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Using GIS to develop geospatial datasets for understanding environmental processes
in the Victoria Land, Antarctica, coastal biome
Carolyn J. Merry
Dept. of Civil & Environmental Engineering & Geodetic Science
Byrd Polar Research Center
The Ohio State University
Columbus, OH 43210
Summary
A Geographic Information System (GIS) offers an excellent potential for providing a
database of space-time information for the Victoria Land, Antarctica, coastal biome. A
GIS is best described as “a system of hardware, software, data, people, organizations and
institutional arrangements for collecting, storing, analyzing and disseminating
information about areas of the earth” (Dueker and Kjerne, 1989). In the past traditional
methods have been used to analyze map data one layer at a time, involving tedious hours
in map composition and compilation. With a GIS many data layers are organized and
stored together in the computer using automated methods. The data layers are
georegistered using software utilities and then are subsequently used in analysis and
modeling routines to produce final results and maps.
A GIS is composed of four basic subsystems: data input, data storage and retrieval, data
manipulation and analysis, and a reporting system (DeMers, 2000). The data input
subsystem is used to collect and preprocess spatial data from various sources, which can
include remote sensing data, field-sampling data, or from existing digital data sources.
Data conversion routines are available to scan or digitize analog maps for input to a GIS.
The data storage and retrieval subsystem allows for the data layers to be stored in a
common format database that organizes the data, allowing the map layers to be queried
and retrieved for editing and updating and for expansion purposes. The data manipulation
and analysis subsystem allows for data conversion and to perform analytical and
modeling functions on the map layers. The reporting subsystem can be used to display
part or all of the data in the form of maps, tables, graphics or in a report format.
Basic data structures of raster and vector format are used in a GIS. Vector data consists of
points, lines, polygons or surfaces. Raster data are represented on a fixed grid (called grid
cells or pixels) and is typically used for remote sensing data. The data characteristics that
all GIS layers have in common include space, which is where features are located on the
earth’s surface. An attribute is used to identify the quality or characteristic of a feature.
Relationships between features are described with topological relationships that include
area definition, adjacency, connectivity and nestedness. A newer, nontopological data
structure is the shapefile, which stores the geometry and attribute information for a
geographic feature in a dataset that includes a main file, an index file, and a database
table (DeMers, 2000). Time can also be considered as another spatial dimension in a GIS.
There is a wealth of information that can be used in a GIS. Remotely-sensed data would
include back-and-white (panchromatic) aerial photographs, radar images, GPS-located
data, sonar and LIDAR data. Biophysical or hybrid variables can be derived from color
aerial photographs, multispectral data, hyperspectral data and multi-band radar imagery.
Examples of these biophysical or hybrid variables include x,y geographic location, z
topographic/bathymetric, vegetation (chlorophyll concentration, biomass (green and
dead), foliar water content, absorbed photosynthetically active radiation (APAR),
phytoplankton), surface temperature, soil moisture, surface roughness, evapotranspiration,
atmosphere (tropospheric chemistry, temperature, water vapor, wind speed/direction,
energy inputs, precipitation, cloud and aerosol properties), BRDF (bidirectional
reflectance distribution function), ocean (color, phytoplankton, biochemistry, sea height),
snow and sea ice (extent and characteristics), volcanic effects (temperature, gases), land
use (urban infrastructure and land use), and vegetation (stress) (Jensen 2000).
Conventional data sources are also available. These would include data derived from
maps, statistical data from published tables, CAD drawings, archived data from the
Internet, and the 4 Ds from the U.S. Geological Survey (DRG – Digital Raster Graphic,
DLG – Digital Line Graph, DEM – Digital Elevation Model, and DOQQ – Digital
Orthophoto Quarter Quad).
Remote sensing will be important for use in the Victoria Land study because of the
remoteness of Antarctica. Remote sensing is defined as “the practice of deriving
information about the earth’s land and water surfaces using images acquired from an
overhead perspective, using electromagnetic radiation in one or more regions of the
electromagnetic spectrum, reflected or emitted from the earth’s surface” (Campbell,
1996). Landsat (MSS and TM), SPOT, IRS, Ikonos, NOAA AVHRR, SeaWiFS,
Radarsat, ERS and the NASA EOS suite of sensors (MODIS, ASTER, MISR, CERES,
MOPITT) will be important sources of information about Victoria Land. These remote
sensing sensors can provide historical, as well as current, datasets for the coastal biome
environment. Incorporating temporal datasets into a GIS will be a challenge, but a
necessity, for characterizing the coastal biome over time and space.
