Detection of Water Level in Lakes using Satellite Images

S. Priyanka*, M. Narayana**
* PG Student, Department of Electronics and Communication Engineering, Jayaprakash Narayan College of Engineering, Telangana, India.
** Professor & Head, Department of Electronics and Communication Engineering, Jayaprakash Narayan College of Engineering, Telangana, India.
Periodicity:January - March'2017
DOI : https://doi.org/10.26634/jip.4.1.13521

Abstract

The objective is to estimate change in water level of lakes using satellite images. High resolution images provided by Landsat satellites are collected and passed through an image processing stage for calculating the lake properties such as Area and Perimeter. The images used in this paper are obtained from the USGS website. All the selected images are cloud free. In this paper, changes in water level are observed by calculating the areas and perimeters of four different lakes in two different times, using satellite images. These satellite images were acquired by Thematic Mapper on the Landsat5 and the Operational Land Imager (OLI) on Landsat 8. To detect the changes, image is classified into two classes, namely land and water. Thus, lake surface and surrounding ground were precisely separated. Then Perimeter (boundary) and Area of the lake are calculated. Area is computed by enumerating black colored pixels and Perimeter by enumerating edges of the lake, i.e. image gradient vector, gives the perimeter of the lake. The Area and Perimeter of the lake change as the water level changes.

Keywords

Image Gradient Vector, Lake, Landsat, Pixels, Satellite Images.

How to Cite this Article?

Priyanka, S., and Narayana, M. (2017). Detection of Water Level in Lakes using Satellite Images. i-manager’s Journal on Image Processing, 4(1), 23-32. https://doi.org/10.26634/jip.4.1.13521

References

[1]. Bryant, R. G., & Rainey, M. P. (2002). Investigation of flood inundation on playas within the Zone of Chotts, using a time-series of AVHRR. Remote Sensing of Environment, 82(2), 360-375.
[2]. Castañeda, C., Herrero, J., & Casterad, M. A. (2005). Landsat monitoring of playa-lakes in the Spanish Monegros desert. Journal of Arid Environments, 63(2), 497-516.
[3]. Collins, J. B., & Woodcock, C. E. (1996). An assessment of several linear change detection techniques for mapping forest mortality using multitemporal Landsat TM data. Remote Sensing of Environment, 56(1), 66-77.
[4]. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.
[5]. Coppin, P. R., & Bauer, M. E. (1996). Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3-4), 207-234.
[6]. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Review ArticleDigital change detection methods in ecosystem monitoring: A review. International Journal of remote sensing, 25(9), 1565-1596.
[7]. Duan, Z., & Bastiaanssen, W. G. M. (2013). Estimating water volume variations in lakes and reservoirs from four operational satellite altimetry databases and satellite imagery data. Remote Sensing of Environment, 134, 403- 416.
[8]. Gallego, F. J. (2004). Remote sensing and land cover area estimation. International Journal of Remote Sensing, 25(15), 3019-3047.
[9]. Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing prentice hall. Upper Saddle River, NJ.
[10]. Goodwin, N. R., Collett, L. J., Denham, R. J., Flood, N., & Tindall, D. (2013). Cloud and cloud shadow screening across Queensland, Australia: An automated method for Landsat TM/ETM+ time series. Remote Sensing of Environment, 134, 50-65.
[11]. Grey, W. M. F., Luckman, A. J., & Holland, D. (2003). Mapping urban change in the UK using satellite radar interferometry. Remote Sensing of Environment, 87(1), 16-22.
[12]. Griffiths, P., Hostert, P., Gruebner, O., & van der Linden, S. (2010). Mapping megacity growth with multisensor data. Remote Sensing of Environment, 114(2), 426-439.
[13]. Landsat Science. Landsat Mission by NASA. Retrieved from https://landsat.gsfc.nasa.gov
[14]. Langbein, W. B., & Iseri, K. T. (1960). General introduction and hydrologic definitions. US Government Printing Office: Washington, DC, USA.
[15]. Lunetta, R. S. E., & Christopher, D. (1999). Remote Sensing Change Detection: Environmental Monitoring Methods and Applications. Taylor & Francis, London.
[16]. Mas, J. F. (1999). Monitoring land-cover changes: a comparison of change detection techniques. International Journal of Remote Sensing, 20(1), 139-152.
[17]. National Research Council. (2008). Integrating Multiscale Observations of US Waters. National Academies Press.
[18]. United States Geological Survey (USGS)/Earth Resources Observation Center (EROS). Retrieved from http://glovis.usgs.gov
[19]. Verpoorter, C., Kutser, T., & Tranvik, L. (2012). Automated mapping of water bodies using Landsat multispectral data. Limnology and Oceanography: Methods, 10(12), 1037-1050.
[20]. Yang, X., & Lo, C. P. (2002). Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23(9), 1775- 1798.
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