Monitoring seasonal changes of Meighan wetland using SAR, thermal and optical remote sensing data

Document Type : Full length article


Department of Remote sensing and GIS, Faculty of Geography, University of Tehran


Monitoring seasonal changes of Meighan wetland using SAR, thermal and optical remote sensing images
The aim of this study is to monitor the seasonal changes of Meighan wetland located in Markazi province in Iran. This is a multi-sensor approach; Sentinel-1 and Landsat 8 images were captured from May 2019 to January 2020. Modified Normalized Difference Water Index (MNDWI) and Land surface temperature were computed based on spectral bands of Landsat 8. Backscattering values in VH and VV polarimetric bands of Sentinel 1 images were also considered. Different wetland land cover classes were extracted based on these three measures. The results of each season were further compared with the classification output with support vector machines. The wetland main water body reaches its maximum extent in May 2019 (61.18 square kilometers) and its minimum extent is reported in August 2019 with an extent of 19.25 square kilometers. The outputs of the support vector machine classification were more compatible with MNDWI index. The results of this study show that the multi-sensor approach can efficiently be used in monitoring seasonal changes of wetland.
Wetlands are one of the natural ecosystems that play an important role in plant and animal diversity conservation. Wetlands are very sensitive to environmental changes because they are located in an intermediate zone between land and marine ecosystems. Their constant monitoring is of great importance especially in wetlands with seasonal changes pattern. The Wetland ecosystems are influenced by anthropogenic and natural factors. Drought, reduced rainfall, unsustainable management of water resources, overexploitation, and dam construction threaten wetlands. Field surveying and mapping of natural resources are generally not cost-effective because these methods are expensive and time-consuming. Also, it is not possible to repeat it periodically with a constant interval. Therefore, the use of remote sensing data such as optics and radar data is necessary in the study of natural resources. However, natural landscapes are complex and composed of various land cover types. Optical multispectral images are not always able to classify such a landscape, perfectly. This source of data is also affected by atmospheric conditions; the presence of clouds or fog block capturing these images. SAR sensors unlike optics sensors are capable of capturing images in all weather conditions. In fact, the use of each satellite image has advantages and disadvantages and in many applications they complement each other. Multi-sensor approaches beneficiate from the capabilities of different satellite images. Researches have shown that a multi-sensor approach in natural resources studies, especially wetlands is of great value. The multi-source approach and the seasonal variations discussed in this study have not been followed in any research on Meighan wetland. The benefits of Sentinel-1 characteristics; such as suitable spatial and radiometric resolutions and free access highlight the finding of this research.
Materials and methods
Meighan wetland is located in the center of Iran in Markazi province. This wetland has ecological and economical importance in the region. In the last two decades, one road is constructed on it and divided it into two parts; this changes the wetland into a calm environment and subsequently the evaporation has been increased. In this study, the seasonal changes of Meighan wetland were investigated using Landsat 8 and Sentinel-1 images. The images in each season were selected in such a way that the minimum possible difference exist between their acquisition date. The preprocessing steps were done independently on each optic and SAR image. Sentinel-1 SAR images have been calibrated and the digital numbers were converted into the corresponding backscattering values (in decibel) in each polarimetric band. Although, from spectral reflectance values in different Landsat bands, Modified Normalized Difference Water Index (MNDWI) were calculated in each season. Land surface temperatures were also calculated from thermal bands. Five different land cover classes are observed in the wetland and its surroundings; main water body of the wetland, shallow water zone, saline soil, surrounding area and remaining land covers (known as others). These areas were also extracted based on MNDWI index, land surface temperature (LST) and backscattering values in VH and VV sentinel-1 polarimetric bands. Then, the whole area is classified by the support vector machine classifier. In the last step, the extracted regions from different methods were compared with the land cover classification results in each season. The differences and similarities of the extracted areas were discussed further.
