International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
2. STUDY AREA 
The study site covers the area of old growth forest plantation 
of north forest division of Iran. This area is within latitude 
36 42 to 37 20 N and longitude of 49 10 to 49 59 E (Fig 1). 
The climate is wet and is characterize by high rainfall, high 
relative humidity and equable temperature. 
  
  
Figure 1. Color composite (3, 2, 1) of the study area. 
3. DATA 
Three sets of TM & ETM- of 1991, 1998 and 2002 were 
used in this study. The images were geometrically corrected. 
The control points were selected from common points 
recognizable on the ETM+ image and topographic map. The 
ETM+ image (2002) were corrected by 30 points using ig 
degree polynomials (RMSE=0.34 pixel). 26 control points 
were selected on the ETM+, 1991 and 1998 image that by 
image to image registration, (using 2"! degree polynomials), 
two images were corrected (RMSE-0.3&0.43). The pixels 
were resampled by the nearest neighbor method to maintain 
their original data. 
4. METHODOLOGY 
The digital image processing has been done using PC based 
of Intergraph package on Windows XP. In this investigation 
forest canopy density modelling has been prepared. The 
Forest Canopy Density model utilizes forest canopy density 
as an essential parameter for characterization of forest 
conditions. This model involves bio-spectral phenomenon 
modelling and analysis utilizing data derived from four 
indices. 
- A Advance Vegetation Index (AVI). 
- Bare Soil Index (BI). 
- Shadow Index or Scaled Shadow Index (SI, SSI). 
- Thermal Index (TI). 
Using this four indices the canopy density calculate in 
percentage for each pixel. 
> Characteristics of Forest (4) Indices 
The indices have some characteristics as below. The Forest 
Canopy Density Model combines data from the four (4) 
indices. Fig. 1 illustrates the relationship between forest 
conditions and the four indices (VI, BI, SI and TI). 
Vegetation index response to all of vegetation items such as 
the forest and the grassland. Advanced vegetation index AVI 
reacts sensitively for the vegetation quantity compared with 
NDVI. Shadow index increases as the forest density 
increases. Thermal index increase as the vegetation quantity 
increases. Black colored soil area shows a high temperature. 
Bare soil index increases as the bare soil exposure degrees of 
ground increase. These index values are calculated for every 
pixel. Fig. 2 shows the characteristics of four indices 
compared with forest condition. 
High 
  
set 
Low 1 MM 
Vegetation sees Saco 
m——À Bare Soil sommes emperature 
Figure 2. The Characteristics of four indices for forest 
condition 
Note that as the FCD value increase there is a corresponding 
increase in the SI value. In other words, where there is more 
tree vegetation there is more shadow. Concurrently, if there 
is less bare soil (i.e. a lower BI value) there will be a 
corresponding decrease in the TI value. It should be noted 
that VI is "saturated" earlier than SI. This simply means that 
the maximum VI values that can be regardless of the density 
of the trees or forest. On the other hand, the SI values are 
primarily dependent on the amount of tall vegetation such as 
tree, which cast a significant shadow. 
Table.1 shows combination characteristics between four 
indices. 
Hi-FCD | Low-FCD Grass-Land te 
AVI Hi Mid Hi Low 
BI Low | Low Low Hi 
SI Hi Mid Low Low 
TI | Low Mid Mid Hi 
Table.1. Combination Characteristics between Four Indices 
> Normalisation of Landsat TM Bands 
The Landsat TM bands (except band 6) were normalized 
using linear transformation (equations 1 and 2). 
  
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