Volume IV-1/W1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 83-89, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-83-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1/W1, 83-89, 2017
https://doi.org/10.5194/isprs-annals-IV-1-W1-83-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  30 May 2017

30 May 2017

LANDSAT BIG DATA ANALYSIS FOR DETECTING LONG-TERM WATER QUALITY CHANGES: A CASE STUDY IN THE HAN RIVER, SOUTH KOREA

J. C. Seong1, C. S. Hwang2, R. Gibbs1, K. Roh1, M. R. Mehdi3, C. Oh4, and J. J. Jeong5 J. C. Seong et al.
  • 1Dept. of Geosciences, University of West Georgia, Carrollton, Georgia, USA
  • 2Dept. of Geography, Kyunghee University, Seoul, South Korea
  • 3NED University of Engineering & Technology, Karachi, Pakistan
  • 4Dept. of GIS Engineering, NamSeoul University, CheonAn, South Korea
  • 5Dept. of Geography, SungShin Women's University, Seoul, South Korea

Keywords: Big data, Landsat, Han River, reflectance, water quality, remote sensing

Abstract. Landsat imagery satisfies the characteristics of big data because of its massive data archive since 1972, continuous temporal updates, and various spatial resolutions from different sensors. As a case study of Landsat big data analysis, a total of 776 Landsat scenes were analyzed that cover a part of the Han River in South Korea. A total of eleven sample datasets was taken at the upstream, mid-stream and downstream along the Han River. This research aimed at analyzing locational variance of reflectance, analyzing seasonal difference, finding long-term changes, and modeling algal amount change. There were distinctive reflectance differences among the downstream, mid-stream and upstream areas. Red, green, blue and near-infrared reflectance values decreased significantly toward the upstream. Results also showed that reflectance values are significantly associated with the seasonal factor. In the case of long-term trends, reflectance values have slightly increased in the downstream, while decreased slightly in the mid-stream and upstream. The modeling of chlorophyll-a and Secchi disk depth imply that water clarity has decreased over time while chlorophyll-a amounts have decreased. The decreasing water clarity seems to be attributed to other reasons than chlorophyll-a.