UNIT 72 - GIS AND GLOBAL SCIENCE

• Remotely sensed imagery
• Terrestrial-based sources

• Multiple sources
• Data volumes
• Geometric rectification, geographic referencing
• Issues of data storage
• Database model
• Documentation, access, dissemination, archiving
• Internal dataset consistency
• Merging terrestrial and satellite data
• In summary

• CORINE
• UN Environment Program GRID project
• Global Change Diskette Project
• Digital Chart of the World

UNIT 72 - GIS AND GLOBAL SCIENCE

Compiled with assistance from Helen Mounsey, Birkbeck College, University of London

A. INTRODUCTION

• why do we need GIS and databases for the globe?

• ever-increasing concern over the quality of the earth's environment
  • frequent press reports on issues such as global warming and the greenhouse effect, the ozone hole, deforestation and water pollution

• these are global issues, but we can also identify disasters, which, although local in origin, have pronounced continental or global scale consequences

• for example, the Brundtland Report noted that during the 900 days the World Commission on Environment and Development was at work:
  • the African drought put at risk the lives of 35 million people, and probably killed up to 1 million of them
  • the leak at a chemical factory in Bhopal, India, killed 2000 people and injured 200,000 more
  • the explosion at a nuclear power plant at Chernobyl, USSR, caused environmental damage throughout Europe
  • a chemical fire in Switzerland caused toxic materials to be transported by the Rhine as far as the Netherlands
  • at least 60 million people died of diarrhoeal diseases caused by malnutrition and dirty water
  • of these, only the Bhopal incident could be argued to be local in its effects

• there is clearly an ever-greater need to monitor processes at a global scale in order to gain knowledge of the earth's processes and how these affect and are affected by human activity
  • this knowledge is very sketchy at present

• two developments contribute to improving the situation:
  • technical development and ever increasing speed and power of digital computing
  • increasing sources of data for use in environmental modeling

• the ultimate aim is a global database and associated GIS (access and analysis system)
  • at a large enough scale (e.g. > 1:250,000) and with fine enough resolution (e.g. &LT 250 m)

  • to enable environmental scientists to develop models which replicate, as near as is possible, the earth's processes
  • would assist in data integration and visualization at global scales

• global databases are derived from two sources
  • remotely sensed imagery
  • terrestrial-based sources - analog maps, statistics and digital data recording

• aircraft and (more usually) satellite-borne sensors provide much information at a global scale for environmental analysis

• characteristics:
  • usually global (or near-global) coverage
  • repeated coverage over intervals of hours to days (depending on sensor) enables construction of time series
  • spatial resolution of data is improving, e.g. for example Landsat MSS - 80 m, SPOT - 10 m
  • very many existing sources of remotely sensed imagery, the largest contributor being NASA

• major new development is the NASA Earth Observing System (EOS)
  • comprehensive information system - includes data processing, access and analysis capabilities as well as hardware
  • aims to be international in system provision, use and benefit
  • will provide consistent, long-running datasets into the 1990s and beyond
  • EOS is based on the collection of data from two proposed NASA polar platforms, one European Space Agency platform and one Japanese polar platform (a polar platform is a satellite in an orbit which passes over both poles)
  • this will generate a massive dataflow (estimated at 1012 bytes (1 terabyte) per day)

Analog maps and tabular statistics
• digital data derived from maps are an important contributor to global databases, and, as a data source, complementary to remote sensing

• usually based on ground survey or checking, digitized cartographic data can provide:
  • human assigned attributes (e.g. place names or administrative boundaries)
  • a more useful / detailed classification of features
  • a historical (pre 'advent of remote sensing') data source

• to be useful the maps from which these data are derived should be:
  • part of a series which offers global coverage and is based on common standards of accuracy of source material and common cartographic conventions
  • at scales larger than 1:1 million - smaller scales are too highly generalized to represent reality with any degree of utility and are of use only for general reference

• maps are a frequent source of data on topography, soils, geology etc.

• tabular statistics originate from many national organizations (e.g. census gathering agencies) and are collected by international organizations into databanks (e.g. the UN, World Bank, OECD, etc.)

