Full text: Special UNISPACE III volume

International Archives of Photogrammetry and Remote Sensing. Voi. XXXII Part 7C2, UNISPACE III, Vienna 1999 
“Resource Mapping from Space” 
9:00 am -12:00 pm, 22 July 1999, VIC Room B 
Vienna, Austria 
1979; Lees and Ritman 1990; Olsson 1989; Skidmore et al. 
1997; Zhou and Garner 1990). The difference between image 
processing (of remotely sensed data) and analysis using 
geographic information systems becomes blurred when 
remotely sensed and other geographically registered data are 
merged in a (raster) GIS data model. There are many common 
processing techniques including geometric correction, 
cartographic output, and classification methods. 
One processing technique that incorporates remote sensing and 
is executed in a GIS, is the expert system. Expert knowledge 
from farmers, agronomists, soil scientists, foresters, indigenous 
people or others interested in land resource management is an 
important (and sometimes the only) source of information. 
Participatory techniques have been adapted to extract 
information through interviews and discussion groups, 
particularly for land use planning. Expert systems can 
incorporate such knowledge into a GIS, and then automatically 
map areas and features of interest. An example is the Land 
Classification and Mapping Expert System (LCMES) 
(Skidmore et al., 1997). Forest soils were mapped using terrain 
parameters such as slope and aspect, remote sensing, and a 
knowledge base generated by interviewing soil scientists. 
Interestingly, there was no difference between the accuracy of 
the expert system map and a map prepared by a soil scientist. 
Expert systems retain the robustness of the scientific method, 
but allow knowledge and other ‘soft’ data to be used in analysis, 
as well as mapping more rapidly and with low'er input of labour. 
Despite these successes with expert systems, as w'ell as the rapid 
growth of GIUS described above, satellite remote sensing has 
played a relatively minor role in the commercial success of GIS. 
There are a number of reasons for the poor uptake of remote 
sensing, but five appear to dominate. Firstly, the coarse spatial 
resolution of satellite images is fine for regional scale studies 
but most GIS analysis is at the local scale. In addition a limited 
suite of wavelength bands have been available, and these have 
usually been rather broad. Secondly, the primary data input into 
GIS has been conventional cartographic maps in the form of 
vector data layers, which are expensive to convert to a digital 
(vector) format. The new high spatial resolution imagery 
described in the previous section will probably become the 
primary data model, as well as providing the primary data 
source. Thirdly, there is a mismatch between the vector data 
structure (based on points, lines and polygons) used in GIS, and 
the raster (pixel) based data structure used in remote sensing. 
There are difficulties in converting between these two data 
models. In addition, users have been conditioned to accept line 
work on maps as representing a boundary', when in fact most 
boundaries of natural resources features (such as soils, 
vegetation species mixes, or wildlife habitat suitability') are 
gradients. ‘Cartographic conditioning’ results in most users 
delineating homogeneous areas (polygons) on maps, instead of 
mapping variables as a continuum, such as the density' of a 
species, or the concentration of soil nutrients. The fourth reason 
for the slow uptake of remote sensing in GIS is poor 
accessibility to data due to the expense of higher resolution 
imagery, poor delivery systems for the data, or data being in the 
wrong format. Tills problem is particularly acute for 
environmental monitoring where budgets are often tight. 
Fifthly, there is a lack of expertise to extract information 
available in remotely sensed images. 
Key issues in the field of integrating GIS and remote sensing 
are scale and accuracy. Scale of the imagery, or the map data 
with which the imageiy is being fused, may be mismatched to 
each other. The scale may alos be inappropriate to the task. 
Detailed local information may be viewed as noise in a regional 
scale, whilst important local detail may be lost when working at 
regional scales. The second key issue is accuracy'. It is still rare 
to find maps, or other spatial products, with a quantitative 
statement of accuracy’, even though accuracy assessment 
techniques are by now well known. 
In this presentation, it is shown that remote sensing has not 
penetrated the natural resource management arena as deeply, 
nor as quickly, as predicted 20 years ago. This is despite rapid 
changes to a service economy, and associated demand for 
information, including spatial information. In less developed 
countries. GIS and remote sensing offer regional and local 
mapping and monitoring, which may be used to assist in 
sustainable development. Growth of GIS has remained very 
high for the last 10 years, but the remote sensing industry' has 
not capitalised on this market, and a number of reasons for this 
are discussed. Some new remote sensing systems may offer 
solutions, including high spatial resolution imageiy, high 
spectral resolution imagery and to a lesser extent radar. 
Aspinall, R. and N. Veitch (1993). “Habitat mapping from 
satellite imagert and wildlife survey using a Bayesian 
modeling procedure in a GIS.” Photogrammetric 
Engineering and Remote Sensing 59(4): 537-543. 
Britannica. E. (1989). The New Encyclopaedia Brittanica. 
Chicago. Encyclopaedia Britannica. 
Burrough, P. A. (1993). Modelling land resource scenarios with 
field data, remote sensing and process models in GIS 
is easy. NARGIS 93, Darwin, AGPS: Canberra. 
Campbell, H. and I. Masser (1992). GIS in British Local 
Government: An Overview of Take-Up and 
Implementation. Proceedings of EGIS '92, Munich, 
EGIS Foundation. 
Drucker, P. (1991). “The new' productivity challenge.” Harvard 
Business Review! November-December). 
Fairall, J. (1995). ESR1 still ahead says report. Spatial Business. 
1: 1. 
Green, R. (1990). “Geographic information systems in Europe.” 
Cartographic Journal 27: 40-42. 
Hoffer, R. M. (1975). Natural resource mapping in mountainous 
terrain by computer analysis of ERTS-1 satellite data, 
Purdue University: Indiana.

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