Volume XLI-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 607-610, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-607-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 607-610, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-607-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jun 2016

21 Jun 2016

TRAJECTORY-BASED ANALYSIS OF URBAN LAND-COVER CHANGE DETECTION

Y. H. Zhang1,2,3 and H. P. Liu1,2,3 Y. H. Zhang and H. P. Liu
  • 1State key laboratory of remote sensing science, Beijing, China
  • 2Beijing key laboratory of environmental remote sensing and digital city, Beijing, China
  • 3School of Geography, Beijing Normal University, Beijing , China

Keywords: change trajectory, similarity index, long time series, Landsat

Abstract. China have occurred unprecedented urban growth over the last two decades. It is reported that the level of China’s urbanization increased from 18 % in 1978 to 41 % in 2003, and this figure may exceed 65 % by 2050. The change detection of long time serious remote sensing images is the effective way to acquire the data of urban land-cover change to understand the pattern of urbanization. In this paper, we proposed the similarity index (SI) and apply it in long time series urban land-cover change detection. First of all, we built possible change trajectories in four times based on the normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) that extracted from time series Landsat images. Secondly, we applied SI in similarity comparison between the observed change trajectory and the possible trajectories. Lastly, verifying the accuracy of the results. The overall accuracy in four periods is 85.7 % and the overall accuracy of each two years is about 90 % and kappa statistic is about 0.85. The results show that this method is effective for time series land-cover change detection.