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