Determination of the Lukanga Swamps Flood Boundary using Landsat Imagery, Rainfall and Water Level Data

Alick R Mwanza^1, , Edwin Nyirenda² and Wilma Nchito³

¹Department of Geomatic Engineering, University of Zambia, Lusaka, Zambia

²Department of Civil and Environmental Engineering, University of Zambia, Lusaka, Zambia

³Department of Geography and Environmental Studies, University of Zambia, Lusaka, Zambia

Cite this paper

Alick R Mwanza, Edwin Nyirenda and Wilma Nchito. Determination of the Lukanga Swamps Flood Boundary using Landsat Imagery, Rainfall and Water Level Data. Journal of Geosciences and Geomatics. 2019; 7(5):237-244. doi: 10.12691/JGG-7-5-3

Abstract

The Lukanga Swamp is a major wetland situated in the Central Province of Zambia. It is Zambia’s fifth largest wetland whose flood boundary fluctuates with rainfall. Despite one of their many uses being that of flood control, they are no exceptions to this natural phenomenon - flooding. Hence, this study aimed at determining the most probable flood boundary of Lukanga swamps using Landsat images and rainfall data. Seasonal rainfall amounts received over the study area for the period 1972 – 2002, as well as the water level data of the swamp was used to determine wettest years as a means of selecting Landsat imagery which depicted flooding. Rainfall was determined by interpolating rainfall from adjacent meteorological stations as there is no such station in the study area. The selected Landsat imagery was used for delineation of the swamp’s likely maximum flood extent using Remote Sensing and GIS software. The most likely maximum flood extent was found to be 11,891 km² at peak flooding.

Keywords

Lukanga Swamps, Wetland, Landsat imagery, Geographical Information System, flood boundary, rainfall interpolation, image processing

