GIS for Coastal Zone Management - Chapter 2

CHAPTER TWO Bridging the Land-Sea Divide Through Digital Technologies Simon Gomm 2.1 INTRODUCTION There are many different types of users of coastal zone information, from the casual user who may only want to browse, to the sophisticated user who makes frequent use of mapping and demands continuous improvement. These user communities are diverse in the topics they address, covering such areas as Local and Central Government, environmental and economic analysis, and also increasingly leisure use. A common mapping framework that bridges the land-sea divide allows users to build applications and decision-making tools necessary to promote the shared use of such data throughout all levels of Government, the private and non-profit sectors and academia. A consistent framework also serves to stimulate growth, potentially resulting in significant savings in data collection, enhanced use of data and assist better decision-making. As well as a physical division, the land-sea divide has also, for many spatial data suppliers, acted as a limit to their area of responsibility, or formed a data product boundary. As a result users wanting to model the diverse aspect of the coastal zone across this divide have had to identify, obtain and combine separate datasets to provide the data coverage they require. The combination process must resolve integration problems resulting from the differing projections, scale of capture and other specification issues of the source datasets. This process can be time consuming, result in inconsistent data and can cause a hindrance to the management of a particularly sensitive environmental zone. This chapter will look at the technical issues involved with the integration of data across the land-sea divide and identify means for resolving these. Examples within this chapter have been drawn from the work done by Ordnance Survey of Great Britain, United Kingdom Hydrographic Office and the British Geological Survey on integrated coastal zone mapping project (ICZMap) (Gomm, 2001). © 2005 by CRC Press LLC 2.2 DATA SPECIFICATIONS At the commencement of a project the user will need to have assessed the project’s spatial and non-spatial requirements, and these will provide the key criteria for defining and selecting suitable data. Typically such criteria will include: physical extent of the project area, data content, attribution, positional accuracy, spatial resolution, currency, projection, datum and transfer format. These criteria are all discussed in more detail below. 2.2.1 Spatial Extent The extent of the area for which data are required needs to be defined clearly in the coordinate system to be used in the project. At a minimum the extent should be defined by a bounding rectangle using the x and y coordinates of 2 diagonal corners. Ideally the extent should be delineated by a bounding polygon at an appropriate spatial resolution. A bounding polygon will allow for better selection of relevant information and itself provide a tool for analysis within the project. In defining the extent a sufficient margin should be included to allow for inclusion of features which may have an influence on the application. Figure 2.1 Bounding Rectangle. © Crown Copyright 2004. All rights reserved. Figure 2.2 Bounding Polygon. © Crown Copyright 2004. All rights reserved. © 2005 by CRC Press LLC In Figure 2.1 the extent of the ICZMap coastal zone project is defined as a simple bounding rectangle and in Figure 2.2 as a polygon defined by buffering the coastline 20km offshore and 5km inland. In practice, the latter was more appropriate for the application and datasets of the ICZMap project, with its emphasis on the processes and dynamics of the land-sea interface. Whilst it is normal to consider defining extent only in two dimensions, it is worth noting that there will be applications where height/depth extents, along with the temporal aspect may need to be specified. 2.2.2 Data Content The use to which data are to be put will define the features that are required. At the simplest level this may merely be raster imagery for use as backdrop mapping within an application. At a more complex level, where the data are required to form part of the analysis, the user will need to consider what features and object classes are required. In defining these requirements there will be some features that fall uniquely on the land or in the sea, but there will also be others that by their nature bridge this land-sea divide. 2.2.3 Attribution Attribution of features within a dataset can be at a number of different levels. As a minimum this could take the form of feature coding, allowing selection of required features for analysis and symbolisation. Beyond this, additional attribution concerning the real-world properties of the feature and how it was captured will increase the versatility of the data. These attributes will vary for different features within a dataset depending on the real-world object they represent, and the uses for which the dataset was intended. 