Anzali Wetland Surface Area Evaluation Based on Landsat Time Series Data and NDWI Indices

Document Type : Research Paper


1 Department of Forestry, Natural Resources Faculty, Guilan University, Some Sara, Iran

2 of Forestry, Faculty of Natural Resources, University of Guilan, Somehsara, Iran

3 Department of Waste Proccessing, Environmental Research Institute, University Jihad of Gilan Province, Rasht, Iran


Wetland habitats are one of the most important natural ecosystems in the world. Evaluating and managing these valuable ecosystems require accurate and up-to-date data that remote sensing makes it possible. In this study, the changes of Anzali International Wetland in Gilan province, Iran, were investigated using Landsat satellite images and the Modified Nomalized Diffrence water index (MNDWI) in Google Earth Engine (GEE) platform during the years 1986 to 2020. To monitor waterbodies changes, two classes of water and non-water area were classifeied by Support Vector Machine (SVM) algorythm and MNDWI index was used to distinct the water surface areas. On the other hand, climate data including TRMM satellite data and PDSI index from TerraClimate data and Caspian Sea water level data were used to determine their effects on water level fluctuation of the wetland. The maps of SVM classification had overall accuracy more than 87% and Kappa coefficient was more than 88%. The wetland water body loss has decreased by 20% in its area according to MNDWI index maps, it has reached from 5926 hectares to 954 hectares, so that initially (until 2000) there was an upward trend and then a downward trend in the wetland water level. Also, the water level of Anzali wetland have been affected more by the sea level than the climatic factors. The results show that water indices and Google Earth Engine are efficient tools to identify the trends of water level changes of wetlands, and could provide more detailed scientific guidance to protect and manage natural resources in the studied areas.


Main Subjects



Wetlands play a critical role in the environment. With the impacts of climate change as one of the most important factors affecting the balance of wetlands, these natural ecosystems have suffered severe climatic and biophysical parameters which cause undergone many changes in their functions and structures. Therefor evaluating the surface water area in wetlands is a functional requirement for studying ecological and hydrological processes. The wetland restoration and management requires sufficiently frequent and high-spatial-resolution remote sensing data to represent its dynamics. This paper reviews the current status of detecting and evaluating surface water are using remote sensing data and index in Google Earth Engine(GEE).


Materials and methods

In this study, the Anzali International Wetland in Guilan province, Iran was selected as the study area and to identify the changes of the wetland, Landsat time series images and the normalized difference waterindex in Google Earth Engine were used. this study used the available 36 scenes Landsat TM, and OLI images in this region from 1986 to 2020 and processed the data on the (GEE) platform. Support Vector Machine (SVM) algorithms were applied to produse custom maps and two classes of water and non-water area of the wetland were classifeied in ENVI 5.6. The water index was used to quantify the spatiotemporal variability of the surface water area changes over the years and MNDWI maps were computed using the appropriate GEE function. Also climate data including TRMM satellite data and PDSI index from TerraClimate data and Caspian Sea water level data were used.



The results outline different aspects of the spatial and temporal distribution of the wetland water surface area. The produced maps had reasonable accuracy with the Support Vector Machine (SVM) algorithms. Overall accuracies based on the algorithms used ranged between 87% and 92% and Kappa coefficient between 88% and 94%. This level of accuracy is reasonable considering the area classified in this study. The results of comparing the MNDWI index with the Landsat classification confirm that the MNDWI index more accurately monitors the details of changes. The wetland water body loss has decreased by 20% in its area according to mNDWI index maps, so that initially (until 2000) there was an upward trend and then a downward trend. This confirms the process of drying and disappearing of the water areas. Also the water level changes of the wetland have been affected by the sea level on the trend more than climatic factors.



The analysis of changes in land use/land cover has always been a major theme in remote sensing techniques applied to Earth observation. The results demonstrated the capabilities of using the Google Earth Engine platform and water indices to characterize and identify the trends of the water level changes of wetlands with acceptable accuracy, and it is also applicable to wetland research in other regions. In particular, the approach proposed here, based on the use of SVM classification, appears to be quite effective and reliable (with an accuracy higher than 0.87) for the identification of changes in coastal wetland environment and MNDWI index in (GEE) was more accurate to provide water changes maps in the study period of time. The findings of this study underscore the relevance of GEE and Landsat composite data in evaluating and mapping coastal wetlands and could provide more detailed scientific guidance for wetland managers by quickly detecting wetland changes at a finer spatiotemporal resolution.


Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mohammad Javad Mirzadeh, S., & Hopkinson, C. (2019). Canadian wetland inventory using Google Earth engine: the first map and preliminary results. Remote Sensing, 11(7), 842.
Arévalo, P., Bullock, E. L., Woodcock, C. E. & Olausson, P. (2020). A Suite of Tools for Continuous Land Change Monitoring in Google Earth Engine. Frontier in Climate, 2, Article 56740.
Asghari Sarasekanrood, S.Jalilyan, R.Pirouzinejad, N.,  Madadi, A., &  Yadeghari, M. (2020). Evaluation of Water Extraction Indices Using Landsat Satellite Images (Case Study: Gamasiab River of Kermanshah). Journal of Geographical Sciences, 20(58), 53-70. (In Persian)
Ashoori, A.,  & Varasteh Moradi, H. (2014). Changes in the diversity of migratory wintering ‎waterbirds and Sub Waterbirds ‎in Anzali International Wetland. Wetland Eco Biology, 6(20), 55-66. ‎(In Persian)
Baatz, M., & Schape, A. (2000, July). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. In: Strobl, J., Blaschke, T. and Griesbner, G., Eds., Angewandte Geographische Informations-Verarbeitung, XII, Wichmann Verlag, Karlsruhe, Germany, 12-23.   
Banko, G. (1998). A review of assessing the accuracy of classifications of remotely sensed data and of methods including remote sensing data in forest inventory. IIASAI, International Institue for Applied Systems Analysis, A-2361, Austria: Laxenburg.
Benz, S.A., Bayer, P., & Blum, P. (2017). Identifying anthropogenic anomalies in air, surface, and groundwater temperatures in Germany. Science of the Total Environment, 584–585, 145–153.
Deblauwe, V., Droissart, V., Bose, R., Sonké, B., Blach‐ Overgaard, A., Svenning, J.C., Wieringa, J.J., Ramesh, B.R., Stévart, T., & Couvreur, T.L.P. (2016). Remotely sensed temperature and precipitation data improve species distribution modelling in the tropics. Global Ecology and Biogeography, 25‌(4), 443-454.
Dronova, I., Gong, P., & Wang, L. (2011). Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China. Remote Sensing of Environment, 115‌(12), 3220-3236.
Du, Z. Li. W., Zhou, D., Tian, L., Ling, F., & Wang, H. (2014). Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sensing Letters, 5(7), 672-681.
Ebrahimi, H., & Kardavani, P. (2014).   Recognitionthe Climate Change in International Anzali Wetland Using Mann-Kendal Test. Journal of wetland Eco biology, 6(21), 59-71. (In Persian)
Feng, L., Han, X., Hu, C., & Chen, X. (2016). Four decades of wetland changes of the largest freshwater lake in China: Possible linkage to the Three Gorges Dam? Remote Sensing of Environment, 176: 43-55.
Feyisa, GL., Meilby, H., Fensholt, R., & Proud S. R. (2014). Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment, 140: 23-35
 Ghorbani. R.,   Taghipour. A. A., &  Mahmoudzadeh H. 2013. Analysis and Evaluation of Land Use Changes in International Wetlands of Ala-Gol, Alma- Gol & Ajay-Gol In Turkaman Sahra, Using Multi-temporal Satellite Images. Geography And Environmental Planning, 23(4), 167-184. (In Persian)
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
Hajibigloo, M.,  Sheikhy, V. B.,  Memarian H.‌, & Komaki, CH. B. (2019). Determination of quantity and allocation disagreement indices in selection of appropriate algorithm for land use classification in pixel and objected base in Gorgarood river basin. Journal of RS and GIS for Natural Resurces, 10(4), 1-20. (In Persian) 
Hamzeh, S., Sedighi, A., & Falahaty, E. (2019, November). Investigating the Effects of of climate change on Shadegan wetland using remote sensing data. 6th International Regional Conference on Climate Change, Tehran, Iran. (In Persian)
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., Thau, D., Stehman, S.V., Goetz, S.J., & Loveland, T.R. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853.
