Coastlines are fundamentally unique features. Their behavioural patterns are predominantly subjects of numerous environmental and engineering studies. With the magnitude of the effects of coastal flooding and erosion, there is a need for accurate techniques for data capture and data processing. With an emphasis on the zero-cost open source GIS software, there is no existing evaluative procedure for demonstrating the analytical capabilities of large-scale UAV-based outputs for microscale analysis for small changes on the beach such as sediment movement, erosion/accretion of individual features. There were four different drone surveys in the study area to determine microscale change over time. A three-stage analysis procedure helps in determining the overview of the coastline and highlights the region(s) of optimum change requiring further spatial analysis with micro-scale change detection. Results obtained show the analytical capabilities of large-scale UAV-based outputs for relatively small but detailed analysis using the open-source QGIS. Results obtained show that spatial analyses of the zoomed areas at different viewpoints and scales improve the confidence level of the hillshading and contour of that particular section on the coastline. The UAV photogrammetry and the three-stage analysis procedure can detect a 1cm change on the beach using the free and open-source QGIS software. It shows the profile modelling of the coastal inundations for both pre and post-flooding events at sub-centimetre intervals can be obtained from QGIS modelling, data computation, analysis, and visualization.

Coastal cliff is almost a vertical elongated structure with a wave-cut notch and a landslip. Cliffs are geological formations with an almost unpredictable and unstoppable detachment between constitutes formations. Due to health, safety, environmental, and military restrictions, there are more regulations and restrictions on the use of drones. There are also the issues of portability and high cost for the purchase of hybrid drones and Terrestrial Laser Scanners (TLS). These negate the regular monitoring of the coastal cliff. This research develops a rapid, low-cost, and precise digital photogrammetry methodology for the continuous monitoring of the cliff by using the pole as the platform and a mobile phone as a sensor. The most practical vertical camera angle, image overlaps, survey distance to the cliff, and realistic time range for surveys are all determined from the basic surveying principles. Precise geometrically related point clouds generated are with or without the Global Navigation Satellite Systems (GNSS). The standard deviation for “alignment and surface deviation” at every point on each point cloud is ± 0.05 m in the Northing and ± 0.12 m on the Easting’s for the self-calibrated digital camera and without the use of GNSS control points. With the GNSS controls, the maximum deviation in the XYZ coordinates is ± 5 cm. Change analysis performed identifies areas of cut, fill, and the segment of threats in all point clouds. The photogrammetric technique developed is very cheap, simple, and reliable with minimum labor. The results obtained indicate the applicability of this methodology for second-order cliff Deformation study.

Within the GIS world, LIDAR becomes an important and convenient data so urce. Many researchers are developing algorithms to extract a bare-earth model and building boundaries from LIDAR data. This pap er presents two different methods for building reconstruction using LIDAR data. The first is a traditional method using filtered LIDAR data and combining cadastral building boundaries data (for example, Ordnance Survey Landline data). The second uses the Voro noi Diagram to trace building outlines. To extrude buildings, we use Computer Aided Design (CAD)-type Euler Operators to create a TIN m odel and then we use the operators to modify the TIN, e.g. extrude buildings, interactive editing or further spatial analysis.

The global positioning system (GPS) has become the most extensively used positioning and navigation tool in the world. Applications of GPS abound in surveying, mapping, transportation, agriculture, military planning, GIS, and the geosciences. However, the positional and elevation accuracy of any given GPS location is prone to error, due to a number of factors. This has serious implications for some applications, such as real-time navigational systems. GPS accuracy can be significantly improved with additional data, possibly from multiple sources, and especially from multiple receivers. In the case of a single GPS receiver, its position and elevation can be considerably improved with the use of spatial data. For vehicle tracking, map matching can be employed to intelligently snap the GPS location to a road centreline, while height aiding can augment the GPS solution by utilising a digital terrain model (DTM), thereby reducing the number of satellites required to determine a position. This paper describes the use of map matching and height aiding, and examines the effect of different terrain resolutions (Ordnance Survey 1:50,000 and 1:10,000 scale DTMs) on plan position and elevation accuracy for vehicle tracking. Furthermore, the user's choice of interpolation algorithm for estimating heights from the DTM is investigated. The results of the experiments described in this paper demonstrate that height aiding alone reduces the mean error in elevation from 22.5 to 17.5m for of a single GPS receiver, and the mean error in plan position from 6 to 5m. However, map matching and height aiding combined, reduces the elevation RMSE of a single GPS receiver from 22.5m to approximately 4m (1:50,000 scale DTM) and down to 0.8m (1:10,000 scale DTM), while the plan position RMSE is reduced from 5.9 to 3.2m (either DTM). It is also demonstrated that when the number of satellites visible to the receiver is reduced, or the satellite geometry is poor, map matching and height aiding considerably improves the plan and elevation accuracy. The use of a higher-order interpolant (e.g. a bicubic or biquintic polynomial) is shown to slightly improve performance, compared to a bilinear interpolant, for the lower-resolution DTM, but has little overall benefit for the higher resolution DTM.

