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Comparative Analysis of Pixel and Object Based Classification Approach for Rapid Landslide Delineation with the Aid of Open Source Tools in Garhwal Himalaya

  • Research Articles
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Journal of the Geological Society of India

Abstract

Landslides are the natural geomorphic processes that are essential for the landscape development. Loss of tremendous amount of life, natural resources as well as property has made this phenomenon a natural disaster. Number of researches have been done to understand this phenomenon and how to overcome this calamity with the aid of upcoming modern information services and technological advancements. Despite all these, there are still certain undocumented slope failure events due to inaccessibility and lack of a proper database. With the advancement of satellite remote sensing and geographical information system (GIS), it has certainly become easier to monitor and prepare landslide database especially in rough and rugged terrain of the Himalaya for delineation of risk zones. Free availability of high-resolution images and an open source efficient software have certainly been proved advantageous for this purpose. This paper aims at quick and accurate landslide inventory mapping, using high resolution Sentinel 2 data along with normalized difference vegetation index (NDVI), over which unsupervised and object-based image analysis (OBIA) was done to extract landslide features in an efficient manner. Both these processes were achieved with the aid of open source SAGA (System for Automated Geoscientific Analyses) software. The performances of these classifications were analyzed for their quantitative reliability in extracting the landslide features in two different areas (test area 1 and 2). In this, the object based image classification outstands, with the overall accuracy percentage difference of 17.11% higher than pixel-based classification approach in test area 1 and about 21% higher in test area 2.

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References

  • Andrea Trevino. (2016) Introduction to K-means Clustering Retrieved from https://www.datascience.com/blog/k-means-clustering

  • Bechtel, B., Ringeler, A. and Böhner, J. (2008) Segmentation for Object Extraction of Trees using MATLAB and SAGA. SAGA-Seconds Out, Hamburger Beiträge Zur Physischen Geographie Und Landschaftsökologie. Univ. Hamburg, Inst. für Geographie, pp.1–12.

  • Bist, K. and Sinha, A. (1980) Some observations on the geological and structural setup of Okhimath area in Garhwal Himalaya. Himalayan Geol., v.10, pp.467–475.

    Google Scholar 

  • Blaschke, T. (2010) Object based image analysis for remote sensing. ISPRS Jour. Photogrammetry and Remote Sensing, v.65(1), pp.2–16.

    Article  Google Scholar 

  • Böhner, J., Selige, T. and Ringeler, A. (2006) Image segmentation using representativeness analysis and region growing. Göttinger Geographische Abhandlungen v.115, pp.29–38.

    Google Scholar 

  • Cai, S. and Liu, D. (2013) A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images. Remote Sensing Lett., v.4(10), pp.998–1007.

    Article  Google Scholar 

  • Chen, Z., Zhang, Y., Ouyang, C., Zhang, F. and Ma, J. (2018) Automated landslides detection for mountain cities using multi-temporal remote sensing imagery. Sensors, v.18(3), pp.821.

    Article  Google Scholar 

  • Dhanachandra, N., Manglem, K. and Chanu, Y.J. (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, v.54, pp.764–771.

    Article  Google Scholar 

  • Dingle Robertson, L. and King, D.J. (2011) Comparison of pixel-and object-based classification in land cover change mapping. Internat. Jour. Remote Sensing, v.32(6), pp.1505–1529.

    Article  Google Scholar 

  • Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., & Martimort, P. (2012) entinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, v. 120, pp.25–36.

    Article  Google Scholar 

  • ESA (2015) Sentinel -2 User Handbook (User Guide). Retrieved 2 February 2019, from European Space Agency https://sentinel.esa.int/web/sentinel/document-library/content/-/article/sentinel-2-user-handbookSentinel-2

  • Flanders, D., Hall-Beyer, M. and Pereverzoff, J. (2003) Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Jour. Remote Sensing, v.29(4), pp.441–452.

    Article  Google Scholar 

  • Ghosh, S., van Westen, C.J., Carranza, E.J.M., Jetten, V.G., Cardinali, M., Rossi, M. and Guzzetti, F. (2012) Generating event-based landslide maps in a data-scarce Himalayan environment for estimating temporal and magnitude probabilities. Engineering Geol., v.128, pp.49–62.

    Article  Google Scholar 

  • Gilbertson, J.K., Kemp, J. and Van Niekerk, A. (2017) Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentiation using different classification techniques. Computers and Electronics in Agriculture, v.134, pp. 151–159.

    Article  Google Scholar 

  • Han, J., Lee, J.-G. and Kamber, M. (2009) An overview of clustering methods in geographic data analysis. Geographic Data Mining and Knowledge Discovery, v.2, pp. 149–170.

    Google Scholar 

  • Hölbling, D., Friedl, B. and Eisank, C. (2015) An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Science Informatics, v.8(2), pp.327–335.

    Article  Google Scholar 

  • Jaiswal, R.K., Saxena, R. and Mukherjee, S. (1999) Application of remote sensing technology for land use/land cover change analysis. Jour. Indian Soc. Remote Sensing, v.27(2), pp.123.

    Article  Google Scholar 

  • Keyport, R.N. (2013) A Comparison of Pixel-based Versus Object Oriented Analysis of Landslides Using Historical Remote Sensing Data. Master’s report, Michigan Technological University.

  • Khan, S.S. and Ahmad, A. (2004). Cluster center initialization algorithm for K-means clustering. Pattern Recognition Lett., v.25(11), pp.1293–1302.

