Selection of Suitable Supervised Classification Techniques for the Geographic Analysis of Land Using GIS Techniques

  • Abstract
  • Keywords
  • References
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  • Abstract

    Nation has realised the changes in the land surface and the influence of this in the whole ecosystem. The activities of human on land is directly deteriorating the environment quality. This paper mainly focuses on the analysis of the destruction of land cover with the development of land use. The performance of five different Supervised Classification algorithms, which are Parallelepiped, Mahalanobis, Neurel Net, Adaptive Coherence and Spectral Angle Mapper  have been analysed in classifying the Landsat Image of kanyakumari district. Automatic classification of five classes using training data have been performed and the best suitable algorithm for the classification of each class have been analysed. Being a tourism centre with coastal areas on all three sides, the development and the deterioration of kanyakumari district have to be monitored constantly. The proposed system is an automatic approach which helps in the analysis of the patterns of land use and land cover which constantly changes and to map each class clearly and distinct from each other using GIS techniques. The system was evaluated using the performance measures like accuracy and  kappa coefficient using the tools Envi, ArcGIS and QGIS. From the performance analysis, the Spectral Angle Mapper with an overall accuracy  of 97% and kappa coefficient of 0.54 has been selected as the best suitable algorithm for the classification of landsat image of kanyakumari district.


  • Keywords

    Kanyakumari, landsat image, Envi 5.1, QGIS.

  • References

      [1] Agarwal C, Green GM, Grove JM, Evans TP & Schweik CM, “A Review and Assessment of Land-Use Change Models Dynamics of Space, Time, and Human Choice”, Gen. Tech. Rep. NE-297. Newton Square, PA: US Department of Agriculture, Forest Service, Northeastern Research Station, (2001).

      [2] Butt A, Shabbir R, Ahmad SS & Aziz N, “Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan”, The Egyptian Journal of Remote Sensing and Space Science, Vol.18, No.2,(2015), pp.251-259.

      [3] Aspinall R, “Modelling land use change with generalized linear models-a multi-model analysis of change between 1860 and 2000 in Gallatin Valley, Montana”, Journal of environmental management, Vol.72, No.1-2,(2004), pp.91-103.

      [4] Fröhlich B, Bach E, Walde I, Hese S, Schmullius C & Denzler J, “Land cover classification of satellite images using contextual information”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2013).

      [5] Blaschke T, Burnett C & Pekkarinen A, “Image segmentation methods forobject-based analysis and classification”, Remote sensing image analysis:Including the spatial domain, (2004), pp. 211–236.

      [6] Boardman, “SIPS User’s Guide Spectral Image Processing System, Version 1.2”, Center for the Study of Earth from Space, Boulder, (1992).

      [7] Ndehedehe C, Ekpa A, Simeon O & Nse O, “Understanding the neural network technique for classification of remote sensing data sets”, NY Sci J, Vol.6, (2013), pp.26-33.

      [8] Dickinson RE, “Land processes in climate models”, Remote Sensing of Environment, Vol.51, (1995), pp.27–38.

      [9] Foody G, “Remote sensing of tropical forest environments: towards the monitoring of environmental resources for sustainable development”, International Journal of Remote Sensing, Vol.24, (2003), pp.4035–4046.

      [10] Geist HJ, “The Land-use And Cover-Change (LUCC) Project”, Land Use, Land Cover and Soil Sciences-Volume I: Land Cover, Land Use and the Global Change, (2009).

      [11] Gupta M & Srivastava PK, “Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India”, Water International, Vol.35, (2010), pp.233–245.

      [12] Hegazy IR & Kaloop MR, “Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt”, International Journal of Sustainable Built Environment, Vol.4, No.1,(2015), pp.117-124.

      [13] Iqbal MF & Khan IA, “Spatiotemporal land use land cover change analysis and erosion risk mapping of Azad Jammu and Kashmir, Pakistan”, The Egyptian Journal of Remote Sensing and Space Science, Vol.17, No.2,(2014), pp.209-229.

      [14] Jan 5, 2017 LULCC can play a vital role in natural resources management (Iqbal and Khan, 2014;Kantakumar and Neelamsetti, 2015; Lin et al., 2015).

      [15] Merchant JW & Narumalani S, Integrating remote sensing and geographic information systems, SAGE Publications Ltd: London, UK, (2009).

      [16] Kaliraj S, Chandrasekar N, Ramachandran KK, Srinivas Y & Saravanan S, “Coastal landuse and land cover change and transformations of Kanyakumari coast, India using remote sensing and GIS”, The Egyptian Journal of Remote Sensing and Space Science, Vol.20, No.2,(2017), pp.169-185.

