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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing2008 
change of Tehran Metropolitan Area over a study period of two 
decades. Neural networks with SNNS software were trained 
using two datasets; and then predictions were made and the 
simulation results were compared to the real condition visually. 
For prediction, years 1980 and 2000 were used to train the 
network and then predicted land use change for the year of 
2020 was accomplished. A series of simulation outputs for the 
case study town in the period 1980-2000 were produced. 
This paper presents the development of land use change model 
by illustrating an application of remote sensing imageries, 
neuro-fuzzy and GIS as the inputs of this model. The use of 
neural networks has increased substantially over the last several 
years because of the advances in computing performance 
(Skapura, 1996) and the increased availability of powerful and 
flexible ANN software. The main objective of this paper is to 
exhibit how GIS and neuro-fuzzy can be used to forecast land 
use changes over selected region at a specific period. GIS is 
used to develop spatial data layers to be used as inputs to the 
ANNs while basic principles of neuro-fuzzy has been used for 
land use change modelling. The scheme of this work starts 
with design of the neural network and identify the inputs using 
a historical data, using subset of the inputs, the network was 
trained, then neural network testing was performed using the 
full data set of inputs and the final stage was to use the 
information extracted from the neural network to forecast land 
use changes. In this paper the development of land use change 
model for Tehran Metropolitan Area based on neuro-fuzzy was 
undertaken. 
2. DATA PREPARATION 
2.1 Study area 
Tehran Metropolitan Area located in North of Iran was selected 
for the purpose of this study. The city is considered as the 
largest and main economical city of Iran. Tehran is one of the 
most rapidly populated area and land use change regions in Iran. 
From 1980 to 2000, resident population in the Tehran nearly 
doubled. Tehran located on latitude 35° 45' N and longitude of 
51° 30' E. Data on land use, transportation, natural features, 
public lands and digital elevation were integrated using 
Arc/Info 9.2 software. 
2.2 Data Sources 
Two Landsat TM images of the Tehran Metropolitan Area and 
surrounding areas were taken at 1980 and 2000. The images 
were geometrically registered and normalized. National 
Cartographic Centre (NCC) database developed around 2000 
was used as the source of topographic data. NCC topographic 
data were integrated with our database to represent the 
transportation network and locations of roads to provide the 
appropriate inputs to the GIS-based model. 
2.3 Data Pre-processing 
The historical images were geometrically rectified and 
registered to the same projection namely, Universal Transverse 
Mercator (UTM) WGS 1984 to lay them over each other. 
Figure 1 shows the rectification and registration results as an 
example for the 1980 and 2000 images. Registration errors 
were about 0.5 pixel. 
1980 2000 
Figure 1. Rectification and Registration results for 1980 and 
2000 
2.4 Image Classification 
The initial (1980) and final (2000) Landsat imageries were 
subjected to a classification of zones. Supervised classification 
was utilized to classify the images to different land use 
categories. In order to classify the rectified images, four classes 
of interest were specified in the images namely, road, 
residential area, service centre and administrative area. These 
classes were identified using sets of high resolution 
orthophotographs over the area and USGS land classification 
map as ground references. The overall testing accuracy for the 
classification of Landsat TM image (1980) was 82.14%, while 
it was 86.46% for Landsat TM image (2000) which Figure 2 
shows the image classification results. 
Figure 2. Image classification results for 1980 and 2000 
3. SIMULATING URBAN LAND USE CHANGE USING 
NEURO-FUZZY APPROACH 
Land use change for Tehran Metropolitan Area has been 
modelled using two urban maps, one from 1980 and the other 
from 2000. The land use change model follows four sequential 
steps including: (1) processing/coding of data to create spatial 
layers of predictor variables; (2) applying fuzzy logic for 
spatial layers; (3) integrating all input grids (4) analysis of the 
difference between model outputs and real change. 
In Step 1, processing of spatial data, generation series of base 
layers were established within a GIS of exhibiting different 
land uses. In step 2, generation of inputs to neural network, 
achieved from fuzzifications of spatial layers that was prepared 
in previous stage. Step 3, integration of predictor variables is 
required using ANN method. Step 4, spatial error analysis