Hejazizadeh, Z.,  Alijani, B.,  Zeaiean, P.,  Karimi M., &  Rafati, S. (2012). Evaluation of Satellite-based Precipitation Estimates (3B43) and Comparison with Kriging Interpolation Results. Iranian Journal of Remote Sencing & GIS, 4(3), 49-64. (In Persian)
Heydarian. K., Kaboodvandpor. Sh., & Amanollahi. J. (2016). Investigating of Zarivar International Wetland Depth Changes Using Remote Sensing and Artificial Neural Network Model‌. Geographic Space, 16(53), 979-981. (In Persian)
Hu, Y., & Dong. Y. (2018). An Automatic Approach for Land-change Detection and Land Updates Based on Integrated NDVI Timing Analysis and the CVAPS Method with GEE Support. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 347–359.
Jahani Shakib, F., Malekmohammadi, B., Yavari, A. R., Sharifi, Y., & Adeli, F. (2014). Assessment of the Trends of Land Use and Climate Changes in Choghakhor Wetland Landscape Emphasizing on Environmental Impacts. Journal of Environmental studies, 4(3), 631-643. (In Persian)
Javaheri Hoshi, F., ‌Yamani, M., & Jafar biglo, M. (2022). Analysis of the Impact of Caspian Sea Fluctuations on the Stability of the Coastline of Anzali Wetland‌. Quarterly journal of Environmental Erosion Research, 4(44), 35-51. (In Persian)
Javedankherad, E.,  Esmaeili Sari, A., & Bahramifar, N. (2011). Investigation of Persistent Organic Pollutants Residue in Sediments of International Anzali Wetland, Iran. Journal of Environmental studies, 37(57), 1-10. (In Persian)
Joshi, A.R., Dinerstein, E., Wikramanayake, E., Anderson, M.L., Olson, D., Jones, B.S., Seidensticker, J., Lumpkin, S., Hansen, M.C., & Sizer, N.C. (2016). Tracking changes and preventing loss in critical tiger habitat. Science advance, 2, e1501675.
Kharyaband, S., & Attarchi, S. (2020).  Evaluation of Anzali Wetland Depth Changes Using Satellite Images and Meteorological Data over Thirty Years. International Journal Of Remote Sensing & GIS, 12(2), 73-82. (In Persian)
Kheirollahi, M.,   Ghanian, M., & Farrokhy, F. (2013). Operation and Management Model based on Collaborative Mechanism of Design in Shadegan (the views of local stakeholders). Environmental Science, 1(11), 53-61. (In Persian)
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. Iranian Journal of Eco Hydrology, 7(1), 131-146. (In Persian)
Klemas, V. (2011). Remote sensing of wetlands: case studies comparing practical techniques. Journal of Coastal Research, 27 (3), 418-427.
Kumar, L., & Mutanga, O., (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509.
Landish, J., Koch, R., & Gray G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometers, 55, 159-174.
Lee, J. S. H., Wich, S., Widayati, A., & Koh, L. P. (2016). Detecting industrial oil palm plantations on Landsat images with Google Earth Engine. Remote Sensing Applications: Society and Environment, 4, 219–224.
Lee, T. M., & Yeh H. Ch. (2009). Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological Engineering, 35(4):487-496. 
Li, N., Li, L., Lu, D., Zhang, Y., & Wu, M. (2019). Detection of coastal wetland change in China: a case study in Hangzhou Bay. Wetlands Ecology and Management, 27‌(1), 103-124.
Liu, D., Chen, N., Zhang, X., Wang, C., & Du, W. (2020). Annual large-scale urban land mapping based on Landsat time series in Google Earth Engine and OpenStreetMap data: A case study in the middle Yangtze River basin. ISPRS J. Photogramm, 159, 337–351.
Lu D., Mausel, P., Brondizio, E., & Moran E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401.
Ludwig, C., Walli, A., Schleicher, C., Weichselbaum, J., & Riffler, M. (2019). A highly automated algorithm for wetland detection using multi-temporal optical satellite data. Remote Sensing of Environment, 224, 333-351.
