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