Detecting the changes in Miqan lagoon zone by using NDWI, MNDWI, AWEI and supervised SVM models in the period of 1373 to 1401

Document Type : Original Article

Author

Department of Geography, Faculty of human science, University of Zanjan, Zanjan, Iran.

Abstract

Aim: Climate changes and mismanagement in recent years have caused the destruction of wetlands and lakes in large parts of the world, especially arid and semi-arid areas. In the central parts of Iran, this phenomenon is more evident and studies have shown that wetlands and lakes are drying up. Miqan lagoon is one of the cases that has faced a sharp decrease in water level. In this research, spatial and temporal changes of Miqan lagoon from 1373 to 1401 were evaluated using TM and OLI images of Landsat 5 and 8 satellites.
Material & Method: For this purpose, image data from SVM and NDWI, MNDWI and AWEI were used. The spectral and spatial performance of each classification was compared using Pearson's correlation.
Finding: In general, SVM model along with NDWI, MNDWI and AWEI indices achieved better results in terms of spectral and spatial quality. Based on the applied methods, the lake level shows a sharp decrease between 1373 and 1401. The results show the effectiveness of AWEI and MNDWI models in detecting water level changes in certain time intervals.
Conclusion: According to the AWEI model, the area of the lagoon has decreased from 112.6 square kilometers in 1373 to 78.5 square kilometers. The highest Pearson correlation between AWEI and MNDWI was observed in 1373, 1381, 1393 and 1401 with values of 0.93, 0.96, 0.97 and 0.97 respectively.
Innovation: So far, spectral indices and learning algorithm models have not been used in the investigation of Miqan wetland. In this research, new indices such as AWEI have been used along with the support vector machine model.