Copyright

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References

[1]	Baker, C., R. Lawrence, C. Mantagne and D. Patten, 2006, Mapping Wetlands and Riparian Areas using Landsat ETM+ Imagery and Decision-Tree-Based Models, Wetlands, Vol.26, Issue 2, pp 465-474.
[2]	Balulescu, B. M. Buresin, L. Tecar and M. Herbei, 2013, Processing and Interpretation of Satellite Images – Landsat Research Journal of Agricultural Science, Vol. 45, Issue 4.
[3]	DWA, 2010, Water Levels and Flow Data, Department of Water Affairs, Ministry of Energy and Water Development, The Republic of Zambia.
[4]	Edward, K. A., G. A. Classen and E. H. J. Schroten, 1983, The Water Resource in Tropical Africa and its Exploitation, International Livestock Centre for Africa, Research Report No. 6, 11-12, Addis Ababa, Ethiopia.
[5]	Frazier, P. S. and K. J. Page, 2000, Water Body Detection And Delineation with Landsat TM Data, Photogrammetric Engineering & Remote Sensing, Vol. 66, Issue 12, pp 1461-1467.
[6]	Horning, N., 2004, Selecting the Appropriate Band Combination for a RGB Image using Landsat Imagery, Center for Biodiversity and Conservation, American Museum of Natural History, New York, USA, https://www.amnh.org/our-research/center-for-biodiversity-conservation/capacity-development/biodiversity- informatics/remote-sensing-guides/selecting-the-appropriate-band- combination-for-an-rgb-image-using-landsat-imagery accessed on 13/7/2017.
[7]	ILWIS User Guide, 1993, International Institute of Aerospace Survey and Earth Sciences, Enschede, The Netherlands.
[8]	Imhoff, M. L., C. Vermillion, M. H. Story, A. M. Choudhury and A. Gafoor, 1987, Monsoon Flood Boundary Delineation and Damage Assessment using Space borne Imaging Radar and Landsat, Photogrammetric Engineering and Remote Sensing, Vol. 53, pp. 405-413, NASA Technical Reports Server, http://ntrs.nasa.gov accessed on 27/01/2016.
[9]	Islam, M, P.S.Thenkbail, R. w. kulawardhana, R. Alankara, S. gunasinghe and C. Edussriya, 2008, Semi-Automated Methods for Mapping Wetlands using Landsat ETM+ and SRTM Data, International Journal of Remote Sensing, Vol. 29, Issue 24, pp7077-7106. www.tandfonline.com accessed 24/07/2017.
[10]	Kapangaziwiri, E., 2007, Revised Parameter Estimation Method for the Pitman Monthly Rainfall-Runoff Model, Msc Thesis, Rhodes University, Grahamstown, South Africa, https://www.researchgate.net/publication/29807276 accessed on 3/3/2010.
[11]	Kulawardhana, R.W., P. S. Thenkabail, J. Vithanage, C. Biradar, Md. A. Islam, S. Gunasinghe and R. Alankara, 2007, Evaluation of the Wetlands Mapping Methods using Landsat ETM+ and SRTM Data, Journal of Spatial Hydrology, Vol. 7, Issue 2, pp 62-96.
[12]	Lillesand, T. M., and R. W. Kiefer, 2000, Remote Sensing and Image Interpretation, 4th Edition, John Wiley & Sons, Inc.,U.S.A.
[13]	Ly, S., C. Charles and A. Degre, 2013, Different Methods for Spatial Interpolation of rainfall data for operational Hydrology and Hydrological Modelling at Watershed Scale: a Review, Society of Environment, Biotechnology and Agronomy, Vol. 17, http://popups.ulg.ac.be accessed on 27/01/2016.
[14]	Mair, A and A. Fares, 2011, Comparison of Rainfall Interpolation Methods in a Mountainous Region of a Tropical Island, Journal of Hydrologic Engineering, pp 371-383. www.ascelibrary.org accessed 10/04/2012.
[15]	McCartney, M., 2007, Technical note: Hydrology of the Lukanga Swamp, Zambia, Report on GEF Project on Wetlands in South Africa, http://publications.iwmi.org/pdf/H040529.pdf accessed on 13/11/2016.
[16]	Noori, M. J., H. H. Hassan and Y. T. Mustafa, 2014, Spatial Estimation of Rainfall Distribution and its Classification in Duhok Governorate using GIS, Jpurnal of Water Resource and Protection, Vol. 6, pp. 75-82,
[17]	OpenWeatherMap, 2016, Satellite Imagery: Landsat 8 and its Band Combinations, http://openweathermap.org/news/post/satellite-imagery-landsat-8-and-its-combinations accessed 13/7/2017.
[18]	Pradhan, B., 2009, Flood Susceptible Mapping and Risk Area Delineation Using Logistic Regression, GIS and Remote Sensing, Journal of Spatial Hydrology, Vol. 9, Issue 2, http://www.spatialhydrology.net/index.php/JOSH/article/view/81 accessed on 1/9/2017.
[19]	Sarun, S., P. Vineetha and R. Anil Kumar, 2016, Semi-Automated Methods for Wetlands Mapping using Landsat ETM+: a Case Study from Tsunami Affected Panchyats of Alapped and Arathpuzha, South Kerala, International Journal of Science and Research (IJSR), Vol. 5, Issue 8, pp 1150-1154.
[20]	Tao, T., B. Cho.cat, S. Liu and K. Xin, 2009, Uncertainty Analysis of Interpolation Methods in Rainfall Spatial Distribution – A Case Study of a Small Catchment in Lyon, Journal of Environmental Protection, Vol. 1 pp 50-58.
[21]	USGS, 2016, Landsat – Earth Observation Satellites, Fact Sheet 2015-3081, ver. 1.1, U.S. Department of the Interior, U.S. Geological Survey, https://pubs.usgs.gov/fs/2015/3081/fs2015-3081.pdf accessed 12/06/2017.
[22]	Wagner, P. D., P. Feiner, F, Wilken, S. Kumar and K. Schneider, 2012, Comparison and Evaluation of Spatial Interpolation Schemes for Daily Rainfall in Data Scarce Regions, Journal of Hydrology, Vol. 464-465, pp. 388-400.
[23]	ZMD, 1999, Zambia Rainfall Data, Zambia Meteorological Department, Ministry of Transport and Communication, The Republic of Zambia.