• mostly this provides a source of socio-economic data on the 'human' element in global modeling

  Digital data recording

• this source of data results from automatic data logging
  • mostly in the geo-physical and climatological sciences
  • collected mostly on a national basis
  • then assembled into international databases

• some examples include:
  • the World Data Centers
    • 27 centers worldwide who coordinate the global collection of data
    • determine standards for collection and documentation
    • hold multiple copies of the resultant datasets
    • distribute them freely throughout the world
    • emphasis on physical data
    • geology, geophysics, meteorology, atmospheric physics, oceanography
  • the World Meteorological Organization
    • under the World Weather Watch program
    • collects and supplies members with observational data and processed products for meteorological forecasting

• there are many other such international organizations, gathered together under the auspices of the International Council of Scientific Unions
  • ICSU has endorsed the establishment of the International Geosphere Biosphere Project (IGBP), which has the long-term aim of describing the various processes which affect the Earth's environment, and the manner in which they may be changed by human action

Multiple sources

• global modeling and prediction will in most cases demand data from multiple sources

• often there will be a mixture of remote sensing and analog input
  • remotely sensed data is global in coverage and updated frequently
  • remotely sensed data is most useful when calibrated with ground-based data
  • but, ground-based data often lacks global coverage and is updated infrequently

• possibly the most pressing problem, especially as far as remotely sensed sources of data are concerned

• volumes are potentially huge
  • surface area of earth is order 1014 sq m
  • single coverage of SPOT imagery at 10 m resolution is order 1012 pixels
  • assuming a single value / pixel is stored in 1 byte - then dataset is order 1012 bytes, or 1 terabyte

• note that this is for only one coverage!
  • most application will require more than one coverage in time series, and possibly data from other sources as well

• note that this is for current SPOT platform - future EOS will generate order one terabyte per day - this is 104 conventional magnetic tapes per day, or over a mile of shelves in a conventional tape library per week

• a number of other problems are a consequence of such massive data volumes:

• global databases must be referenced to a common coordinate system if they are to be merged and manipulated from a number of sources
  • conventionally, latitude / longitude is used

• the cost of installing a referencing system into remotely sensed datasets may be prohibitively high

• simple raster data structures are inadequate if rapid access is required for browsing and retrieval

• possible solutions include:
  • vector - but spatial relationships in the data must be stored (which increases data volumes further) or computed every time (which increases access times further)
  • hierarchical - structures based on recursive subdivision of the earth's surface
  • various forms of data compression

• must be multi-purpose and global scale

• the number of possible relationships is large

• the object definition is inexact (what may be a point at one scale is an area at larger scales)

• there is a not-insignificant administrative problem in devising methods of user access to global databases
  • how to document datasets for international, multidisciplinary use?

  • how to enable the user to access a centralized database, probably over computer networks?
  • how to disseminate data and documentation - in what format and on what physical medium?

• how to handle the costs of archiving such large databases?
  • are dual copies of every dataset strictly necessary?

• have all the individual datasets being merged into a global database been collected and classified to consistent and high standards of accuracy, with a common definition of variables?

• this is less of a problem with remotely-sensed data

• can be a serious problem with terrestrial-based sources:
  • e.g. there is no consistently produced topographic map series of the world at a scale greater that 1:1 million
  • e.g. for soils, largest scale is 1:1.5 million, with considerable disagreement between soil scientists over a consistent, global classification of soil type
  • e.g. there is no strictly consistent definition of "total population" in the UK Census of Population through time (some years include visitors, etc.)
  • this is a problem within a well established national data source
  • when multiplied to international scales such problems may become insurmountable

• what errors may be generated through this process?

• how are missing data handled?