Results and discussion
The findings of this study show that the main wetland body reaches its maximum extent in May 2019 based on the SVM classification results. In this month, MNDWI index-based results were closer to the one obtained with the support vector machine classification. The support vector machine classification results and MNDWI index achieved similar results in the delineation of the wetland water zone, the shallow water zone and saline soil. In August 2019, the wetland water area was reduced based on the support vector machine classification. In May 2019 and January 2020, when the wetland water area was larger in comparison to other months, the results of the MNDWI index are close to the results of the support vector machine classification. The extracted area of shallow water class and saline soil class show the highest difference between classification results and MNDWI results. The same results have been obtained by comparison of extracted area based on the backscattering values of VH and VV polarimetric bands and MNDWI index; the maximum differences are observed in shallow water and saline soil classes. This could be related to the sensitivity of SAR backscattering values to moisture content. Over the year, the moisture content varies in response to temperature, rainfall, and evapotranspiration. The changes in moisture content affect the dielectric constant of the material. The dielectric constant governs the magnitude of backscattering values. The moisture changes cause variation in SAR backscattering values over the year.
Long-term wetland change detection is frequently studied with optical remote sensing images. Although, wetlands show the seasonal pattern in response to temperature and rainfall changes over the year, however, wetland seasonal variations are not fully explored. In this study, Sentinel 1 and Landsat8 images covering the study area were captured over the year. The results of the present study showed that the seasonal variation of wetland can be monitored based on a multi-sensor approach. In May 2019, the Meighan main water body reached the highest extent and the smallest area was observed in August 2019. In addition, in January 2020, the wetland water area increased again. Also some differences are observed between the extracted areas based on the MNDWI index, VH and VV polarizations, and the support vector machine classification results in different seasons. These differences are observed more in the spring. The performance of MNDWI index in wetland water area extraction in most seasons is very close to the classification results of the support vector machine. This shows the high capabilities of MNDWI spectral index in monitoring wetlands. In addition, the main water body of the wetland can be well separated by backscattering values of VH and VV Sentinel 1 polarimetric bands.
Land surface temperature, Remote Sensing, Spectral index, Synthetic Aperture Radar images, Wetland


Main Subjects

ابراهیمی خوسفی، ز.؛ خسروشاهی، م.؛ نعیمی، م. و زندی‏فر، س. (1398). ارزیابی و پایش تغییرات رطوبت تالاب میقان با استفاده از تکنیک دورسنجی و ارتباط آن با شاخص‏های خشک‏سالی هواشناسی، سنجش‏ازدور و سامانة اطلاعات جغرافیایی در منابع طبیعی، (2)10: ۱-14.
اصغری سراسکانرود، ص.؛ جلیلیان، ر.؛ پیروزی‏نژاد، ن.؛ مددی، ع. و یادگاری، م. (۱۳۹۹). ارزیابی شاخص‏های استخراج آب با استفاده از تصاویر ماهواره‏ای لندست (مطالعة موردی: رودخانة گاماسیاب کرمانشاه)، فصل‏نامة تحقیقات کاربردی علوم جغرافیایی، ۲۰(۵۸): ۵۳-۷۰.
امیری، ا.؛ عبدالهی کاکرودی، ع ا. و قدیمی، م. (۱۳۹۸). آشکارسازی خطواره‏های مرتبط با گسل دهشیر با داده‏های سنجش‏ازدور اپتیک و رادار، فصل‏نامة علوم و فنون نقشه‏برداری، ۹(۲): ۵۱-64.
انصاری، ا. (1397). ارزیابی و شناخت وضعیت محیط‏ زیست تالاب میقان اراک جهت تدوین برنامة توسعة پایدار، پژوهش‏های محیط ‏زیست، 9(17): ۲۹-42.
رنجبر، ص. و آخوندزاده هنزائی، م. (1398). برآورد رطوبت سطح خاک با استفاده از روش‏های SVR و ANN در تصاویر ماهواره‏های سنتینل 1 و 2، مهندسی فناوری اطلاعات مکانی، 7(4): ۲۱۵-232.
شکری، م. و صاحبی، م.ر. (۱۳۹۶). تلفیق تصاویر رادار با روزنة مجازی و اپتیک با استفاده از تبدیل کرولت، نشریة علمی‏- پژوهشی علوم و فنون نقشه‏برداری، ۷(۲): ۱۲۷-138.
