Evaluation 
Excellent 
Good 
Possible 
Unsuitable 
Urban 
Water 
Impossible (Non-soil) 
Impossible (Stcep) 
  
    
Figure 4. The assessment map for grassland establishment. (*Color Printed) 
CASE STUDY 2 : Evaluation of Land 
Degradation in Northeastern Syria 
In the Abdal Aziz region in northeastern Syria, animal 
grazing on native pasture has occurred for a long time. 
Grazing has been the typical land-use system in this 
region; however, land-use is changing due to an 
expansion of cultivation and forestation recently. These 
human activities have caused difficulties affecting 
environmental changes. To maintain Sustainable 
agricultural activity in dry regions, regional resource 
management in consideration of environmental 
conservation is necessary. 
This study aims to evaluate hazards of land degradation, 
using NN and a map database of the Abdal Aziz region. 
(1) Outline of Evaluation 
Figure 5 shows a flow chart for production of a hazard 
map of land degradation. The operation was divided to two 
parts; first, to construct NN using NEURO92, and 
Second, to draft a hazard map of land degradation using 
the constructed network. The supervisor for NEURO92 
was made as follows. 
First, 214 points data were extracted at randomfromthe 
map database. From these point data, elevation, slope, 
direction of aspect, soil category and vegetation 
coverage were input to the input layer of thes upervisor. 
Output data of the supervisor were degree of degradation 
and extent scale of degradation. They were input s ingly. 
Thus, two supervisor files were prepared, having the 
same input layer and different output layer. 
Using the two supervisors, NN were constructed with 
NEUROS2. To s elect the most suitable network, 15trials 
with different numbers of hidden layer units were 
performed. The learning cycle was repeated 10,000 
times. 
After the s election of networks, the output of the whole 
points in map database were calculated using the network 
parameters with GIS. The outputs were mapped to 
express the degree and extent scale of land degradation. 
These two outputs were integrated by multiplication to 
produce the final hazard map of land degradation. 
Factors = Input 
    
    
  
  
  
Elevation 
Slope 
Direction of aspect 
Soil category 
Vegetation coverage 
br 
Results = Output 
  
  
Degree of land degradation 
Extent of land degradation 
  
  
     
  
Y 
Data Extraction for Supervisor (N=214 points) 
Learning by NEURO92 
= 
NN for Degree NN for Extent 
Map Calculation Map Calculation 
  
(Estimation Map for Degree ) X ( Estimation Map for Extent ) 
      
  
  
S 
f ERE, 
f : e 
e Hazard Map of land Degradation P 
PIERII 7 
Figure 5. The flow to draft the hazard map 
of land degradation. 
791 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996