      [17] B Kassimbekova, G Tulekova, V Korvyakov (2018). Problems of development of aesthetic culture at teenagers by means of the Kazakh decorative and applied arts. Opción, Año 33. 170-186


      [19] Kantakumar LN & Neelamsetti P “Multi-temporal land use classification using hybrid approach”, Egypt. J. Remote Sens. Space Sci., Vol.18, (2015), pp.289-295.

      [20] Kruse FA, Lefkoff AB, Boardman JW, Heiedbrecht KB, Shapiro AT, Barloon PJ & Goetz AFH, “The Spectral Image Processing System (SIPS)-Interactive Visualization and Analysis of Imaging Spectrometer Data”, REMOTE SENS. ENVIRON.,Vol.44, (1993), pp.145-163.

      [21] (PDF) Spectral Correlation Mapper (SCM): An.... Available from: [accessed Jul 13 2018].

      [22] Lambin EF, “Modelling and monitoring land-cover change processes intropical regions”, Progress in Physical Geography, (1997).

      [23] Xiang M, Hung CC, Pham M, Kuo BC & Coleman T, “A parallelepiped multispectral image classifier using genetic algorithms”, Proceedings. IEEE International Geoscience and Remote Sensing Symposium, (2005).

      [24] Mukherjee S, Sashtri S, Gupta M, Pant MK, Singh C, Singh SK, Srivastava PK & Sharma KK, “Integrated water resource management using remote sensing and geophysical techniques: Aravali quartzite, Delhi, India”, Journal of Environmental Hydrology, (2007).

      [25] Rahman, MT, “Detection of land use/land cover changes and urban sprawl in Al-Khobar, Saudi Arabia: An analysis of multi-temporal remote sensing data” ISPRS International Journal of Geo-Information, Vol.5, No.2, (2016).

      [26] Kayet N & Pathak K, “Remote sensing and GIS based land use/land cover change detection mapping in Saranda Forest, Jharkhand, India”, Int Res J Earth Sci, Vol.3, No.10,(2015), pp.1-6.

      [27] Nemani R & Running S, “Land cover characterization using multitemporal red, near‐IR, and thermal‐IR data from NOAA/AVHRR”, Ecological applications, Vol.7(1), (1997), pp.79-90.

      [28] Mahmon NA, Ya'Acob N & Yusof AL, “Differences of image classification techniques for land use and land cover classification. IEEE 11th International Colloquium on Signal Processing & Its Applications (CSPA), (2015), pp.90-94.

      [29] Srivastava PK, Han D, Rico-Ramirez MA, Bray M & Islam T, “Selection of classification techniques for land use/land cover change investigation”, Elsiever,Advances in Space Research, Vol.50, (2012), pp.1250–1265.

      [30] Patel D, Dholakia M, Naresh N & Srivastava P, “Water harvesting structure positioning by using geo-visualization concept and prioritization of mini-watersheds through morphometric analysis in the lower Tapi Basin”, Journal of the Indian Society of Remote Sensing, Vol.40, (2012), pp.299–312.

      [31] Lv Q, Dou Y, Niu X, Xu J, Xu J & Xia F, “Urban land use and land cover classification using remotely sensed SAR data through deep belief networks”, Journal of Sensors, (2015).

      [32] Rawat JS & Kumar M, “Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India”, The Egyptian Journal of Remote Sensing and Space Science, Vol.18, No.1,(2015), pp.77-84.

      [33] Richards IA & Xi J, Remote Sensing Digital Image Analysis-An Introduction, 3rd ed. Springer; Berlin, Germany, (1996).

      [34] Richards JA & Richards JA, Remote sensing digital image analysis, Berlin et al.: Springer, (1999).

      [35] Senthil lekha SL & Kumar SS, “Land cover change analysis of kanyakumari district using sensor images in GIS environment”, International Journal of Applied Engineering Research, Vol.10, No.70,(2015), pp.279-287.

      [36] Senthil lekha SL & Kumar SS, “Classification and Mapping of Land Use Land Cover change in Kanyakumari district with Remote Sensing and GIS techniques”, International Journal of Applied Engineering Research, Vol.13, No.1, (2018), pp.158-166.

      [37] Rwanga SS & Ndambuki JM, “Accuracy assessment of land use/land cover classification using remote sensing and GIS”, International Journal of Geosciences, Vol.8, No.04,(2017), pp.611-622.

      [38] G Mussabekova, S Chakanova, A Boranbayeva, A Utebayeva, K Kazybaeva, K Alshynbaev (2018). Structural conceptual model of forming readiness for innovative activity of future teachers in general education school. Opción, Año 33. 217-240

      [39] Veldkamp A & Lambin EF, “Predicting land-use change”, Agriculture, Ecosystems & Environment, Vol.85, (2001), pp.1–6.

      [40] Zeng YN, Wu GP, Zhan FB & Zhang HH, “Modeling spatial land use pattern using autologistic regression”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2008).







Article ID: 17661
DOI: 10.14419/ijet.v7i3.27.17661

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