    Article  Google Scholar 

  • Makinde, E.O., Salami, A.T., Olaleye, J.B. and Okewusi, O.C. (2016) Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria. Geoinformatics FCE CTU, v.15(2), pp.59–70.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Martins, V., Barbosa, C., de Carvalho, L., Jorge, D., Lobo, F. and Novo, E. (2017). Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Remote Sensing, v.9(4), pp.322.

    Article  Google Scholar 

  • Mueller-Wilm, U., Devignot, O. and Pessiot, L. (2016) Sen2Cor Configuration and User Manual. Telespazio VEGA Deutschland GmbH: Darmstadt, Germany.

    Google Scholar 

  • Nelson, S. (2013) Slope Stability, Triggering Events, Mass Movement Hazards. Nat. Disasters. EENS 3050, Tulane University, pp.1–6.

  • Nowakowski, T. (2015) Ariane space successfully launches Europe’s Sentinel-2A Earth observation satellite. Retrieved 2 February, 2019, from https://www.spaceflightinsider.com/missions/earth-science/arianespace-successfully-launches-europes-sentinel-2a-earth-observation-satellite/

  • Othman, A. and Gloaguen, R. (2013) Automatic extraction and size distribution of landslides in Kurdistan region, NE Iraq. Remote Sensing, v. 5(5), pp.2389–2410.

    Article  Google Scholar 

  • Plank, S., Twele, A. and Martinis, S. (2016) Landslide mapping in vegetated areas using change detection based on optical and polarimetric SAR data. Remote Sensing, v.8(4), pp.307.

    Article  Google Scholar 

  • Rautela, P. and Sajwan, K.S. (2014) Geological Investigations in Rudraprayag District with Special Reference to Mass Instability: Disaster Mitigation and Management Centre Publication, Department of Disaster Management. Government of Uttarakhand.

  • Roering, J.J., Mackey, B.H., Handwerger, A.L., Booth, A.M., Schmidt, D.A., Bennett, G.L., and Cerovski-Darriau, C. (2015) Beyond the angle of repose: A review and synthesis of landslide processes in response to rapid uplift, Eel River, Northern California. Geomorphology, v.236, pp.109–131.

    Article  Google Scholar 

  • Sandric, I., Mihai, B., Chitu, Z., Gutu, A. and Savulescu, I. (2010) Objectoriented methods for landslides detection using high resolution imagery, morphometric properties and meteorological data. In: International archives of the photogrammetry, remote sensing and spatial information sciences—ISPRS archives. International Society for Photogrammetry and Remote Sensing, pp.486–491.

  • Siddque, T. and Pradhan, S. (2018) Stability and sensitivity analysis of Himalayan road cut debris slopes: an investigation along NH-58, India. Natural Hazards, v.93(2), pp.577–600.

    Article  Google Scholar 

  • Singh, C. K., Shashtri, S., Mukherjee, S., Avtar, R., & Singh, S. K. (2010). Monitoring change in land use and land cover in Rupnagar district of Punjab, India using Landsat and IRS LISS III satellite data. Ecological Questions, v. 13, pp.73–79.

    Article  Google Scholar 

  • Sola, I., Garcia-Martin, A., Sandonis-Pozo, L., Álvarez-Mozos, J., Pérez-Cabello, F., González-Audícana, M., & Lloveria, R. M. (2018). Assessment of atmospheric correction methods for Sentinel-2 images in Mediterranean landscapes. Internat. Jour. Appld. Earth Observation and Geoinformation, v.73, pp.63–76.

    Article  Google Scholar 

  • Valdiya, K.S. (1980) Geology of Kumaun Lesser Himalaya. Wadia Institute of Himalayan Geology.

  • Yamazaki, F. and Liu, W. (2016) Remote sensing technologies for post-earthquake damage assessment: A case study on the 2016 Kumamoto earthquake. Paper presented at the 6th Asia Conf. on Earthquake Engg.

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Acknowledgements

The authors express their sincere acknowledgement to the Jawaharlal Nehru University (JNU), New Delhi, India for supporting this research by providing infrastructural and lab facilities. We would gratefully acknowledge library facilities in the form of remote access provided by Central Library, JNU for providing access to instructive research papers and journals. We would also like to extend our thanks to CSIR (Council of Scientific & Industrial Research), India for providing financial support for this research in the form of fellowship grant. We acknowledge ESA Copernicus Open Access Hub and Google earth for the source of Sentinel and Digital Globe images for mapping landslide events. We extend our gratitude to anonymous reviewers for their valuable comments and suggestions which helped to improve the quality of this manuscript.

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Authors and Affiliations

  1. School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, 110 067, India

    Harshita Asthana, Chandrashekhar A. Vishwakarma, Priyadarshini Singh, Pardeep Kumar, Vikas Rena & Saumitra Mukherjee

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  1. Harshita Asthana
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  2. Chandrashekhar A. Vishwakarma
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  3. Priyadarshini Singh
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  4. Pardeep Kumar
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  5. Vikas Rena
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  6. Saumitra Mukherjee
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Correspondence to Saumitra Mukherjee.

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Asthana, H., Vishwakarma, C.A., Singh, P. et al. Comparative Analysis of Pixel and Object Based Classification Approach for Rapid Landslide Delineation with the Aid of Open Source Tools in Garhwal Himalaya. J Geol Soc India 96, 65–72 (2020). https://doi.org/10.1007/s12594-020-1505-1

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  • DOI: https://doi.org/10.1007/s12594-020-1505-1

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