قهرودی تالی، م.؛ میرزاخانی، ب. و عسگری، آ. (۱۳۹۱). پدیدة کویرهایی در تالاب‏های ایران (مطالعة موردی: تالاب میقان)، نشریة جغرافیا و مخاطرات محیطی، ۱(۴): ۹۷-112.
محمودی، س.؛ ساری صراف، ب.؛ رضایی بنفشه، م. و رستم‏زاده، ه. (1398). تأثیر تغییرات محیطی تالاب میقان بر دمای سطح زمین نواحی پیرامونی با استفاده از داده‏های ماهوارة لندست، سنجش‏ازدور و سامانة اطلاعات جغرافیایی در منابع طبیعی، (3)10: ۱-18.
ملکی، م.؛ توکلی صبور، س.م.؛ ضیائیان فیروزآبادی، پ. و رئیسی، م .(1397). مقایسة داده‏های اپتیک و رادار در استخراج عوارض و پدیده‏های زمینی، سنجش‏ازدور و سامانة اطلاعات جغرافیایی در منابع طبیعی، (2)9: 93-107.
میرعلیزاده فرد، س. و منصوری، ش. (1398). ارزیابی شاخص‏های سنجش‏ازدور در مطالعات کمی و کیفی آب‏های سطحی با تصاویر ماهواره‏ای لندست- 8 (مطالعة موردی: جنوب استان خوزستان)، سنجش‏ازدور و سامانة اطلاعات جغرافیایی در منابع طبیعی (کاربرد سنجش‏ازدور و GIS در علوم منابع طبیعی)، 10(2 (پیاپی 35): 63-84.
نجفی، ا.؛ عزیزی قلاتی، س. و مختاری، م.ح. (۱۳۹۶). کاربرد ماشین بردار پشتیبان در طبقه‏بندی کاربری اراضی حوزة چشمة کیله- چالکرود، پژوهشنامة مدیریت حوزة آبخیز، ۸(۱۵): ۹۲-101.
Amani, M.; Salehi, B.; Mahdavi, S. and Brisco, B. (2019). Separability analysis of wetlands in Canada using multi-source SAR data. GIScience & Remote Sensing, 56(8): 1233-1260.
Amiri, A.; Abdollahi Kakroodi, A. and Ghadimi, M. (2019). Detection Dehshir Fault Lineaments Using Radar and Optical Remote Sensing Data, Journal of Geomatics Science and Technology, 9(2): 51-64. 
Ansari, A. (2018). Recognition and Evaluation of the Environmental Status of Meighan Wetland and Planning for a Sustainable Development, Environmental Research, 9(17): 29-42.
Asghari, S.; Jalilyan, R.; Pirozineghad, N.; Madadi, A. and Yadeghari, M. (2020). Evaluation of Water Extraction Indices Using Landsat Satellite Images (Case Study: Gamasiab River of Kermanshah), Journal of Applied Researches in Geographical Sciences, 20(58): 53-70.
 Burges, C.J. (1998). A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2): 121-167.‏
Ebrahimikhusfi, Z.; Khosroshahi, M.; Naeimi, M. and Zandifar, S. (2019). Evaluating and monitoring of moisture variations in Meyghan wetland using the remote sensing technique and the relation to the meteorological drought indices, Journal of RS and GIS for Natural Resources, 10(2): 1-14.
Gautam, V. K.; Gaurav.P. K.; Murugan, P. and Annadurai, M. J.A.P. (2015). Assessment of surface water Dynamicsin Bangalore using WRI, NDWI, MNDWI, supervised classification and KT transformation. Aquatic Procedia, 4: 739-746.‏
Ghahroudi Tali, M.; Mirzakhani, B. and Asgari, A. (2013). Desertification and Playa Expansions in Everglades of Iran (Case Study: Meghan Lake), Journal of Geography and Environmental Hazards, 1(4): 97-112. 
