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Detection of Coastal Landforms in a Deltaic Area Using a Multi-Scale Object-Based Classification Method


Affiliations
1 Geosciences Group, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad 500 037, India
2 Department of Geo-Engineering, Andhra University College of Engineering (A), Vishakhapatnam 530 003, India
3 Department of Physical Sciences, Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, Chitrakoot 485 780, India
 

Coastal landforms play an important role in protecting deltaic areas from erosion due to the action of waves. However, landforms in the deltas are dynamic and vulnerable to changes due to the effect of natural disasters like floods and cyclones. Automatic detection of dynamic landforms from satellite data can provide important inputs for effective coastal zone management. In this study, we developed an Object-Based Image Analysis (OBIA) technique to identify and map landforms in the Krishna delta, east coast of India using Resourcesat-2 LISS-IV multispectral image (5.8 m) and digital elevation model (DEM) (4 m). Since landforms are represented at multiple scales, the plateau objective function method was used to select appropriate scales during multiresolution segmentation. Knowledge-based rules in OBIA, using the parameters tone, texture, shape and context derived from satellite images and height from DEM were developed for classification of landforms. A total of 11 landforms (beach, beach ridge, swale, tidal creek, marsh, spit, barrier bar, mangrove, natural levee, channel island and channel bar) were mapped using this approach. High detection accuracy of these landforms indicates that the method developed has the potential for geomorphological mapping of dynamic landforms in low lying deltaic areas.

Keywords

Beach, Cyclone, DEM, Image Segmentation, Mangrove, OBIA, Resourcesat-2.
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  • SAC (ISRO), Coastal Zones of India, Space Applications Centre (ISRO), Ahmedabad, India, 2012; http://sac.gov.in

  • Nageswara Rao, K., Evolution and dynamics of the Krishna Delta, India. Natl. Geograph. J. India, 1985, 31, 1–9.

  • Prabaharan, S., Srinivasa Raju, K., Lakshumanan, C. and Ramalingam, M., Remote sensing and GIS applications on change detection study in coastal zone using multi temporal satellite data. Int. J. Geomatics Geosci., 2010, 1, 2.

  • Saranathan, E., Chandrasekaran, R., Soosai Manickaraj, D. and Kannan, M., Shoreline changes in Tharangampadi village, Nagapattinam district, Tamil Nadu, India – a case study. J. Indian Soc. Remote Sensing, 2011, 39, 107–115.

  • Bishop, M. P., Shroder, J. J. F. and Colby, J. D., Remote sensing and geomorphometry for studying relief production in high mountains. Geomorphology, 2003, 55(1–4), 345–361.

  • Iwahashi, J. and Pike, R. J., Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature. Geomorphology, 2007, 86(3–4), 409–440.

  • Smith, M. J. and Pain, C. F., Applications of remote sensing in geomorphology. Progress Phys. Geography, 2009, 33(4), 568–582.

  • Gustavsson, M., Development of a detailed geomorphological mapping system and GIS geodatabase in Sweden, Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, 2006, p. 236.

  • Philip, G. and Sah, M. P., Geomorphic signatures of active tectonics in the Trans-Yamuna segment of the western Doon valley, northwest Himalaya, India. Int. J. Appl. Earth Observ. Geoinfor., 1999, 1(1), 54–63.

  • Shroder Jr, J. F. and Bishop, M. P., A perspective on computer modeling and fieldwork. Geomorphology, 2003, 53, 1–9.

  • Martha, T. R., Sharma, A. and Vinod Kumar, K., Development of meander cutoffs – a multi-temporal satellite-based observation in parts of Sindh River, Madhya Pradesh, India. Arabian J. Geosci., 2015, 8(8), 5663–5668.

  • Martha, T. R., Ghosh, D., Vinod Kumar, K., Lesslie, A. and Ravi Kumar, M. V., Geospatial technologies for national geomorphology and lineament mapping project – a case study of Goa state. J. Indian Soc. Remote Sensing, 2013, 41, 905–920.

  • Xiaojun, Y., Damen, M. C. J. and Van Zuidam, R. A., Use of thematic mapper imagery with a geographic information system for geomorphologic mapping in a large deltaic lowland environment. Int. J. Remote Sensing, 1999, 20(4), 659–681.

  • Dragut, L. and Blaschke, T., Automated classification of landform elements using object-based image analysis. Geomorphology, 2006, 81(3/4), 330–344.

  • van Asselen, S. and Seijmonsbergen, A. C., Expert-driven semiautomated geomorphological mapping for a mountainous area using a laser DTM. Geomorphology, 2006, 78(3–4), 309–320.