Keywords

References

Bagheri, M., Bagheri, A., & Sohooli, G. (2016). Analysis of changes in the Bakhtegan lake water body under the influence of natural and human factors, Iran- Water Resources Research, 12(37), 1-11. https://www.iwrr.ir/article_41333.html?lang=en [In Persian]
Boser, B., Guyon, I., & Vapnik, V. (1992). A training algorithm fotoptimal margin classifier, in: Proceedings of the Fifth Annual ACM Conference on Computational Learning Theory, Pittsburgh, 8, 144–152. DOI: 10.1145/130385.130401
Burges, C. (1998). A tutorial on support vector machines for pattern recognition, Data Mining Know. Discov. 2, 121–167. https://www.di.ens.fr/~mallat/papiers/svmtutorial.pdf
Cortes, C., & Vapnik, V. (1995). Support-vector network, Mach. Learn. 20: 273–297. http://image.diku.dk/imagecanon/material/cortes_vapnik95.pdf
Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to SupportVector Machines and Other Kernelbased Learning Methods,Cambridge University Press, 2000, http://dx.doi.org/10.1017/CBO9780511801389.
Dalponte, M., Bruzzone, L., & Gianelle, D. (2012). Tree species classifi-cation in the Southern Alps based on the fusion of very highgeometrical resolution multispectral/hyperspectral images andlidar data, Remote Sens. Environ. 123, 258–270. https://doi.org/10.1016/j.rse.2012.03.013
Davranche, A., Lefebvre, G., & Poulin, B. (2010). Wetland monitoring usingclassification trees and SPOT-5 seasonal time series, RemoteSens. Environ. 114, 552–562. https://doi.org/10.1016/j.rse.2009.10.009
Duan, Z., & Bastiaanssen, W. (2013). Estimating water volume vari-ations in lakes and reservoirs from four operational satellitealtimetry databases and satellite imagery data, Remote Sens.Environ. 134, 403–416. https://doi.org/10.1016/j.rse.2013.03.010
Feyisa, G., Meilby, H., Fensholt, R., & Proud, S. (2014). Automated Water Extraction Index: a new technique for surface water map-ping using Landsat imagery, Remote Sens. Environ. 140, 23–35. https://doi.org/10.1016/j.rse.2013.08.029
Foody, G., & Mathur, A. (2006). The use of small training sets containing mixed pixels for accurate hard image classification: training onmixed spectral responses for classification by a SVM, RemoteSens. Environ. 103, 179–189. https://doi.org/10.1016/j.rse.2006.04.001
Foody, G., Boyd, D., & Sanchez-Hernandez, C. (2007). Mapping a specific class with an ensemble of classifiers, Int. J. Remote Sens. 28, 1733–1746. https://doi.org/10.1080/01431160600962566
Frey, H., Huggel, C., Paul, F., & Haeberli, W. (2010). Automated detection ofglacier lakes based on remote sensing in view of assessing asso-ciated hazard potentials, Grazer Schriften Geogr. Raumforsch. 45, 261–272. DOI:10.5167/UZH-128917
Giardino, C., Bresciani, M., Villa, P., & Martinelli, A. (2010).  Application of remote sensing in water resource management: the casestudy of Lake Trasimeno, Italy, Water Resour. Manag. 24, 3885–3899. DOI: 10.1007/s11269-010-9639-3
Hannv, Z., Qigang, J., & Jiang, X. (2013). Coastline extraction using sup-port vector machine from remote sensing image, J. Multimed.8, 12-24. DOI:10.4304/jmm.8.2.175-182
Huang, C., Davis, L., & Townshend, J. (2002). An assessment of sup-port vector machines for land cover classification, Int. J. RemoteSens. 23, 725–749. https://doi.org/10.1080/01431160110040323
Khosravi, R., Hassanzadeh, R., Hossinjanizadeh, M., Mohammadi, S. (2020). Investigating Water Body Changes Using Remote Sensing Water Indices and Google Earth Engine: Case Study of Poldokhtar Wetlands, Lorestan Province, ECOHYDROLOGY, 7(1), 131- 146. 11. 10.22059/IJE.2020.295498.1265 [In Persian]
Maiti, S., & Bhattacharya, (2009). A. Shoreline change analysis and itsapplication to prediction: a remote sensing and statistics basedapproach, Mar. Geol. 257, 11–23. https://doi.org/10.1016/j.margeo.2008.10.006
McFeeters, S. (1996). The use of the normalized difference water index (NDWI) in the delineation of open water features, Int. J. RemoteSens. 17, 1425–1432. https://doi.org/10.1080/01431169608948714
Nath, R., & Deb, S. (2010). Water-body area extraction from high reso-lution satellite images – an introduction, review, and comparison,Int. J. Image Process. 3, 353–372. https://www.scirp.org/(S(lz5mqp453edsnp55rrgjct55.))/reference/referencespapers.aspx?referenceid=1527018
Ouma, Y., & Tateishi, R. (2006). A water index for rapid mapping of shore-line changes of five East African Rift Valley lakes: an empiricalanalysis using Landsat TM and ETM+ data, Int. J. Remote Sens.27, 3153–3181. https://doi.org/10.1080/01431160500309934
Pardo-Pascual, J., Almonacid-Caballer, J., Ruiz, L., & Palomar-Vázquez, J. (2012). Automatic extraction of shorelines from Landsat TMand ETM+ multi-temporal images with subpixel precision,Remote Sens. Environ. 123, 1–11. https://doi.org/10.1016/j.rse.2012.02.024
Poulin, B., Davranche, A., & Lefebvre, G. (2010). Ecological assessment ofPhragmites australis wetlands using multi-season SPOT-5 scenes,Remote Sens. Environ. 114, 1602–1609. https://doi.org/10.1016/j.rse.2010.02.014
Rezari Moghaddam, M., Mohammadzade, K., Pishnamaz Ahmadi, M. (2020). Investigating and comparing object-oriented algorithms used for extraction of water bodies from Sentinel imagery, Scientific- Research Quarterly of Geographical Data (SEPEHR), 29(115), 21- 34. 11. https://doi.org/10.22131/sepehr.2020.47878 [In Persian]
Rogers, A., & Kearney, M. (2004). Reducing signature variability inunmixing coastal marsh Thematic Mapper scenes using spectralindices, Int. J. Remote Sens. 25, 2317–2335. https://doi.org/10.1080/01431160310001618103
Rokni, K., Ahmad, A., Selamat, A., & Rokni, S. (2014). Water featureextraction and change detection using multitemporal landsatimagery, Remote Sens. 6, 4173–4189. https://doi.org/10.3390/rs6054173
Verpoorter, C., Kutser, T., & Tranvik, L. (2012). Automated mapping of waterbodies using Landsat multispectral data, Limnol. Oceanogr. -Methods 10, 1037–1050.  https://doi.org/10.4319/lom.2012.10.1037
Wang, S., Baig, M., Zhang, L., Jiang, H., Ji, Y., Zhao, H., & Tian, J.  (2015). A Simple Enhanced Water Index (EWI) for percent surfacewater estimation using Landsat data, IEEE J. Sel. Top. Appl. EarthObserv. Remote Sens. 8, 90–97. https://ieeexplore.ieee.org/document/7027774
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensedimagery, Int. J. Remote Sens. 27, 3025–3033. https://doi.org/10.1080/01431160600589179
Yousefiroshan, M. (2022). Estimation of lake Urmia water area using Landsat 8 satellite imagery using MNDWI index, Journal of Geography, 20(74), 165- 186. 11. http://dor.net/dor/20.1001.1.27833739.1401.20.74.9.9  [In Persian]
Journal of Arid Regions Geographic Studies
Volume 14, Issue 54 - Serial Number 54
February 2024
Pages 119-104

Files

Share

How to cite

Statistics