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Coastal Classification of Minab area Using Aster Image
1M. Damizadeh , 2S. Choopani 3H. Hossaini pour
1,2,3 Agricultural and Natural Resources Research Center of Hormozgan Province
P.O.Box 79159-1577, Bandar Abbas, Iran
Tel. ++98-761-3333050
Fax: ++98-761-3332496
E mail: damizadeh@yahoo.com
Abstract
Use of satellite images is an effective technique for study and classification of land resources in coastal zone. This article assess the application of Aster images for classification of Minab coastal areas in south of Iran.
This study is based on Shepard Classification Method using Aster images for the year 2001. For this purpose, the image of the study area was evaluated for its capability in recognition of coastal units and then the coastal areas were classified using image processing techniques. There are different methods for classification of coastal zones which Shepard method was selected for classification of the study area.
GIS methods were also applied to estimate coastal characterization parameters for the study area offering the advantages of spatial data handling capabilities and automatic extraction of thematic information.
The results show that the processing of ASTER remote sensing dataset can thus be used as a powerful tool for coastal mapping. Aster sensor has more capability in term of spatial and spectral resolution comparing other images for recognition of coastal phenomena. The results also show that Alluvial Plain, Alluvial Fan and Delta are the largest units in the
study area
Introduction
Coastal zones are mostly subjected to concentrated population and economic activities. In these areas, there is a close relationship between water, soil, vegetation and human activities. The main step for achieving protection and sustainable development of coastal zones is identifying all natural factors and classifying them. Coastal classification provides a suitable tool for better management of coastal zone, resulting less expensive coastal projects and preventing waste of projects budget. There are different methods for classification of coastal zones which Shepard method were selected for classification of the study area.
Shepard (1973) abandons the high-level classificatory submergent-emergent dichotomy of previous workers by placing submergent coasts at a lower level in the classification and suggesting that emergent coasts can be ignored. The basis (highest-level distinction) in this classification is the difference between coasts shaped mainly by terrestrial agencies (Primary Coasts) and those modified by marine processes (Secondary Coasts).
Satellite and airborne remote sensors have been used for several decades for mapping land use and surface cover of the earth. Landsat thematic mapper (TM) data and Spot high resolution visible (HRV) data have been used successfully to detect major categories of coastal wetlands and near shore bottom (coral reef) features (Fikel and Daprato, 1993). The use of satellite imagery for mapping wetlands provides a number of advantages over conventional aerial photographs and other data including timeliness, synopticity, and reduced costs (Dobson et al., 1995).
Method and Materials
The present study focuses on applying remote sensing techniques with employing ASTER data for coastal classification. The study area is located in southern part of Islamic Republic of Iran, 80km in the east of the Bandar Abbas city, north of Persian Gulf. (Fig. 1)
Figure 1: The location of study area
Satellite data from the Aster image was geometrically corrected with respect to 1:50,000 digital topographic sheets and ground control points (GCP) that were taken using GPS. For mapping the coastal units, Aster image was evaluated by per-processing techniques. ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is an imaging instrument that is flying on the NASA’s Terra satellite launched in December 1999. ASTER is a high-resolution multi-spectral imaging sensor with visible-near infrared, short wavelength infrared and thermal infrared spectral bands. ASTER acquires 14 spectral bands and can be used to obtain detailed maps of land surface temperature, emissivity, reflectance and elevation. ASTER data has been used to carry out volcanic studies, urban studies, lithologic mapping, monitoring of coastal environments.
Delineation of different units was performed in two ways. First using some professional remote sensing software and their capabilities, the units boundaries were separated as isodata and second the primary map was corrected manually by visual interpretation and updated features were digitized on screen using geomorphological characteristics of each coastal units. Some Image enhancements techniques were used to obtain more detailed classification. In this stage, Visible and near-infrared bands have been processed and interpreted in mapping framework of the study area and a field work was also performed for checking the units classifying and getting more accurate results.
Figure 2: sample of Aster image from the study area
Results and Conclusion
Figure 3 shows the final coastal classification map of Minab coastal area. The area of each units is tabulated in Table1. The results show that: alluvial plain, alluvial fan and delta units are the largest units in the study area which are very well recognized on the image with their digital number.
Mud flat and sabkha units are the main units in the coastal belt of the study area with an extension of about 86363 hectares. The other important unit is mangrove forests which identified and delineated simply based on the characteristic in the near infrared reflection and its association with mud flats habitats along the estuarine / brackish waters. The total spatial distribution of Mangroves was estimated as 1139 ha.
In addition, the results show that the processing of ASTER remote sensing dataset can thus be used as a powerful tool for coastal mapping. Aster sensor has more capability in term of spatial and spectral resolution than other images for recognition of coastal phenomena.
Mangrove forests is one of the most important units and major ecosystem of coastal area that must be protect against the shipping and human activities and monitored over the time.
Table 1: Coastal map units and area of Minab
Figure 3: Classification map of Bandar Abbas coastal area using Shepard method
References
1M. Damizadeh , 2S. Choopani 3H. Hossaini pour
1,2,3 Agricultural and Natural Resources Research Center of Hormozgan Province
P.O.Box 79159-1577, Bandar Abbas, Iran
Tel. ++98-761-3333050
Fax: ++98-761-3332496
E mail: damizadeh@yahoo.com
Abstract
Use of satellite images is an effective technique for study and classification of land resources in coastal zone. This article assess the application of Aster images for classification of Minab coastal areas in south of Iran.
This study is based on Shepard Classification Method using Aster images for the year 2001. For this purpose, the image of the study area was evaluated for its capability in recognition of coastal units and then the coastal areas were classified using image processing techniques. There are different methods for classification of coastal zones which Shepard method was selected for classification of the study area.