• there are problems of data acquisition, in particular from terrestrial sources

• there are problems of spatial and temporal inconsistency both within and between datasets

• we have limited experience in handling very large databases, with consequent issues of structure, access and administrative support
  • the cost of all this may at least in the short term limit the development of global databases

• the increasing application of GIS is, however, critical, to enable users to:
  • merge datasets from widely disparate sources
  • handle, analyze and map the results
  • model environmental processes at a global scale

• very few truly global environmental databases at present

• some are developing at a continental scale, e.g. CORINE

• Co-Ordinated Information on the European Environment

• established as a project in 1985, to build an environmental database covering the 12 Member States of the European Community (2.25 million sq km)

• has now assembled a large number of consistent datasets into a centralized database

• these include:
  • topography
  • soils
  • climate
  • nature reserves and other sites of scientific importance
  • water resources
  • atmospheric pollution

• to be of use to policy makers, these are supported by socio-economic data

• certain key findings from the project:
  • many datasets are unavailable for reasons of cost, confidentiality, administrative inadequacies or non- collection in certain countries
  • where available, they may mask massive discrepancies in data collection methods and huge internal inconsistencies
    • e.g. in a climatological dataset we find 8 methods of calculation for evapotranspiration, and 5 for maximum monthly temperature
  • enormous problems in merging datasets derived from maps of different scales and projections
  • merging larger scale dataset derived from remotely- sensed sources with smaller scale ones from terrestrial sources involved fundamental decisions on generalization vs. loss of detail
  • important issues of user access and data use, especially by unskilled users who may not understand the 'fuzzy' nature of some of the datasets, and the likelihood of error propagation through application of GIS techniques

• nevertheless, the project is expanding both in content and scale
  • likely to be subsumed into the Environmental Agency being established in the European Community for the provision of technical, scientific and economic information for use in environmental monitoring

• GRID = Global Resources Information Database

• established in 1980, and now based in Nairobi

• GRID aims to:
  • establish global, regional and, in some cases, national environmental datasets of known quality
  • establish computer systems which can handle these
  • establish regional nodes for the dissemination of local sub-sets of data

  • train scientific staff in the use of this information

• unlike CORINE, it draws heavily on data from remotely- sensed sources (from NASA), and also from other bodies such as FAO (Food and Agriculture Organization of United Nations), UNESCO (United Nations Educational, Scientific and Cultural Organization) and IUCN (International Union for the Conservation of Nature)

• much of its work has been at regional or continental scale thus far
  • e.g. projects on sea level rise in the Mediterranean, and the distribution of elephants in Africa

• moving towards global-scale studies
  • e.g. global deforestation project

• a project of the International Geosphere-Biosphere Program

• a project designed to create and distribute to research groups, particularly the developing countries, medium- resolution digital data sets on diskettes for micro- computers

• contains satellite imagery and complementary thematic data

• sponsored by the Defense Mapping Agency
  • contracted to ESRI

• source - the Operational Navigational Charts
  • coverage at 1:1 million of all the world's land area
  • show elevation (500 m contours), cultural features, hydrography
  • maintained for air navigation
  • currently being digitized

• is intended to be a general source of high resolution cartographic data for the globe
  • to be delivered in 1991 on CD-ROM

Most of the material in this unit is extracted from various papers in:

Mounsey, H.M. (Ed.), 1988. Building Databases for Global Science, Taylor and Francis, London. See in particular papers by Simonett and by Peuquet in Part Two, and by Mooneyhan (on GRID) in Part Three.

Additional material:

Briggs, D.J. and H.M. Mounsey, 1989. "Integrating land resource data into a European Geographical Information System," Journal of Applied Geography 9:5-20. A good source on the CORINE project.

IGBP, 1988. Global change report #4: a plan for action, International Geosphere Biosphere Project, Stockholm.

Many other reports on global science are available from IGBP, ICSU and NASA.

DISCUSSION AND EXAM QUESTIONS

1. Discuss the relative advantages of the various spatial data models in global database building. Give examples of datasets which might be best suited to each type.

2. The greatest problems in the construction of global databases lie not with the datasets, hardware or software, but with the "liveware" - the human element of use (or abuse!) of the databases. Discuss some of the issues which might lie behind this statement.

3. Select one of the major disasters mentioned in this unit (or another known to you of similar magnitude). Discuss likely sources of data, and particular GIS techniques, which you would use to address this problem and its associated issues.

4. Some parts of the world are relatively rich in spatial data, and others are relatively poor. Examples of the latter include much of the Third World and Antarctica. Because of gaps in coverage and variable quality it could be argued that the globe as a whole is data-poor. Is spatial data handling technology more or less valuable in data-poor areas? Discuss the arguments on both sides of this issue.

Back to Geography 470 Home Page

Back to GIS & Cartography Course Information Home Page