2.2.4 Positional Accuracy and Spatial Resolution An appreciation of the positional accuracy requirements of a dataset is important to ensure that the data are used in an appropriate way with other datasets, and to put results of any analysis in to the correct context. The positional accuracy of spatial data can be expressed both in terms of its absolute accuracy and its relative accuracy. Absolute accuracy is a measure to which a coordinated position in the dataset corresponds to the true position of the real world feature it represents. Relative accuracy expresses the positional accuracy between points in a dataset, and is a comparison of the distance between features in a dataset with the real world distance. Datasets with a high relative accuracy but low positional accuracy may indicate a systematic shift in the data with respect to the coordinate system. The spatial resolution represents the coordinate precision to which data are stored in the dataset, and affects the maximum achievable accuracy for a dataset, © 2005 by CRC Press LLC e.g. if the spatial resolution is only 10m then this could affect the absolute accuracy of a point by up to 7m. 2.2.5 Currency Currency is sometimes overlooked as an aspect of a dataset’s specification. Attribution should ideally allow for the recording of temporal information at feature level. This can include creation date, capture date and modification date (with nature of modification). Occasionally, temporal information will be limited to the date of the dataset’s last update. The temporal information is important both from an analysis point of view, but also for the initial selection of data for the project. For example, the ability to select the position of the top and bottom of coastal slopes for a given year can allow predictive analysis of coastal erosion processes to be studied. 2.2.6 Projections and Datum Coordinates within a dataset will be relative to a given projection and datum. Map projections attempt to represent the curved surface of the Earth on a flat plane. All projections are approximations and some will better represent large areas of the world, albeit with large distortions, while others are more suitable for small specific areas with much smaller distortions. A geodetic datum or spheroid is a mathematical approximation of the surface of the Earth, which itself is an imperfect sphere. Numerous geodetic datums have been calculated over the years, some suitable for global applications and others calculated to minimise errors on a country-by-country basis. Height datum represents a base level from which elevations are measured and each information source may have its own. Datasets on land will often share a common height datum (e.g. use of Ordnance Survey Newlyn Datum in Great Britain); however different datums will usually be used for marine datasets. Frequently, with marine datasets having their origin from navigation charts, depth values will be typically based on local lowest astronomic tide values. For a project it is important to determine what projection and datum are most suitable for the application, and to ensure that you are aware of what the projections and datum of the source datasets are. 2.2.7 Data Transfer Format Datasets are transferred between media using a chosen transfer format. Whilst there is no single common transfer format there are a number of commercial ones, such as Autodesk-AutoCAD DXF, ESRI Shapefile and MapInfo TAB and MIF/MID formats, which are becoming more widely adopted as de facto standards. Such formats can be limited as they are primarily intended for transfer between users of the same software. When read by other software, information may be lost due to data model differences. © 2005 by CRC Press LLC Ideally a neutral, non-software-specific, transfer format is needed. Many attempts have been made to try to implement these, one of the most recent examples being GML (Geography Markup Language) promoted by the OpenGIS consortium as a development of the more widely used XML Web document markup language. However, in practice, the software being used for a project may act as a limiting factor in what formats it can and cannot read. 2.2.8 Metadata Much of the information describing a dataset should be included in its metadata if present. As well as accompanying a dataset, metadata are now frequently being held on readily accessible databases, allowing users to identify datasets suitable to their requirements. The international standard for digital geospatial metadata (ISO 19115) is now being adopted by many national bodies, such as US Federal Geographic Data Committee (FGDC) for its Content Standard for Digital Geospatial Metadata (CSDGM) (http://www.fgdc.gov/metadata/metadata.html/), and the UK’s Association for Geographic Information (AGI) with its GIGateway project (http://www.gigateway.org.uk/default.asp/). 2.3 DATA SELECTION The properties of datasets that have been outlined above cover some of the points that need to be considered in establishing a project and specifying data requirements. Metadata services provide a tool for locating data, but they will not tell you if they are best suited for your purposes. It is unlikely that any dataset will fully match your requirements, but what should be considered is the ability to integrate and modify them to satisfy your needs. Typically, more flexible datasets will have a richness of feature classes and attribution, and good positional accuracy, but these will come at an increase in cost and data volumes. 2.4 DATA INTEGRATION It is unlikely that a single dataset will satisfy the full needs of a project. This is particularly true when modelling the coastal zone, where there is likely to be one source for the land, another for the sea and potentially other subsidiary datasets straddling both. In these cases there will inevitably be some data integration issues. Typical problems and potential solutions are discussed here. 2.4.1 Differences in scale Datasets which have been captured from graphic products can be said to have a nominal scale at which they were intended to be used. In these datasets, differences in scale will show themselves in the positional accuracy and spatial resolution, as well as in the features and attributes that are present e.g. field © 2005 by CRC Press LLC ... - tailieumienphi.vn