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Title
Special UNISPACE III volume
Author
Marsteller, Deborah

International Archives of Photogrammetry and Remote Sensing. Vol. XXXII Part 7C2, UNISPACE III, Vienna 1999
9
I5PR5
UNISPACE III - ISPRS Workshop on
“Resource Mapping from Space”
9:00 am -12:00 pm, 22 July 1999, VIC Room B
Vienna, Austria
I5PR5
Actual Use
Near-Actual Use
Some Use
Little or no Use
Oil and gas
Agri-industry
Fishing industry
Alternative energy
Land navigation
Transport and shipping
Forestry industry
Coal & mining
European Commission
Navigation industry'
Water and Utilities
Construction
Meteorological sector
Software
Public operations
Insurance
Agri-industry 1 .
T ravel/tourism/leisure
Public national admin
Real industry
Insurance
Local & regional govt
Non-government orgs
News/media
Software
Intergovernment bodies
Intergovernment bodies
Travel/tourism/leisure
Table 1: The use of remote sensing by industry in the EU
NEW SATELLITE REMOTE SENSING SYSTEMS
Some new remote sensing systems offer exciting possibilities
for mapping and monitoring of land resources. High spatial
resolution satellite images are being developed by private sector
companies, with a resolution of between 85 cm and 3 m for
panchromatic and multispectral imagery respectively. The
satellites have a telescope that can point at targets nominated by
customers. The rapid acquisition of large scale images will
assist natural resource managers, particularly for monitoring
purposes. Another advantage of high resolution imagery is that
image resolution will better match the large scale used in most
GIS analyses. In addition, overlapping pairs of images, will
permit high accuracy digital elevation models to be generated.
High spectral resolution, or hyperspectral, imagery combines
spatial imaging with a spectrometer. A spectrometer is a device
which records up to several hundred narrow spectral bands with
a spectral resolution of 10 nm or narrower. In other words,
rather than having a few wide bands for each pixel, imaging
spectrometers produce a more complete spectrum for every
pixel of the image. Unfortunately, broad band scanners tend to
average out important differences in reflectance such as specific
absorption pits. In addition, spectral ranges where the broad
bands are placed may not coincide with the areas of maximum
difference in the spectral curses for vegetation. There is great
potential for hyperspectral remote sensing in sustainable land
management. Materials and cover types may be identified,
permitting a vastly improved ability to map and monitor land
cover and surface materials, monitor land degradation through
changes in vegetation composition and structure, measure
evapotranspiration and assess and monitor environmental
degradation and fragmentation.
A third promising remote sensing image type is radar. The tone
on a radar image relates to backscatter, with a light tone
equating to strong backscatter. When the microwave interacts
with the ground, it is scattered to varying degrees. Because
objects depolarise radiation in different amounts, objects may
be identified from their polarisation. Radar wavelength
significantly affects the backscatter response of objects, so
characterisation of objects based on wavelength is possible.
Radar penetrates haze, smoke or cloud, and may be obtained
regardless of weather of time of day (a major advantage in the
‘cloudy’ northern latitudes, and the Tropics).
INTEGRATION OF GIS AND REMOTE SENSING
As demand increases to access and use limited natural
resources, how may GIS and remote sensing assist in finding
solutions? Linking together remote sensing and GIS is
technically simple. However, GIS and remote sensing
technologies are separated in many organisational entities. In
Europe, the professional split between these two bodies is clear,
with two different associations viz. the European Association of
Remote Sensing Laboratories (EARSel) and the Association of
Geographical Information System Laboratories of Europe
(AGILE). It appears that many GIS and remote sensing
professionals are not aware of the benefits of integrating these
systems.
A number of studies have shown how remote sensing data and
ancillary geographic data may be combined to improve the
accuracy of maps, models and simulations (Aspinall and Veitch
1993; Burrough 1993; Hoffer and and staff 1975; Hoffer et al.
J Italics refer to emerging or ‘rising star’ industries in the remote sensing field