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installing headquarters, etc). These are integrated to 
the six factors such as observation/line-of-fire, 
covered/concealed area, obstacles, major 
terrain&feature, mobile space, and easiness of troop 
disposition, which will be evaluated using GIS 
techniques and be entered as input data of the 
modeling. The RA rating is computed as the 
summation of each input factor times its weight. 
With quantitative results generated by the RA 
analysis, the intelligence staff analyzes the situation 
and makes a final decision through the RA modeling 
simulation. The detailed process is as shown in 
Figure 1. 
  
| Evaluation of enemy threats 1 
i 
| Determining the area of interest | 
J 
Terrain analysis 
  
  
  
  
. Prepare GIS data layers 
. Integrate to six factors 
. Evaluation of each factor 
. Determining weight for each factor 
l 
| Analysis of the candidate RAs i 
J 
| Obtaining priority table w.r.t RAs | 
  
  
  
  
  
  
  
LE Simulation & Final decision "nm 
  
Figure 1. Processing flow of RA modeling 
The RA modeling assigns a rating to each 
factor, in a scale of 1 to 100, based on functional 
curves. Each rating is then scaled by a weighting 
value, and the weighted ratings are summed to obtain 
the priority for the each RA. The usefulness of the 
priority number comes into play when comparing two 
or more RAs. The RA with the highest priority can 
be considered the most possible invasion RA, and 
therefore the RA modeling becomes a useful tool in 
predicting and preparing for threats of hostile forces. 
The priority of each candidate RA can be obtained by 
the following algebra. 
RA; = 3X F; ) 
where RA; = each RA 
W; = weight for factor j 
F; = factor j 
The weight value for each evaluation factor can 
be determined by the Delphi method(ATTAS, 1993) 
with advices of military experts. The weight values 
are heavily dependent on the field mobile capacity 
and on the type of military units (infantry or 
mechanized), and thus the process of deriving weight 
values is very important. In this case, the expertise 
of experienced military experts and/or terrain analyst 
can be valuable source of information. 
Maps of the RA modeling are designed to be 
Screening-level tools. The present study takes a GIS 
approach to derive the above RA modeling map. Each 
of the six factors is represented by data layers in 
GIS, so changes to the final map can be made simply 
by modifying factor values in the appropriate data 
layers and by re-running the calculation algorithms. 
The GIS analyzing algorithms for the multi-layered 
data such as digital elevation model (DEM), soil, land 
cover, slope gradient, slope aspect, etc. have been 
implemented. These algorithms include weighted 
boolean arithmetic, spatial interpolation, polygonal 
operation, and user defined functions(Laurini, 1992). 
However, this method greatly depends on the 
preparation of accurate GIS data and on the setting 
of weights by military experts and terrain analyst. 
Additionally the artificial intelligence techniques such 
as knowledge-based processing and expert system 
are required. 
The study uses the IPB which has been 
developed to provide military officers with terrain 
information necessary for efficient battlefield operation 
and troop movements. Thus, the system puts great 
emphasis on user interface for military officers to be 
used easily. 
DATA LAYERS 
The terrain analysis is performed to derive the 
necessary GIS data layers for the RA selection and 
evaluation. It contains the calculation of 
covered/concealed area, slope gradient, contour, 
elevation, and mobility. We use DEM, soil, land 
cover, slope gradient, slope aspect, etc. as input data 
for analyzing algorithms. We briefly explain the 
above data layers in the following and display 
corresponding images in Figure 2. 
Covered area 
Covered area is rated by three degrees of 
"good", "normal" and "bad" considering slope gradient, 
land cover, etc. and used it when evaluating terrain 
in an operation field. 
Concealed area 
Concealed area is rated by the aerial observation 
399 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B4. Vienna 1996