The National Assembly for Wales (NAW) is responsible for monitoring the effects of dredging for fine aggregate from sandbanks off the coast of South Wales. A key monitoring objective is the analysis of changes to the sandbank bathymetry and the adjacent coastline. This paper reviews the monitoring strategy, with a particular emphasis on the use of laserscanning with LiDAR over the last six years for large-scale topographic beach mapping and analysis. The focus is on the methodologies that were implemented in order to make the data compatible, consistent and usable within a geographical information system (GIS). The issues that are addressed include data handling strategies; automatic error/blunder detection of spurious data; identifying sources of errors; projection and datum transformations; LiDAR artefacts; quality control; choice of digital terrain model and spatial resolution; choice of interpolation algorithm; the calibration of LiDAR surveys to ensure consistency; and LiDAR accuracy compared with land surveys. Some of these issues have proved problematic, which if not correctly resolved, can produce significant application errors, thus reducing confidence in this technology. The paper concludes with some examples of the analyses undertaken to date.

The maintenance and dissemination of spatial databases requires efficient strategies for handling the large volumes of data that are now publicly available. In particular, satellite and aerial imagery, radar, LiDAR, and digital elevation models (DEMs) are being utilised by a sizeable user-base, for predominantly environmental applications. The efficient dissemination of such datasets has become a key issue in the development of web-based and distributed computing environments. However, the physical size of these datasets is a major bottleneck in their storage and transmission. The problem is often exaggerated when the data is supplied in less efficient, proprietary or national data formats.This paper presents a methodology for the lossless compression of DEMs, based on the statistical correlation of terrain data in local neighbourhoods. Most data and image compression algorithms fail to capitalise fully on the inherent redundancy in spatial data. At the same time, users often prefer a uniform solution to all their data compression requirements, but these solutions may be far from optimal. The approach presented here can be thought of as a simple pre-processing of the elevation data before the use of traditional data compression software frequently applied to spatial data sets, such as GZIP. Identification and removal of the spatial redundancy in terrain data, with the use of optimal predictors for DEMs and optimal statistical encoders such as Arithmetic Coding, gives even higher compression ratios. Both GZIP and our earlier approach of combining a simple linear prediction algorithm with Huffman Coding are shown to be far from optimal in identifying and removing the spatial redundancy in DEMs. The new approaches presented here typically halve the file sizes of our earlier approach, and give a 40–62% improvement on GZIP-compressed DEMs.

The fundamental aim of a digital elevation model (DEM) is to represent a surface accurately, such that elevations can be estimated for any given location. It is, therefore, necessary to have efficient and precise algorithms for the computation of surface elevations between given points. The hypothesis presented here, is that higher-order interpolation techniques will always be more accurate than the likes of the popular bilinear algorithm. This hypothesis will be evaluated through an assessment of the accuracy with which DEMs can be interpolated to higher spatial resolutions. A variety of interpolation techniques are assessed, ranging from the one-term level plane to the 36-term biquintic polynomial. In general, techniques that take account of the local terrain neighbourhood are more consistent and accurate, reducing the rms. error by up to 20% of the bilinear interpolant.

The paper demonstrates how two algorithms used by geographical information systems (GIS) for site selection can be implemented on a multi-processor computer architecture. The architecture used is a cluster of parallel workstations. Such networks are prevalent in corporate industry, academia, local and national government, and environmental agencies, and are available for exploitation without the need for any further investment. The paper provides an explanation as to how the problems can be divided dynamically and effectively, amongst the processors that make up the cluster. The results demonstrate that speed-up performance is almost directly proportional to the number of processors utilized.

A necessary application of GIS is the ability to determine the most suitable sites for a particular development, particularly if visibility, or more specifically, visual intrusion is likely to be a key factor in gaining planning approval. To aid the site selection process in these instances, it is essential to have an indication of the nature of the terrain and, in particular, be able to determine the visibility index of either an area or set of points. The visibility index of a point is defined as the number of occurrences of a particular entity within an unobstructed line-of-sight (LOS) from that point. Entities might include buildings, postcode centroids, or most commonly, other surveyed heights within the terrain model. As the complexity of the terrain model or the number of entities increases, so does the processing overhead. This inevitably means that conventional uni-processor systems provide poor response times when calculating the visibility index. In these circumstances parallel processing techniques can be used to enhance the benefits delivered by the GIS.In the past, the authors have focused their research around a PC-based Transputer network for parallelising this problem (Ware et at, 1996). Whilst this has provided some benefits, the processors are nonetheless slow in comparison to modern processors and moreover are a specialised resource. The increasing availability of computer networks, combined with advances in modern PC operating systems, means that many organisations already have an existing multipurpose parallel processing resource which could be utilised.In this paper, the authors present algorithms and techniques for parallelising the visibility index operation on a cluster of Pentium-based workstations. In this environment the main area of concern is the division of the workload between all the available processors, such that only subsets of the regular grid digital elevation model (DEM) are mapped to each processor. Each DEM partition is processed separately and later collated to provide die overall solution. Promising results for an inter-visibility study of the South Wales valleys are presented, with emphasis on an application for determining the most suitable locations for siting wind turbine generators (WTGs) within a pre-defined area.

