Open Access Open Access  Restricted Access Subscription Access

Water Body Mapping and Monitoring using Landsat Time Series Satellite Images

Afzal Ahmed, M. D. Kamrul Hasan, Eshrat Jahan Esha

Abstract


Bangladesh is believed to be extremely vulnerable to climate change, which may result in abnormal spatio-temporal pattern in rainfall and increased variability of temperature across the region. Consequently, the frequency and intensity of various natural hazards are expected to increase, which may affect the availability of fresh water on the surface as well as underground. That’s why mapping of water body and its continuous monitoring is important (Regional Water Report 37, FAO 2011).This study aims at identifying water body in the coastal belt of Bangladesh using Landsat 5 TM time-series satellite images for the year 2000, 2005 and 2010. Satellite derived indices e.g. WRI, NDVI, NDWI, MNDWI, AWEI, NDMI are computed from Landsat data of 2000, which were compared with the base map for selecting the best index for water body identification. The result shows that NDWI is more robust in extracting water bodies compared to other indices. Furthermore, unsupervised and supervised image classification techniques have been applied on all three years data. Both the index images as well as the classified images are reclassified to produce binary images showing water and non-water area. Average accuracy of the classification is 88%. Result shows that there is remarkable increase in water area after 2005. The reason might be attributed to the fact that the study area has suffered from several natural calamities during the study period.

Keywords: Water Security, Landsat Satellite Image, Remote Sensing Indices, Mapping and Monitoring


Full Text:

PDF

References


Regional Water Report 37, Food and Agricultural Organization 2011; http://www.fao.org/nr/water/aquastat/basins/gbm/index.stm

Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery, 4173–4189.

Sarp, G., & Ozcelik, M. (2016). Water body extraction and change detection using time series: Integrative Medicine Research.

Feyisa, G. L., Meilby, H., Fensholt, R., & Proud, S. R. (2014). Remote Sensing of Environment Automated Water Extraction Index : A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140, 23–35.

Fisher, A., Flood, N., & Danaher, T. (2016). Remote Sensing of Environment Comparing Landsat water index methods for automated water classi fi cation in eastern Australia. Remote Sensing of Environment, 175, 167–182.

Elsahabi, M., Negm, A., & Hamid M.H. El Tahan, A. (2016). Performances Evaluation of Surface Water Areas Extraction Techniques Using Landsat ETM+ Data: Case Study Aswan High Dam Lake (AHDL). Procedia Technology, 22, 1205–1212.

HAQUE, S. A. (2006). Review Article Salinity Problems And Crop Production in Coastal Regions of Bangladesh. Pak. J. Bot., 38(5): 1359-1365, 2006.

Mcgee, J., Campbell, J., & Parece, T. (2015). Remote Sensing in an ArcMap Environment.

Jeff C. H, Richard P. S., Thomas B. B., Anna M. M. (15 March 2017) Using Landsat to extend the historical record of lacustrine phytoplankton blooms: A Lake Erie case study. Remote Sensing of Environment. Volume 191, Pages 273–285

U.S. Geological Survey (USGS). (2012). Landsat — A Global Land-Imaging Mission. U.S. Geological Survey Fact Sheet 2012-3072

Graham, J. (2010). Lesson 2 : How to Bring Landsat Data into ArcGIS , Mosaic and Clip Scenes.


Refbacks

  • There are currently no refbacks.