Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 789-796, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-789-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 789-796, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-789-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  23 Jun 2016

23 Jun 2016

URBAN LAND COVER/USE CHANGE DETECTION USING HIGH RESOLUTION SPOT 5 AND SPOT 6 IMAGES AND URBAN ATLAS NOMENCLATURE

S. S. Akay1 and E. Sertel2 S. S. Akay and E. Sertel
  • 1Istanbul Technical University, Center for Satellite Communication and Remote Sensing, 34669, Istanbul, Turkey
  • 2Istanbul Technical University, Department of Geomatics Engineering, 34669, Istanbul, Turkey

Keywords: Object-Based Classification, Remote Sensing, SPOT-5, SPOT-6, Urban Mapping, Urban Change

Abstract. Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time.

This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.