Data in a GIS can be of four types: nominal, ordinal, interval or ratio data (Chrisman,
1997). With nominal data, the objects are categorized or named into groups and assigned
a class value. The class value numbers are related to names, such as tree species, soil
types, or land cover. Ordinal data implies a rank or order to the data. For example, 1
could be classed as good, 2 as moderate, or 3 as poor. Interval data are placed on a
number line with an arbitrary zero point and an arbitrary interval. The intervals between
data values are meaningful and differences can be quantified. Example data would
include soil temperature, elevation or degrees Fahrenheit. Ratio data measures have a true
origin (zero value) and an arbitrary interval and proportions can be quantified. Example
data would include electromagnetic radiation, rainfall, slope or degrees Kelvin.
A base map provides the geographic reference system to serve as the foundation for later
rectification of additional map layers. The base map shows the spatial relationships
between features and the geometric shape of the features. Coordinate systems are
important in developing the base map. A geographic coordinate system (latitude/
longitude or decimal degrees) is recommended for small-scale research. A plane
coordinate system (Universal Transverse Mercator – UTM or state plane coordinates) is
recommended for large-scale research. Considerations for selecting the coordinate system
and the map projection for the base map are important, as the base reference system
serves as the foundation for additional map layers supporting the various research
activities.
GIS analysis tools are used to derive spatial relationships from the data layers. Examples
of a GIS analysis tool that can be used for understanding and modeling environmental
processes include inventory operations. Such operations include measurement of distance,
area and size, and perimeter of an object. Spatial queries include a graphic query of
objects in the database or a Boolean operation, such as AND, NOT, OR or XOR, that can
be used to query two or more map layers. Database operations include lists or reports
extracted from the relational database.
Other spatial analysis tools that would typically be used in the Victoria Land study would
include buffer and overlay operations. Buffer operations are used to create zones at a
specified distance that surround a point, line or an area feature. The buffer is created
based on the location, shape and orientation of the object. The buffer can also be
incremented at multiple distances from the feature object. Overlay operations occur when
one map layer is overlain on another. Algebraic operations, such as ADD or MULTIPLY,
are performed to result in a new map data layer. With an overlay operation, the newlycreated overlay map may need to be further processed to “clean up” the data layer. Such
operations include dissolve, merge, clip, intersect or union.
Network analysis may also be important for application to the Victoria Land study.
Networks are comprised of links (line entities) that are connected with the link attributes
sharing a common theme, primarily related to flow. Examples include transportation
networks or a stream network. Networks would allow modeling of flow throughout the
Victoria Land area or could be used to track the movement of animals along a corridor.
Three-dimensional analysis allows us to analyze spatial information from a 3-D
perspective. This is important for visualization purposes of the database and for
conducting terrain analysis. Products from a terrain analysis include viewsheds, slope,
aspect, hillshading, elevation, and watershed maps.
The space-time concept in GIS allows us to model spatially-related events using time (t)
as one dimension in space. The correlation between spatial movement and time can be
derived by using conventional mathematical functions, such as distance, which is
calculated in three dimensions. For example, distance, velocity and space-time distance
can be represented, respectively, as:
Distance [ ]x,y,t = sqrt [ x2 + y2 ]
Velocity [ ] x,y,t = sqrt [ x2 + y2 ] / t
Space-Time Distance [ ] x,y,t = sqrt [ x2 + y2 + t2 ]
A proposed distributed computing environment for database storage, data integration and
data sharing across universities, and even countries, is recommended for the Victoria
Land study. This allows for distributed analysis of the datasets and promotes team
collaboration. The GIS should be able to handle a wide variety of datasets. Datasets that
are developed from field activities, traditional map sources and remote sensing data
should be archived and then distributed through various media. The data distribution
policy should be “open” and provide for public access.
In summary, a GIS is recommended for providing a database framework for storing,
analyzing and archiving space-time information for the Victoria Land, Antarctica, coastal
biome.
References Cited
Campbell, J.B., 1996. Introduction to remote sensing, 2nd ed. The Guilford Press: New
York, 621 p.
Chrisman, N., 1997. Exploring geographic information systems. John Wiley & Sons,
Inc.: New York, 298 p.
DeMers, M.N., 2000. Fundamentals of geographic information systems, 2nd ed. John
Wiley & Sons, Inc.: New York, 498 p.
Dueker, K.J. and D. Kjerne, 1989. Multi-purpose cadastre: terms and definitions. ASPRS
and ACSM: Falls Church, Virginia.
Jensen, J.R., 2000. Remote sensing of the environment – an earth resource perspective.
Prentice Hall: New Jersey, 544 p.