An evaluation of Miqan wetland changes over a 12-year interval and proposing management approaches: A remote-sensing Prespective

Document Type: Research Paper


1 Payame Noor University, Tehran, Iran

2 Department of Natural Resources and Environment, Payame Noor University, Tehran, Iran

3 Environment Protection Organization, Tehran, Iran


     Miqan desert wetland is one of the 10 major wetlands in Iran. In 2008, it has changed into a non-hunting area due to its fragile ecology.  Evaluating the change trends of the wetland and the use of its surrounding lands is of high significant in order to maintain the wetlands. In this regard, the satellite images of the ETM+ and OLI sensors of years 2002 and 2013 were initially collected. After performing atmospheric and geometrical correction operation through using the instructed samples derived from Maximum Likelihood Classification Method, they were classified into five different classes namely wetlands, barren lands, grasslands, dry-land farming and irrigated farming. The results indicate the overall accuracy of 80.20 and 84.91 for image classification in 2002 and 2013, respectively.The most significant changes observed were an increase of water level water from 7784.57 hectares in 2002 to 7887.35 hectares in 2013 for wetlands; however, the long-term average annual rainfall in recent years shows a 26-percent reduction. Accordingly, the reason of this 0.26-percent increase of the wetland water level can be attributed to the flow of water from the wastewater treatment center. An increase 20% in the dry land farming, and the 12% reduction of grasslands, 0.31% irrigated farming as well as 8.27% barren lands. The main reasons for such changes include setting a sewage refinery in one of the wetland entrances, conversion of grasslands into dry land farming, recent droughts, and growing salinity-tolerant plants such as Nitraria Schoberi. 


Avis, C.A., A. Weaver, K. Meissner, 2011. Reduction in areal extent of high-latitude wetlands in response to permafrost thaw. Nature Geoscience, 4(7); 444-448. DOI: 10.1038/ ngeo1160.

Bargiel, D, 2013. Capabilities of high resolution satellite radar for the detection of semi-natural habitat structures and grasslands in agricultural landscapes. Ecological Informatics, 13; 9–16.

Christopher, A, 2001. Remote sensing and geographic information systems. Translated by Farhatjah B., Armed Forces Geographical Organization Publication center: Tehran; p. 42.

Imam, E, 2011. Use of geospatial technology in evaluating landscape cover type changes in Chandoli National Park, India Use of geospatial technology in evaluating landscape cover type changes in Chandoli National Park, India, Computational Ecology and Software, 1(2); 95-111.

Jensen, J.R. 1996. Introductory digital image processing. New Jersey, USA.

Kauth, R.J., G.S. Thomas, 1976. The tasseled cap - A graphic description of the spectral temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Perdue University, West Lafayette, Indiana, p.  41-51.

Koghan Ndzeidze, S, 2008. For the degree of Master of Sciene in Geography presented on August, 25; 225-235.

Koh, C.N., P.F. Lee, R.S. Lin, 2006. Bird species richness patterns of northern Taiwan: primary productivity, human population density, and habitat heterogeneity. Diversity & Distributions, 12; 546–554.

Lausch, A., F. Herzog, 2002. Applicability of landscape metrics for the monitoringlandscape change: issues of scale, resolution and interpretability. Ecological Indicator, 2; 3-15.

Ma, C., G.Y. Zhang, X.C. Zhang, Y.J. Zhao, H.Y. Li, 2012. Application of Markov model in wetland change dynamics. In: Tianjin Coastal Area, China. 18th Biennial ISEM Conference on Ecological Modelling for Global Change and Coupled Human and Natural System. PP.  252–262.

Macleod, R.S., R.G. Congalton, 1998. A Quantitative Comparison of Change Detection Algorithms for Monitoring Eelegrass from Remotely, 4; 2-52.

Mohsen, A, 1999. Remote Sensing and Image Interpretation. John Wiley Sons Inc, USA.

Ozesmi, S.L., E.M.  Bauer, 2002. Satellite remote sensing of wetlands. Wetlands Ecology and Management, 10; 381-402.

Sanchez1, A., D. Abdul Malak1, A. Guelmami, C. Perennou, 2015. Development of an Indicator to Monitor Mediterranean Wetlands. PLoS ONE 10(3): 22-694. doi:10.1371/journal.pone.0122694.

Shim, D, 2014. Remote sensing place: Satellite images asvisual spatial imaginaries. Geoforum, 51; 152–160.

Shirdeli, A, 2014. Hydropolitics and hydrology issues in Hirmand/Helmand international river basin. Management Science Letters, 4; 807–812.

Szuster, B.W., Q. Chen, M. Borger, 2011. A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Applied Geography, 31; 525–532.

Tigges, J., T. Lakes, P. Hostert, 2013. Urban vegetation classification: Benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment, 136; 66-75.

Tucker, C.J. 1979. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment, 8(2); 127-150.

Ulbricht, k.A., W.D. Heckendorff, 1998. Satellite image for recognition of landuschanges. Photogrammetry and remote sensing, 53; 235-244.

Wang, Y., D. Zhou, Y. Sun, 2011. Assessment of the ecological health of wetlands in Honghe supported by RS and GIS techniques. Acta Ecologica Sinica, 31(13); 3590-3602.

Yousefi, S., S. Mirzaee, M. Tazehc, H. Pourghasemi, H. Karimi, 2015. Comparison of different algorithms for land use mapping in dry climate using satellite images: a case study of the Central regions of Iran. Desert, 20-1; 1-10.

Yuan, D., C. Elvidge, 1998. NALC LandCover Change Detection Pilot Study: Washington D.C Area Experiments. Remote Sensing ofEnvironment, 66; 166-178.