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COMPARISON OF SPOTS AND LANDSAT7 FOR FOREST AREA MAPPING
A. A. Darvishsefat^*, P. Fatehi^, A. Khalil Pour", A. Farzaneh^
" University of Tehran, Faculty of Natural Resources, Karaj, Iran
adarvish(g) ut.ac.ir p_fatehiS8@yahoo.com
° Forest, Range and Watershed Organization of Iran, Ozghol, Tehran, Iran
(khalil pour, alifarzaneh)@hotmail.com
Commision VII/3
KEY WORDS: Forestry, SPOTS, Landsat7, Mapping, Comparison
ABSTRACT:
The main purpose of this study is comparison of the potential of the Landsat7 ETM-- data and SPOTS-H RG image
for forest area mapping at the scale of 1:25000 in northern of Iran. A Landsat7-ETM+ image and SPOTS-HRG in Pan
and XS modes, with the spatial resolution of 5 and 10 meters, were analyzed. The images were almost simultaneous.
The investigation on the image quality showed that there was a non-systematic misregistration between SPOTS-Pan
and SPOTS-XS. There were no other noticeable radiometric and geometric distortions. Orthorectifications of the both
satellite data were implemented using ephemeris data and a digital elevation model. The geocoded images were
checked for reliability in comparison with the digital topographic map. Diverse suitable spectral transformation such
as rationing, PCA, and Tasseled Cap transformation were performed on the images. To generate effective
multispectral bands with better spatial resolution, the green, red and near infrared bands, which lie in the spectral
range of panchromatic band, were fused with Pan using DIRS method as a radiometric approach.
In order to estimate the potential of the satellite data precisely, a ground truth was prepared using aerial photographs.
In this relation, 38 black and white aerial photographs at the scale of 1:7000 were taken especially for this project.
These airphotos were orthorectified and interpreted.
Image classifications were performed using supervised and a new hybrid approach (digital and visual). At first, the
images were classified using maximum likelihood classifier without any knowledge of a-priori possibilities. The best
band sets were selected using Bhatacharrya distance criterion and the defined training areas. To get advantage of
contextual information and expert knowledge, a forest/non-forest classification was also carried out using visual
interpretation at computer display.
Based on comparison of the resulted maps and the ground truth, a better result had been achieved from the hybrid
approach, up to 4%. The Landsat7 and SPOTS data concluded an overall accuracy of 93% and 97 % respectively. It
could be concluded that the SPOTS-HRG data is more appropriate than Landsat7 for forest mapping and updating at
the scale of 1:25000. SPOTS data permits fine and accurate forest/non-forest mapping. Revision of the forest road is
also precisely possible trough SPOTS image.
1. INTRODUCTION potential in forest type mapping. Satellite data have been
used to map forest resources since the lunch of the
Forests are one of the most important features in natural landsat! in 1972 (Joffre, 1991; Brockhaus and Khorram,
resources. Because of the valuable functions of the 1992; Darvishsefat, 1995; Kayitakire er al, 2002). It has
forests, public and political interest is directed toward been suggested that more accurate mapping will be
progress in sustainable use of the forests. Sustainable possible if satellite data with high spatial resolution,
planning and management of forests require some good spectral resolution and improved processing
information about forest resources such as forest map. methods are used (Naseri et al., 2003). The Landsat7
Mapping of forest in large areas is not easily through data has had a significant impact on the remote sensing
field survey or by means of aerial photo interpretation, society (Cheng ef al., 2002). It provides six visible and
In contrast, satellite data with their own characteristics infrared bands (30m), one thermal infrared band (60m)
such as being able to cover large areas, their revisit and a new simultaneous panchromatic band (15m) with
frequency, their constant spatial resolution and finally ^ a wide coverage area (approximately 170 km by 185
their possibility of automatic analysis has created a high — km). Availability of SPOTS data can be considered as a
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