International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
      
INVESTIGATING THE CAPABILITY OF IRS-P6-LISS IV SATELLITE IMAGE FOR 
PISTACHIO FORESTS DENSITY MAPPING (CASE STUDY: NORTHEAST OF IRAN) 
F. Hoseini*, A. A. Darvishsefat" *, N. Zargham* 
a MSc Graduated, Faculty of Natural Resources, University of Tehran, Iran - Faezehhoseinil S@yahoo.com 
b Prof., Faculty of Natural Resources, University of Tehran, Iran - adarvish@ut.ac.ir 
* Associate Prof., Faculty of Natural Resources, University of Tehran, Iran - zargham@ut.ac.ir 
KEY WORDS: IRS-P6-LISS IV, Ground Truth, Pistachio Forests, Forest Density, Iran 
ABSTRACT: 
In order to investigate the capability of satellite images for Pistachio forests density mapping, IRS-P6-LISS IV data were analyzed in 
an area of 500 ha in Iran. After geometric correction, suitable training areas were determined based on fieldwork. Suitable spectral 
transformations like NDVI, PVI and PCA were performed. A ground truth map included of 34 plots (each plot 1 ha) were prepared. 
Hard and soft supervised classifications were performed with 5 density classes (0-596, 5-10%, 10-15%, 15-20% and > 20%). 
Because of low separability of classes, some classes were merged and classifications were repeated with 3 classes. Finally, the 
highest overall accuracy and kappa coefficient of 70% and 0.44, respectively, were obtained with three classes (0-5%, 5-20%, and > 
20%) by fuzzy classifier. Considering the low kappa value obtained, it could be concluded that the result of the classification was 
not desirable. Therefore, this approach is not appropriate for operational mapping of these valuable Pistachio forests. 
1. INTRODUCTION 
Pistachio (Pistacia vera) natural forests are one of the most 
important forest reserves in Iran which are considerable for 
their conservational and economical conditions. Providing 
spatial information and thematic maps are essential for 
recognize recognizing and manage managing these valuable 
forests. Unfortunately, there is not enough and comprehensive 
information related to this area. 
Forest canopy play a significant role in forest production, 
climate and ecosystem functions. Information describing forest 
canopy is essential for monitoring and sustainable management. 
There is a high urgency to develop new methods to map forest 
canopy in an efficient and timely manner. Remote sensing 
techniques are applied due to their synoptic and repetitive 
nature and ability to be utilized in areas which are not easily 
accessible. There are a variety of approaches that have been 
used to map forest canopy: (1) object-based classification 
[Dorren et al., 2003], (2) Maximum likelihood forest canopy 
density mapper (4) artificial neural network [Joshi ef al., 2006]. 
Due to importance of forest density maps, various satellite data 
and forest stands, continuing of these researches is needed. 
The main objective of this study is to investigate the capability 
of IRS-P6-LISS IV image for Pistachio forests density mapping 
in Khorasan province of Iran. 
2. METHODOLOGY 
2.1 Study area 
The study area in this research covered 500 hectares of the 
Khajekalat pistachio forest of Khorasan province in northeast of 
Iran at the border to Turkeminestan (36? 56' 56" N and 49? 53' 
54" (Figure 1). This area is characterized by warm summer and 
cold winter with low precipitation which is located on 500-1243 
meters above sea level. 
  
* Corresponding author. 
Pure stands of pistachio (Pistacia vera) with rarely associate 
species as Zygophylum eurypterum & Amygdalus scoparia in 
forms of bush, shrub and high trees are observed [Revised Plan 
of Pistachio Forest of Khajehkalat Studies, 2009]. Forest 
structure consists of coppice and high stands with low density 
(<25%) as sown in figure 2. 
a SER 
  
Figure 1. Location of the study area in Iran 
    
Figure 2. Overall view of the study area