Proceedings, XXth congress: Proceedings, XXth congress

Altan, M. Orhan

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Persistent identifier:: 1663674213

Title:: Proceedings, XXth congress

Sub title:: Istanbul, 12 - 23 July 2004

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1663674213

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.; Auch bezeichnet als XXth International Congress for Photogrammetry and Remote Sensing

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing, Commission Primary Data Acquisition

Document type:: Multivolume work

Volume

Persistent identifier:: 166368779X

Title:: Proceedings, XXth congress

Scope:: IV, 226 Seiten

Year of publication:: 2004

Place of publication:: Istanbul

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 166368779X

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(35,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Altan, M. Orhan

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 20., 2004, Istanbul; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: INTERPRETATION OF TROPICAL VEGETATION USING LANDSAT ETM+IMAGERY M. M. Rahman, E. Csaplovics, B. Koch, M. Köhl

Document type:: Multivolume work

Structure type:: Chapter

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). XT r zr" E eue = es =e N ; ^ bL Le À Ms e or adeo etus China ^ | Bangladesh Nepal A \ A = x. t % India +8 - Üü ( Myanmar Study area HL f SIN 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 158 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 In ee RAM RR ged Cad

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