proper use of information requires the identification of the uncertainty associated with it. As such, there has been a substantial amount of research dedicated to describing and quantifying spatial data uncertainty. Recent advances in sensor technology and image analysis techniques are making image-derived geospatial data increasingly popular. Along with development in sensor and image analysis technologies have come departures from conventional point-by-point measurements. Current advancements support the transition from traditional point measures to novel techniques that allow the extraction of complex objects as single entities (e.g., road outlines, buildings). As the methods of data extraction advance, so too must the methods of estimating the uncertainty associated with the data. Not only will object uncertainties be modeled, but the connections between these uncertainties will also be estimated. The current methods for determining spatial accuracy for lines and areas typically involve defining a zone of uncertainty around the measured line, within which the actual line exists with some probability. Yet within the research community, the proper shape of this \u27uncertainty band\u27 is a topic with much dissent. Less contemplated is the manner in which such areas of uncertainty interact and influence one another. The development of positional error models, from the epsilon band and error band to the rigorous G-band, has focused on statistical models for estimating independent line features. Yet these models are not suited to model the interactions between uncertainty fields of adjacent features. At some point, these distributed areas of uncertainty around the features will intersect and overlap one another. In such instances, a feature\u27s uncertainty zone is defined not only by its measurement, but also by the uncertainty associated with neighboring features. It is therefore useful to understand and model the interactions between adjacent uncertainty fields. This thesis presents an analysis of estimation and modeling techniques of spatial uncertainty, focusing on the interactions among fields of positional uncertainty for image-derived linear features. Such interactions are assumed to occur between linear features derived from varying methods and sources, allowing the application of an independent error model. A synthetic uncertainty map is derived for a set of linear and aerial features, containing distributed fields of uncertainty for individual features. These uncertainty fields are shown to be advantageous for communication and user understanding, as well as being conducive to a variety of image processing techniques. Such image techniques can combine overlapping uncertainty fields to model the interaction between them. Deformable contour models are used to extract sets of continuous uncertainty boundaries for linear features, and are subsequently applied to extract a boundary of influence shared by two uncertainty fields. These methods are then applied to a complex scene of uncertainties, modeling the interactions of multiple objects within the scene. The resulting boundary uncertainty representations are unique from the previous independent error models which do not take neighboring influences into account. By modeling the boundary of interaction among the uncertainties of neighboring features, a more integrated approach to error modeling and analysis can be developed for complex spatial scenes and datasets

Strategies for Handling Spatial Uncertainty due to Discretization

Geographic information systems (GISs) allow users to analyze geographic phenomena within areas of interest that lead to an understanding of their relationships and thus provide a helpful tool in decision-making. Neglecting the inherent uncertainties in spatial representations may result in undesired misinterpretations. There are several sources of uncertainty contributing to the quality of spatial data within a GIS: imperfections (e.g., inaccuracy and imprecision) and effects of discretization. An example for discretization in the thematic domain is the chosen number of classes to represent a spatial phenomenon (e.g., air temperature). In order to improve the utility of a GIS an inclusion of a formal data quality model is essential. A data quality model stores, specifies, and handles the necessary data required to provide uncertainty information for GIS applications. This dissertation develops a data quality model that associates sources of uncertainty with units of information (e.g., measurement and coverage) in a GIS. The data quality model provides a basis to construct metrics dealing with different sources of uncertainty and to support tools for propagation and cross-propagation. Two specific metrics are developed that focus on two sources of uncertainty: inaccuracy and discretization. The first metric identifies a minimal?resolvable object size within a sampled field of a continuous variable. This metric, called detectability, is calculated as a spatially varying variable. The second metric, called reliability, investigates the effects of discretization on reliability. This metric estimates the variation of an underlying random variable and determines the reliability of a representation. It is also calculated as a spatially varying variable. Subsequently, this metric is used to assess the relationship between the influence of the number of sample points versus the influence of the degree of variation on the reliability of a representation. The results of this investigation show that the variation influences the reliability of a representation more than the number of sample points

