Land cover classification using landsat-8 optical data and supervised classifiers

  • Authors

    • K V. Ramana Rao
    • Prof P. Rajesh Kumar
    2018-04-15
    https://doi.org/10.14419/ijet.v7i2.17.11567
  • LANDSAT-8, Optical data, Land cover classification, Maximum Likelihood

  • Abstract

    Land use and land cover information of an area has got importance in various aspects mainly because of various development activities that are taking place in every part of the world. Various satellite sensors are providing the required data collected by remote sensing techniques in the form of images using which the land use land cover information can be analyzed.  Constistency of Landsat satellite is illustrated with two time periods such as Operational Land Imager (OLI) of 2013 and consecutive 2014 procured by earth explorer with quantified changes for the same period in visakhapatnam of hudhud cyclone. Since this city is consisting of mainly urban, vegetation, few water bodies, some area of agriculture and barren,five classes have been chosen from the study area. The results indicate that due to the hudhud event some changes took place.  vegetation and built-up land have been increased by An increase of 19.1% (6.3 km2) and 11% (5.36 km2) has been observed in the case of vegetation and built up area  where as a decrease of 1.2% (4.06 km2), 6.1% (1.70 km2) and 1.2% (0.72 km2) has been observed in the case of  agriculture, barren land, and water body respectively. With the help of available satellite imagery belonging to the same area and of different time periods along with the  change detection techniques landscape dynamics have been analyzed. Using various classification algorithms along with the data available from the satellite sensor the land use and land cover classification information of the study area has been obtained. The maximum likelihood algorithm provided better results compared to other classification techniques and the accuracy achieved with this algorithm is 99.930% (overall accuracy) and 0.999 (Kappa coefficient).

     

     

  • References

    1. [1] "National land use and land cover mapping using Multi Temporal", AWiFS data ." (National Remote Sensing Agency Project report).

      [2] A., Henry., L. mabit, Jaramillio. R.E., Y. Cartagena, and J. Lynch. "Land use effetcs on erosion and carbon storage of the Rio Chimbo watershed." Plant Soil, no. 366 (2011): 477-491.

      [3] Buvtaert, W., Wyseure. G., B. de Bievre, and J Decikers. "The effect of land-use changes on the hydrological behaviour of Histic Andsols in south Ecuador." (Hydral Process), no. 19 (2005): 3985-3997.

      [4] Chavez, P. "Image-based atmospheric corrections revisited and improved." (Photogramm.Eng.Remote Sens), no. 62 (1996): 1025-1036.

      [5] Cortijo, F.J. and N.PD.L. Blanca. "A Comparative Study of Some Non-parametric Spectral Classifiers. Applications to Problems with High Overlapping." (Int. J. remote Sensing) 18, no. 6 (1997): 1259-1275.

      [6] Goetz, A.F.H., Boardman, J.W., Kindel, B., Heidebrechr, K. "Atmospheric Corrections: On Delivering Surface Reflectance from Hyper spectral Images." 3118 (28-30 July 1997): 14-22.

      [7] Herzog, S.K., Martinez. R., Jorgensen, P.M., Tiessen., H., Eds. "Consequences of Climate Change for Ecosystems and Ecosystem Services in the Tropical Andes. In Climate Change and Biodiversity in the tropical andes;."

      [8] Hoffbeck, J.P., D.A Landgrebe. "Classification of Remote Sensing Images Having High Spectral Resolution." (Remote Sens. Environ), no. 57 (1996): 119-126.

      [9] "Landsat Higher Level Science Data Products." (USGS (United States Geological Survey)) 2015.

      [10] Mas, J F. "Monitoring land cover changes; a comparison of change detection techniques." International journal of remote sensing 20, no. 1 (1999): 139-152.

      [11] R. Manonmani, G. Mary Divya Suganya. "Remote sensing and GIS Application in change detection Study in Urban Zone Using multi-temporal satellite." (International journal of geomatics and geosciences) 1, no. 1 (2010).

      [12] Spehn, E.M., M. Liberman, and ) C. (Eds. "Land Use Change and Mountain Biodiversity;." (CRC Press: ) 2003.

      [13] Survey), USGS (United Stated Geological. "Landsat Surface Reflectance High Level Data Products." 2015.

      [14] Young, K.R. "Stasis and flux in long-inhabited locales: Change in rural Andean landscapes. In Land Change Science in the Tropics;." (Springer) 2008.

      [15]

  • Downloads

  • How to Cite

    V. Ramana Rao, K., & P. Rajesh Kumar, P. (2018). Land cover classification using landsat-8 optical data and supervised classifiers. International Journal of Engineering & Technology, 7(2.17), 101-104. https://doi.org/10.14419/ijet.v7i2.17.11567

    Received date: 2018-04-15

    Accepted date: 2018-04-15

    Published date: 2018-04-15