Guo, M.; Li, J.; Sheng, C.; Xu, J. and Wu, L. (2017). A review of wetland remote sensing. Sensors, 17(4): 777.‏
Kaplan, G.; Avdan, Z. Y. and Avdan, U. (2019). Mapping and monitoring wetland dynamics using thermal, optical, and SAR remote sensing data. Wetlands Management: Assessing Risk and Sustainable Solutions, 23-87.‏
Liu, Y. and Xiao, C.C. (2020). Water Extraction on the Hyperspectral Images of GAOFEN-5 Satellite Using Spectral Indices. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43: 441-446.‏
Ma, S.; Zhou, Y.; Gowda, P. H.; Dong, J.; Zhang, G.; Kakani, V.G.; Wagle, P.; Chen, L.; Flynn, K.C. and Jiang, W. (2019). Application of the water-related spectral reflectance indices: A review. Ecological Indicators, 98: 68-79.
Mahmoodi, S.; Rostamzade, H.; Sari, B. and Rezaei, M. (2019). The effect of Meighan wetland environmental changes on land surface temperature of surrounding areas by using Landsat satellite data, Journal of RS and GIS for natural Resources, 10(3): 1-18. 
Maleki, M.; Tavakkoli Sabour, S.; Zeaieanfirouzabadi, P. and Raeisi, M. (2018). Comparison of optic and radar data for terrain feature extraction, Journal of RS and GIS for Natural Resources, 9(2): 93-107.
Maleki, S.; Baghdadi, N.; Soffianian, A.; El Hajj, M. and Rahdari, V. (2020). Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland. International Journal of Remote Sensing, 41(6): 2277-2302.
McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of remote sensing, 17(7): 1425-1432.‏
Mir Alizadehfard, S. and Mansouri, S. (2019). Evaluation of indicators of remote sensing measurement in quantitative and qualitative studies of surface water with Landsat-8 satellite images (Case study: South of Khuzestan province). Journal of RS and GIS for Natural Resources, 10(2): 63-84.
Mokhtari, M. H. and Najafi, A. (2015). Support vector machine and artificial neural network classification methods of land use extraction of satellite images Landsat. Journal of technology of agriculture and natural resources, water and soil sciences, 19: 72-35.‏
Najafi, A.; Azizi Ghalati, S. and Mokhtari, M.H. (2017). Assessment Kernel Support Vector Machines in Classification of Land uses (Case Study: Basin of Cheshmeh kileh-Chalkrod), Journal of Watershed Management Research, 8(15): 92-101.
Pohl, C. and Van Genderen, J. L. (1998). Review article multi-sensor image fusion in remote sensing: concepts, methods and applications, International journal of remote sensing, 19(5): 823-854.‏
Ranjbar, S. and Akhundzadeh Hanzaei, M. (2019). Estimation of soil surface moisture using SVR and ANN methods in Sentinel 1 and 2 satellite images. Spatial Information Technology Engineering, 7(4): 215-232.
Shokri, M. and Sahebi, M. R. (2017). Fusion of Synthetic Aperture Radar Data and Optic Images based on Curvelet Transform, Journal of Geomatics Science and Technology, 7(2): 127-138. 
Slagter, B.; Tsendbazar, N. E.; Vollrath, A. and Reiche, J. (2020). Mapping wetland characteristics using temporally dense Sentinel-1 and Sentinel-2 data: A case study in the St. Lucia wetlands, South Africa. International Journal of Applied Earth Observation and Geoinformation, 86, 102009.‏
Xu, H. (2006). Modification of Normalized Difference Water Index (NDWI) to enhance open water features in remotely sensed imagery. International journal of remote sensing, 27(14): 3025-3033.‏
Zhang, W.; Hu, B. and Brown, G. S. (2020). Automatic Surface Water Mapping Using Polarimetric SAR Data for Long-Term Change Detection. Water, 12(3): 872.‏
Zhu, C.; Zhang, X. and Huang, Q. (2018). Four decades of estuarine wetland changes in the Yellow River delta based on Landsat observations between 1973 and 2013. Water, 10(7): 933.‏
Zhu, W.; Jia, S. and Lv, A. (2014). Monitoring the fluctuation of Lake Qinghai using multi-source remote sensing data. Remote Sensing, 6(11): 10457-10482.‏
Volume 53, Issue 3
December 2021
Pages 365-380
  • Receive Date: 25 April 2021
  • Revise Date: 30 August 2021
  • Accept Date: 01 September 2021
  • First Publish Date: 12 September 2021