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Figure 2. Framework of the study 
2.3 Vegetation indices 
In the next step panchromatic band of QuickBird imagery was 
applied as the base image for geometric correction of the 
ASTER data. For this purpose imagine Auto Sync module of 
ERDAS was used to perform automatic image registration. In 
order to extract more useful information from the ASTER 
images, appropriate image enhancements were applied on the 
different spectral bands. To reduce the effects of the background 
reflectance, a variety of indices were examined and the most 
suitable indices identified among them. 
The most known and widely used ratio-based index, 
Normalized Difference Vegetation Index (NDVI), which is 
sensitive to soil background, was used in this study. NDVI is 
calculated on a per-pixel basis as the normalized difference 
between the red and near infrared bands from an image: 
NDVI= (NIR-RED)/ (NIR+RED) 
Also three of the most commonly used and functionally 
different broadband soil VIs were selected (Bannari et al., 1995; 
Kasawani et al., 2010). 
The VIs was calculated and analyzed using Spatial-Modeler 
module in ERDAS Imagine. The VIs involved in this study are 
as follows: 
-PVI (Richardson and Wiegand 1977): 
PVI = sin (a) NIR — cos (a) Red 
Where, 
a = angle between soil line and NIR axis 
- SAVI (Huete 1988): 
SAVI = [(NIR — R)/ NIR+R+L] X 1+L 
Where, 
L = 0.5 to minimize the soil influence, but the range of value is 
reduced. These values are depending on the red reflectance that 
has a coherent relation with soil influence. 
- TSAVI (Baret and Guyot 1991): 
TSAVI = a (NIR — aR — b)/ [Red + a (NIR — b) + 0.08 (1 + a2)] 
Where, 
a — slope of soil line determine by NIR plot 
b = intercept of soil line 
2.4 Preparing the crown area map 
In order to preparing the crown area map, delineated individual 
tree crowns were used. About 30 plots were located randomly 
on the image with an area about 1 ha and then average crown 
area has been calculated for each plot. 
Then, mean DNs of plots with vegetation indices, was 
calculated. simple linear regression was done between quantities 
of vegetation indices as dependent (Y) variable and same 
quantities of each plot in crown area as independent variable 
(Xi). 
3 RESULTS AND DISCUSSION 
This paper applied color image segmentation to delineate 
individual tree crowns from QuickBird images to estimate mean 
tree per hectare for one of the most common tree species in 
Zagros forest in Iran. The results were compared with field data 
in the crown by crown basis. No missing crown was found and 
the delineated crowns shoed perfect match with field measured 
crowns. The models were developed using single linear 
regression analysis, where the dependent variables were 
vegetation indices, and the independent variable was mean 
crown area per ha. The validation was carried out using paired t- 
Student test and standard error of estimation. The results 
indicated significant correlation between average crown area 
per ha and vegetation indices. Figure 3 illustrate the scatter plot 
and fitted line for each index. The scatter plots showed different 
crown area for the same value of VIs. That means several 
factors other than crown area contribute in creating surface 
reflectance of the area. To produce certain values of the VIs not 
only crown size is important but also tree spatial pattern is 
significant. Small scattered single trees make more mixed pixels 
in the border comparing to the one big tree with the identical 
crown area. Moreover, the ratio between crown area and pixel 
size is important. Many small crowns can be successfully 
delineated using QuickBird imagery while can have strong 
reflectance on the ASTER image.The investigation of several 
VIs to estimate mean crown area in open forests showed the 
relative preference of indices which are based on soil and 
background characteristics. 
4 CONCLUSION 
One of the main objectives of this study was to evaluate the 
capability and suitability of the ASTER satellite data, to 
estimate average crown area per ha in open forests of Iran. The 
presented results showed that spectral and spatial information of 
the ASTER imagery can be correlated with the biophysical 
forest parameters. At least spatial resolution more than the 
average crown diameter is needed for successful development 
of empirical relationships between remote sensing data and field 
measurements. 
Very high resolution remote sensing data can be used to provide 
reliable crown area map. 
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