ESTIMATION OF MEASUREMENT UNCERTAINTY OF SEAFLOOR ACOUSTIC BACKSCATTER

In the last three decades, Multibeam echo sounders (MBES) have become the tool of choice to study the seafloor. MBES collects two distinct types of data: bathymetry that provides topographic details of the seafloor and backscatter that has the potential to characterize the seafloor. While the uncertainty associated with MBE bathymetry has been well studied, the uncertainty in MBES backscatter measurement has received relatively little attention, hindering the improvements in quantitative analysis of backscatter data. Both acquisition and processing stages can introduce uncertainty in the final seafloor backscatter products. Application of well-established uncertainty quantification principles to seafloor backscatter data is challenging for several reasons: the uncertainty sources are not well known, they vary on a case-by-case basis, and standards do not exist for acquisition and processing. This dissertation focuses on assessing uncertainty in backscatter measurements and is comprised of four separate but related studies that identify and address the challenges of uncertainty quantification of backscatter measurements. The first study (Lucieer et al., 2018) which is presented as background, describes an end users’ survey identifying key uses and challenges of backscatter data acquisition and processing. The study identified that consistency and repeatability of backscatter measurements is a major constraint in the use and re-use of backscatter. The second study (Malik et al., 2018), identified the sources of uncertainty and categorized them as significant or insignificant based on various use cases. The most significant sources of uncertainty were found to be inherent statistical fluctuations in the backscatter measurement, calibration uncertainty, seafloor slope and water column absorption estimation. While calibration uncertainty remains the main issue in advancing the quantitative use of the backscatter, the other sources were also shown to cause large uncertainties. These include non-standardized methods used to account for seafloor slope and absorption, and data interpretation errors due to missing background information about the processing procedures. With a comprehensive list of uncertainty sources established, two uncertainty sources, seafloor slope and processing errors, were examined further in the third (Malik, 2019) and the fourth (Malik et al., submitted) study respectively. Seafloor slope corrections are important to correct for both the area insonified and the incidence angle. Both of these corrections are adversely affected if seafloor slope corrections are not applied. Even in cases where the seafloor slope is used, further uncertainty can occur if the highest resolution bathymetry is not used. The results from this study showed that for the purpose of accurate slope corrections, the spatial scale of backscatter data should be selected based on the best available bathymetry. The majority of end users depend on third-party software solutions to process the backscatter data. The fourth study evaluated the output of three commonly used software packages after inputting the same data set and found that there were significant differences in the outputs. This issue was addressed by working closely with software developers to explore options to make the processing chain more transparent. Two intermediate processing stages were proposed and implemented in three commonly used software tools. However, due to proprietary restrictions, it was not possible to know the full details of the software processing packages. Differing outputs likely result, in part, from the different approaches used by the various software packages to read the raw data. Quality assessment and uncertainty quantification of MBES backscatter measurements is still at an early stage and further work is required to develop data acquisition and processing standards to improve consistency in the backscatter acquisition and processing. Publications: Lucieer, V.; Roche, M.; Degrendele, K.; Malik, M.; Dolan, M.; Lamarche, G. User expectations for multibeam echo sounders backscatter strength data-looking back into the future. Mar. Geophys. Res. 2018, 39, 23–40. doi:10.1007/s11001-017-9316-5. Malik, M.; Lurton, X.; Mayer, L. A framework to quantify uncertainties of seafloor backscatter from swath mapping echosounders. Mar. Geophys. Res. 2018, 39, 151–168. doi.org/10.1007/s11001-018-9346-7. Malik, M. Sources and Impacts of Bottom Slope Uncertainty on Estimation of Seafloor Backscatter from Swath Sonars. Geosciences 2019, 9, 183. doi: 10.3390/geosciences9040183. Malik, M.; Schimel, A.; Masetti, G.; Roche, M.; Deunf, J.L.; Dolan, M.; Beaudoin, J.; Augustin, J.M.; Hamilton, T.; Parnum, I. Results from the first phase of the Backscatter Software Inter-comparison Project. Geosciences. Submitted

An Evolutionary Approach to Adaptive Image Analysis for Retrieving and Long-term Monitoring Historical Land Use from Spatiotemporally Heterogeneous Map Sources

Land use changes have become a major contributor to the anthropogenic global change. The ongoing dispersion and concentration of the human species, being at their orders unprecedented, have indisputably altered Earth’s surface and atmosphere. The effects are so salient and irreversible that a new geological epoch, following the interglacial Holocene, has been announced: the Anthropocene. While its onset is by some scholars dated back to the Neolithic revolution, it is commonly referred to the late 18th century. The rapid development since the industrial revolution and its implications gave rise to an increasing awareness of the extensive anthropogenic land change and led to an urgent need for sustainable strategies for land use and land management. By preserving of landscape and settlement patterns at discrete points in time, archival geospatial data sources such as remote sensing imagery and historical geotopographic maps, in particular, could give evidence of the dynamic land use change during this crucial period. In this context, this thesis set out to explore the potentials of retrospective geoinformation for monitoring, communicating, modeling and eventually understanding the complex and gradually evolving processes of land cover and land use change. Currently, large amounts of geospatial data sources such as archival maps are being worldwide made online accessible by libraries and national mapping agencies. Despite their abundance and relevance, the usage of historical land use and land cover information in research is still often hindered by the laborious visual interpretation, limiting the temporal and spatial coverage of studies. Thus, the core of the thesis is dedicated to the computational acquisition of geoinformation from archival map sources by means of digital image analysis. Based on a comprehensive review of literature as well as the data and proposed algorithms, two major challenges for long-term retrospective information acquisition and change detection were identified: first, the diversity of geographical entity representations over space and time, and second, the uncertainty inherent to both the data source itself and its utilization for land change detection. To address the former challenge, image segmentation is considered a global non-linear optimization problem. The segmentation methods and parameters are adjusted using a metaheuristic, evolutionary approach. For preserving adaptability in high level image analysis, a hybrid model- and data-driven strategy, combining a knowledge-based and a neural net classifier, is recommended. To address the second challenge, a probabilistic object- and field-based change detection approach for modeling the positional, thematic, and temporal uncertainty adherent to both data and processing, is developed. Experimental results indicate the suitability of the methodology in support of land change monitoring. In conclusion, potentials of application and directions for further research are given

Geostatistical Models for Exposure Estimation in Environmental Epidemiology.

Studies investigating associations between health outcomes and exposure to environmental pollutants benefit from measures of exposure made at the individual level. In this thesis we consider geostatistical modelling strategies aimed at providing such individual-level estimates. We present three papers showing how to adapt the standard univariate stationary Gaussian geostatistical model according to the nature of the exposure under consideration. In the first paper, we show how informative spatio-temporal covariates can be used to simplify the correlation structure of the assumed Gaussian process. We apply the method to data from a historical cohort study in Newcastle-upon-Tyne, designed to investigate links between adverse birth outcomes and maternal exposure to black smoke, measured by a fixed network of monitoring stations throughout a 32-year period. In the second paper, we show how predictions in the stationary Gaussian model change when the data and prediction locations cannot be measured precisely, and are therefore subject to positional error. We demonstrate that ignoring positional error results in biased predictions with misleading prediction errors. In the third paper, we consider models for multivariate exposures, concentrating on the bivariate case. We review and compare existing modelling strategies for bivariate geostatistical data and fit a common component model to a data-set of radon measurements from a case-control study designed to investigate associations with lung cancer in Winnipeg, Canada