This paper describes a project being undertaken to validate the performance of a Geographical Information System (GIS) being developed to predict, quantify and qualify the visual impact of proposed wind farm developments. In order to determine the optimal choice of terrain data model, elevation and topographic data sets, and visibility algorithms, a validation study for the Taff fly Wind Farm has been commissioned. The issues involved in calculating and presenting visual impact are discussed, whilst a methodology for verifying the results is also described. Existing methods for presenting the visual impact of wind farms are limited and susceptible to error, whilst the interpretation of the results can often do more harm than good. The use of GIS to visualise the probable impacts is illustrated to add meaning to the results of a wind farm visibility study.

The Constrained Implicit TIN (Triangulated Irregular Network) is a data storage scheme offering both efficient storage and access to spatial data, and flexible modelling of the geographical phenomena represented by that data. The scheme provides for the inclusion of both 2-D geographical objects, defined in terms of constituent polygons, lines and vertices, and terrain defining triangulated surfaces made up from collections of 3-D points or contour lines. The triangulations conform to constrained Delaunay criteria, and allow for the inclusion of the 2-D geographical objects as a series of constraining edges. Efficient storage is facilitated by the fact that surface triangulations are derived at run-time in response to specific user queries. Thus the storage overhead inherent in traditional TIN models, incurred by having to store TIN topology, is removed. Effective access to data is supported by means of a spatial indexing scheme based loosely on the PMR-quadtree. Since TIN construction takes place on-the-fly, the Constrained Implicit TIN offers flexibility in that decisions as to which specific phenomena are included in a particular model can be deferred until run-time.

This paper addresses the issues faced by developers in identifying suitable sites for wind farms. The selection criteria can be broadly classified into those which minimise environmental impact, including visual intrusion; maximise the resource potential; and minimise the development costs; or a compromise solution which considers all of these issues. Given a set of weighted inclusion and exclusion criteria, a geographical information system (GIS) can easily model this information to determine the optimal wind farm sites. The paper illustrates a flexible approach to achieving this goal, which is driven by the specific requirements of any user.

Europe has seen a remarkable growth in renewable energy in recent years, particularly wind energy. One of the reasons for this growth has been a widespread realisation of the environmental concerns of traditional energy production. However, this increased environmental consciousness is also in danger of severely curtailing wind energy programmes in Europe, as planning applications face vociferous opposition from some campaigners who believe that wind farms scar our most valued landscapes. This paper describes a number of projects in which we are using GIS to support planning applications for wind farm developments. The scope of this work is in four main areas: site selection; visibility analysis; viewshed verification; and the visualisation of environmental impacts, particularly visual intrusion.

Growth in the available quantities of digital geographical data has led to major problems in maintaining and integrating data from multiple sources, required by users at differing levels of generalization. Existing GIS and associated database management systems provide few facilities specifically intended for handling spatial data at multiple scales and require time consuming manual intervention to control update and retain consistency between representations. In this paper the GEODYSSEY conceptual design for a multi-scale, multiple representation spatial database is presented and the results of experimental implementation of several aspects of the design are described. Object-oriented, deductive and procedural programming techniques have been applied in several contexts: automated update software, using probabilistic reasoning; deductive query processing using explicit stored semantic and spatial relations combined with geometric data; multiresolution spatial data access methods combining poini, line, area and surface geometry; and triangulation-based generalization software that detects and resolves topological inconsistency.

The triangulated irregular network (TIN) provides a versatile and widely used approach to representing terrain models in a way that retains the original sample points, adapts to variation in data density and incorporates linear features corresponding to natural or man-made phenomena. Classification of the scale-related priority of the constituent points and linear features can be used to create hierarchical, multiresolution TIN representations. A large proportion of the data items included in conventional and hierarchical TIN data structures are concerned with recording the topology of the triangulation. Although TINs typically use many fewer points than the main alternative representation of regular rectangular grids, they do not usually occupy much less data storage, due to the topological data. This paper describes a novel multiresolution storage scheme which uses an approach termed the Implicit TIN, in which storage requirements are reduced significantly by storing only the vertices and constraining features. TIN topology is reconstructed by a procedure when required. The Implicit TIN storage scheme has been demonstrated in the context of an experimental multiscale database. Variable-scale access is provided to polygonal regions of a terrain model which includes polygon, line and point objects that constrain the constructed triangulated model.