International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B-YF. Istanbul 2004
aerial photographs, satellite and radar imagery. For example,
selective interpretation keys were developed for tropical
woodland of Tanzania (Howard, 1959), and for coniferous and
hardwoods of Middle-European trees (Grundman, 1984;
Anthony, 1986; modified by Hildebrandt, 1996). On a SPOT
false colour composite natural forest, bamboos, scattered trees,
brush, plantation, reed forest could be identified in northeastern
Bangladesh (Arquero, 1997), On 1:50,000 scale aerial
photographs covering moist tropical forest of Kerala and
Tamilnadu, India, the following forest cover and land use types
were delineated: tropical evergreen, tropical semi-evergreen,
moist deciduous, dry deciduous, teak plantation, eucalyptus
plantation, reeds, bamboos, rubber plantations and tea estates
(Tomar, 1968).
2. MATERIALS AND METHODS
2.1 The Study Area
The study area is located in the southeastern Bangladesh
(Figure 1). The forests of the study area can be classified as
tropical wet evergreen forests and tropical semi-evergreen forest
(Champion et al., 1965). This forest type is characterized by the
presence of a considerable amount of evergreen trees in the
upper canopy. The top canopy reaches a height of 40-60 meters.
A few semi-evergreen or deciduous species may occur, but
usually they do not change the evergreen character of the forest.
The forest is rich in epiphytes, orchids, woody climbers, ferns,
mosses and palms particularly in shady moist places (Das,
1990).
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Study area
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Figure 1 Location of the study area
2.2 Remote Sensing Data
The study selected Landsat ETM+ data, which is relatively
cheap and available. It could be easily used by developing
countries where most of the tropical forests lie. The study has
decided to develop a selective interpretation key for the Landsat
ETM+ sensor for the following reason. Because of the relatively
poor spatial resolution of satellite imagery (in compare with
aerial photographs, where mostly dichotomous keys are widely
used), image color, texture and location are important image
characteristics that must be used to interpret vegetation types;
these characteristics can easily be presented in selective keys.
2.3 Image Pre-processing
The Landsat ETM+ image was received with geometric
correction form USGS. However, a linear shipment was
discovered during the field mission and therefore the image was
re
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shifted accordingly. The current study area consists of small
hills. But topographic normalization could not be applied due to
lack of appropriate digital elevation model. The accuracy of
geo-coding was checked using a portable GPS from the known
location of the geodetic points of Survey General of
Bangladesh. The study used a modified dark object subtraction
method namely COST method (Chavez, 1989; 1996). One
percent minimum reflectance was fixed for atmospheric haze
correction (Chavez, 1996; Moran et al., 1992). Using the model
maker of ERDAS Imagine software the digital count was
converted to surface reflectance.
2.4 Visual Interpretation and Field Verification
A computer-aided unsupervised classification scheme was
applied to the Landsat ETM+ image. Simultaneously a variety
of image channels were displayed on computer-screen and tried
to find an optimal combination where a variety of vegetation
classes were distinguishable. Additionally a correlation matrix
of different spectral bands for the sample pixels was calculated
to ease the band selection process. Interpretation could
delineate eight different vegetation classes. All variety of
vegetation classes was reached during 2002-2003. The location
was identified by a portable GPS (GPS 12, Garmin). A detail
description of vegetation was recorded and a panoramic camera
photo was taken for the field-plots. A total number of seventy
sample plots were collected.
3. RESULTS AND DISCUSSION
3.1 Selection of Optimal Band Combination
The correlation matrix of the spectral bands contains useful
information about the redundancy of information and selection
of optimal band combination for interpretation purpose. If the
bands show strong correlation (value near to 1.000) this indicate
the bands usually contain similar information to each other.
When those bands are visualized the minimum separibilty
among different feature would be noticed. The following table 1
represents the correlation matrix of the selected sample plots
where field sampling was carried out.
Table 1. Correlation matrix of the representative pixels of
Landsat ETM- image for the study area
Bands 1 2 3 4 5 7
1 1.000
2 0.861 | 1.000
3 0.856 | 0.942 | 1.000
4 0.070 | 0.175 | 0.012 | 1.000
S 0.609 0.809 | 0.847 | 0.210 | 1.000
7 0.660 | 0.832 | 0.898 | 0.096 | 0.978 | 1.000
From table 1 it is observed that among visible (band 1-3) and
mid-infrared bands (band 5-7) high correlation exist. It means
that there is a high redundancy of information within those
bands in the vegetated areas. So it makes more sense to select
the bands, which contain minimum redundancy. Therefore, one
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