Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W1, 145-152, 2017
https://doi.org/10.5194/isprs-archives-XLII-5-W1-145-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
15 May 2017
EVALUATE THE CAPABILITY OF LANDSAT8 OPERATIONAL LAND IMAGER FOR SHORELINE CHANGE DETECTION/INLAND WATER STUDIES
W. Pervez, S. A. Khan, E. Hussain, F. Amir, and M. A. Maud National University of Sciences and Technology Islamabad, Pakistan
Keywords: Change Detection Analysis, Satellite Image Processing, Remote Sensing, Imaging Sciences, Operational Land Imager Abstract. This paper explored the capability of Landsat-8 Operational Land Imager (OLI) for post classification change detection analysis and mapping application because of its enhanced features from previous Landsat series. The OLI support vector machine (SVM) classified data was successfully classified with regard to all six test classes (i.e., open land, residential land, forest, scrub land, reservoir water and waterway). The OLI SVM-classified data for the four seasons (i.e. winter, spring, summer and autumn seasons) were used for change detection analysis of six situations; situation1: winter to spring seasonal change detection resulted reduction in reservoir water mapping and increases of scrub land; situation 2: winter to summer seasonal change detection resulted increase in dam water mapping and increase of scrub land. winter to summer which resulted reduction in dam water mapping and increase of vegetation; situation 3: winter to summer seasonal change detection resulted increase in increase in open land mapping; situation 4: spring to summer seasonal change detection resulted reduction of vegetation and shallow water and increase of open land and reservoir water; situation; 5: spring to autumn seasonal change detection resulted increase of reservoir water and open land; and Situation 6: summer to autumn seasonal change detection resulted increase of open land. OLI SVM classified data found suitable for post classification change detection analysis due to its resulted higher overall accuracy and kappa coefficient.
Conference paper (PDF, 2127 KB)


Citation: Pervez, W., Khan, S. A., Hussain, E., Amir, F., and Maud, M. A.: EVALUATE THE CAPABILITY OF LANDSAT8 OPERATIONAL LAND IMAGER FOR SHORELINE CHANGE DETECTION/INLAND WATER STUDIES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5/W1, 145-152, https://doi.org/10.5194/isprs-archives-XLII-5-W1-145-2017, 2017.

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