GIS methods were also applied to estimate coastal characterization parameters for the study area offering the advantages of spatial data handling capabilities and automatic extraction of thematic information.
The results show that the processing of ASTER remote sensing dataset can thus be used as a powerful tool for coastal mapping. Aster sensor has more capability in term of spatial and spectral resolution comparing other images for recognition of coastal phenomena. The results also show that Alluvial Plain, Alluvial Fan and Delta are the largest units in the
study area
Introduction
Coastal zones are mostly subjected to concentrated population and economic activities. In these areas, there is a close relationship between water, soil, vegetation and human activities. The main step for achieving protection and sustainable development of coastal zones is identifying all natural factors and classifying them. Coastal classification provides a suitable tool for better management of coastal zone, resulting less expensive coastal projects and preventing waste of projects budget. There are different methods for classification of coastal zones which Shepard method were selected for classification of the study area.
Shepard (1973) abandons the high-level classificatory submergent-emergent dichotomy of previous workers by placing submergent coasts at a lower level in the classification and suggesting that emergent coasts can be ignored. The basis (highest-level distinction) in this classification is the difference between coasts shaped mainly by terrestrial agencies (Primary Coasts) and those modified by marine processes (Secondary Coasts).
Satellite and airborne remote sensors have been used for several decades for mapping land use and surface cover of the earth. Landsat thematic mapper (TM) data and Spot high resolution visible (HRV) data have been used successfully to detect major categories of coastal wetlands and near shore bottom (coral reef) features (Fikel and Daprato, 1993). The use of satellite imagery for mapping wetlands provides a number of advantages over conventional aerial photographs and other data including timeliness, synopticity, and reduced costs (Dobson et al., 1995).
Method and Materials
The present study focuses on applying remote sensing techniques with employing ASTER data for coastal classification. The study area is located in southern part of Islamic Republic of Iran, 80km in the east of the Bandar Abbas city, north of Persian Gulf. (Fig. 1)
Figure 1: The location of study area
Satellite data from the Aster image was geometrically corrected with respect to 1:50,000 digital topographic sheets and ground control points (GCP) that were taken using GPS. For mapping the coastal units, Aster image was evaluated by per-processing techniques. ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is an imaging instrument that is flying on the NASA’s Terra satellite launched in December 1999. ASTER is a high-resolution multi-spectral imaging sensor with visible-near infrared, short wavelength infrared and thermal infrared spectral bands. ASTER acquires 14 spectral bands and can be used to obtain detailed maps of land surface temperature, emissivity, reflectance and elevation. ASTER data has been used to carry out volcanic studies, urban studies, lithologic mapping, monitoring of coastal environments.
Delineation of different units was performed in two ways. First using some professional remote sensing software and their capabilities, the units boundaries were separated as isodata and second the primary map was corrected manually by visual interpretation and updated features were digitized on screen using geomorphological characteristics of each coastal units. Some Image enhancements techniques were used to obtain more detailed classification. In this stage, Visible and near-infrared bands have been processed and interpreted in mapping framework of the study area and a field work was also performed for checking the units classifying and getting more accurate results.
Figure 2: sample of Aster image from the study area
Results and Conclusion
Figure 3 shows the final coastal classification map of Minab coastal area. The area of each units is tabulated in Table1. The results show that: alluvial plain, alluvial fan and delta units are the largest units in the study area which are very well recognized on the image with their digital number.
Mud flat and sabkha units are the main units in the coastal belt of the study area with an extension of about 86363 hectares. The other important unit is mangrove forests which identified and delineated simply based on the characteristic in the near infrared reflection and its association with mud flats habitats along the estuarine / brackish waters. The total spatial distribution of Mangroves was estimated as 1139 ha.
In addition, the results show that the processing of ASTER remote sensing dataset can thus be used as a powerful tool for coastal mapping. Aster sensor has more capability in term of spatial and spectral resolution than other images for recognition of coastal phenomena.
Mangrove forests is one of the most important units and major ecosystem of coastal area that must be protect against the shipping and human activities and monitored over the time.
Table 1: Coastal map units and area of Minab
Figure 3: Classification map of Bandar Abbas coastal area using Shepard method
References
- Ahmadi H., 1984.Geomorphological studding of Persian Gulf, Oman sea and Bandar Abbas area. Journal of Natural Resource of Iran, No. 37
- Bor-Wen tsai, chang-Yi Chang and Tsu-jen Ding (1997) Spatial Analysis in GIS- The Land Use Changes in the Coastal Area of Yunlin County, Taiwan, Journal of Geographical science, No.23,pp1-12.
- Barusseau, Jp. And Radakovitch, o., 1996. Geological record of littoral sedimentary processes at short time scales. Journal of Coastal Research, 12(4). 801-810. Fort Lauderale (Florida), ISSN 0749-0208.
- Donald G. Bailey and Roger d. Shand, Determining Large Scale Sandbar Evoloution, Image Analysis Unit, and Geography Department Massey University, Palmerston North.
- Eric Bird., Coastal Geomorphology An Introduction, , Principal Flow, Department of Geography, University of Melbourne, Australia, John Wily & Sons, LTD.
- FINKL, C.W., 2004. Coastal classification: Systematic approaches to consider in the development of a comprehensive system. Journal of Coastal Research, 20(1), 166–213.
- Karami khaniki, A. 2004. Coasts of Iran. Soil Conservation and Watershed Resear Institute. 411 page.
- Serge Suanez and Mireill Provansal,1998. Large Scale Evoloution of the Rhone Delta (Southeast France), Journal of Coastal Research, Vol. 14, No. 2. Royal Palm Beach (Florida), ISSN0749-0208.
- Shreedhara V., 2001. Tidal wetland mapping using remotely sensed data. proceeding of 22th Asian conference on remote sensing.