Macleod, R. D., & Congalton, R. G. (1998). A Quantitative Comparison of Change-Detection Algorithms for Monitoring Eelgrass from Remotely Sensed Data. Photogrammetric Engineering and Remote Sensing, 64, 207-216.
Manandhar, S., Dev, S., Lee, Y. H., Winkler, S., & Meng, Y.S. (2018, July). Systematic study of weather variables for rainfall detection. IGARSS 2018- IEEE International Geoscience and Remote Sensing Symposium, 3027- 3030
McFeeters S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Jurnal of Remote Sensing Letters, 17(7), 1425-1432
Modaberi, H., & Shokoohi, A. R. (2019). Using Eco-Hydrologic Methods in Determining Anzali Wetland Environmental Water Requirement. Iran-Water Resources Research, 15(3), 91-104. (In Persian)
Moore, R., & Hansen, M. (2020). Google Earth Engine: A new cloud-computing platform for global-scale earth observation data and analysis. Available online: NASA/ADS.
Orimoloye, I. R., Kalumba, A. M, Mazinyo, S. P., & Nel, W. (2020). Geospatial analysis of wetland dynamics: wetland depletion and biodiversity conservation of Isimangaliso Wetland, South Africa. Journal of King Saud University-Science, 32(1): 90-96.
Rahimi Balouchi, L., & Hojjati, M. (2014, June). Investigating the Effects of Climate Change on Wetland Ecosystems Using Remote Sensing. The First Conference of Climate Change and Towards Sustainable Future, Tehran, Iran. (In Persian)
Salimi, Sh., Almuktar, S. A.A.A.N., & Scholz, M. (2021). Impact of climate change on wetland ecosystems: A critical review of experimental wetlands. Journal of Environmental Management, 286, 112160.
Salman mahini, A. R., & Kamyab, H. R. (2011). Remote sensing and applied geographic information systems in Idrisi, Tehran: Mehr Mahdis. (In Persian)
Shakeri, R., Shayesteh, K.‌, & Ghorbani, M. (2019). Assessment and prediction of land use changes in the Anzali wetland Basin, Based on Land Change Modeler (LCM). Iranian Remote Sensing & GIS journal, 11(2), 93-114. (In Persian)
Sidhu, N., Pebesma, E., & Câmara, G. (2018).  Using Google Earth Engine to detect land cover change: Singapore as a use case. European Journal of Remote Sensing, 1(1), 486-500.
Turner, K. T., Bergh, J., Barendregt, A., Straaten, J., & Maltby, E. (2020). Ecological-economic analysis of wetlands: scientific integration for management and policy. Ecological Economics, 35(1):7-23
Urkett, V., & Kusler, J. (2000). Climate change: Potential impact and interaction in wetlands of the united states. JAWRA Journal of the American Water Resources Association, 36 (2), 313-320. 8.
Wang, L., Diao, C., Xian, G., Yin, D., Lu, Y., Zou, S., & Erickson, T.A. (2020). A summary of the special issue on remote sensing of land change science with Google Earth Engine. Remote Sens. Environ. 248, 112002.
Wolff, D.B., Marks, D.A., Amitai, E., Silberstein, D.S., Fisher, B.L., Tokay, A., Wang. J., & Pippitt, J. L. (2005). Ground Validation for the Tropical Rainfall Measuring Mission (TRMM). Journal of Atmospheric and Oceanic Technology, 22(4):365-380.
Xia, H., Zhao, J., Qin. Y., Yang. J., Cui, Y., Song, H., Ma, L., Jin. N., & Meng, Q. (2019). Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sensing, 11(15), 1824.
Xu, H. (‌2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(‌14), 3025–3033.
Zebardast, L., & Jafari, H. (2011) Use of Remote Sensing in Monitoring the Trend of Changes of Anzali Wetland in Iran and Proposing Environmental Management Solution. Journal of Environmental studies, 37(57), 6457-5764. (In Persian)
Zhu, L., Liu, X., Wu, L., Tang, Y., & Meng, Y. (2019). LongTerm Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery. Remote Sensing, 11(10), 1234.