  • Schneevoigt, N. J., van der Linden, S., Thamm, H. P. and Schrott, L., Detecting Alpine landforms from remotely sensed imagery – A pilot study in the Bavarian Alps. Geomorphology, 2008, 93(1–2), 104–119.

  • Dragut, L. and Eisank, C., Automated object-based classification of topography from SRTM data. Geomorphology, 2012, 142/141, 21–33.

  • Hay, G. J. and Castilla, G., Object-based image analysis: Strengths, weaknesses, opportunities and threats (SWOT). In Proceedings OBIA, Commission VI, WG VI/4, Calgary, CA, 2006.

  • Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S. and Weng. Q., Per-pixel vs object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing Environ., 2011, 115, 1145–1161.

  • Babu, P. V. L. P., Morphological evolution of the Krishna delta. Photonirvachak, 1975, 3, 21–27.

  • Nageswara Rao, K. and Vaidyanadhan, R., Evolution of the coastal landforms in the Krishna delta front, India. Trans. Inst. Indian Geogr., 1979, 1, 25–32.

  • Gamage, N. and Smakhtin, V., Do river deltas in east India retreat? a case of the Krishna Delta. Geomorphology, 2009, 103, 533–540.

  • Baatz, M. and Schäpe, A., Multiresolution Segmentation: an optimization approach for high quality multi-scale image segmentation. In Angewandte Geographische Informationsveraarbeitung XII, Beitrage zum AGIT Symposium Salzburg (eds Strobl, L. J., Blaschke, T. and Griesebener, T.), Herbert Wichmann Verlag, Heidelberg, 2000, pp. 12–23.

  • Martha, T. R., Kerle, N., Jetten, V., van Westen, C. J. and Vinod Kumar, K., Characterizing spectral, spatial and morphometric properties of landslides for automatic detection using object-oriented methods. Geomorphology, 2010, 116(1–2), 24–36.

  • Vamshi, G. T., Martha, T. R. and Vinod Kumar, K., An object-based classification method for automatic detection of lunar impact craters from topographic data. Adv. Space Res., 2016, 57, 1978–1988.

  • Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V. and Vinod Kumar, K., Segment optimisation and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans. Geosci. Remote Sensing, 2011, 49(12), 4928–4943.

  • GSI, ISRO, Manual for National Geomorphological and Lineament Mapping on 1 : 50,000 scale. A Project under National (Natural) Resources Census (NRC), 2010.

  • Shufelt, J. A., Performance evaluation and analysis of monocular building extraction from aerial imagery. IEEE Trans. Pattern Anal. Mach. Intell., 1999, 21(4), 311–326.


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  • Detection of Coastal Landforms in a Deltaic Area Using a Multi-Scale Object-Based Classification Method

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Authors

Tapas R. Martha
Geosciences Group, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad 500 037, India
A. Mohan Vamsee
Department of Geo-Engineering, Andhra University College of Engineering (A), Vishakhapatnam 530 003, India
Vikas Tripathi
Department of Physical Sciences, Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, Chitrakoot 485 780, India
K. Vinod Kumar
Geosciences Group, National Remote Sensing Centre, Indian Space Research Organization, Hyderabad 500 037, India

Abstract


Coastal landforms play an important role in protecting deltaic areas from erosion due to the action of waves. However, landforms in the deltas are dynamic and vulnerable to changes due to the effect of natural disasters like floods and cyclones. Automatic detection of dynamic landforms from satellite data can provide important inputs for effective coastal zone management. In this study, we developed an Object-Based Image Analysis (OBIA) technique to identify and map landforms in the Krishna delta, east coast of India using Resourcesat-2 LISS-IV multispectral image (5.8 m) and digital elevation model (DEM) (4 m). Since landforms are represented at multiple scales, the plateau objective function method was used to select appropriate scales during multiresolution segmentation. Knowledge-based rules in OBIA, using the parameters tone, texture, shape and context derived from satellite images and height from DEM were developed for classification of landforms. A total of 11 landforms (beach, beach ridge, swale, tidal creek, marsh, spit, barrier bar, mangrove, natural levee, channel island and channel bar) were mapped using this approach. High detection accuracy of these landforms indicates that the method developed has the potential for geomorphological mapping of dynamic landforms in low lying deltaic areas.

Keywords


Beach, Cyclone, DEM, Image Segmentation, Mangrove, OBIA, Resourcesat-2.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi06%2F1338-1345