پایش تغییرات مکانی غلظت رسوب معلق (SCC) با کاربرد مدل‌های رگرسیونی خطی و غیرخطی اطلاعات طیفی ماهواره‌ای در رودخانه سفیدرود در شمال ایران
محمد رضا سلامی
1
,
ابراهیم فتائی
2
,
فاطمه ناصحی
3
,
بهنام خانی زاده
4
,
حسین سعادتی
5
1 - دانشجوی دکتری رشته علوم و مهندسی محیط زیست، گروه علوم و مهندسی محیط زیست، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران.
2 - استاد گروه علوم و مهندسی محیط زیست، واحد اردبیل، دانشگاه آزاد اسلامی، اردبیل، ایران.
3 - استاد گروه علوم و صنایع چوب و كاغذ، واحد كرج، دانشگاه آزاد اسلامی، كرج، ایران.
4 - استادیار گروه شیمی، واحد سراب، دانشگاه آزاد اسلامی، سراب، ایران.
5 - استاد گروه علوم و صنایع چوب و كاغذ، واحد كرج، دانشگاه آزاد اسلامی، كرج، ایران.
الکلمات المفتاحية: سفیدرود, غلظت رسوب معلق, لندست 8, نسبت باندی B4/B3, TSM.,
ملخص المقالة :
سفیدرود یکی از پرآب‌ترین رودخانه‌های شمال ایران است که نقش بسیار مهمی در تولیدات کشارزی، دامی، شیلات و تامین انرژی برقآبی استان گیلان دارد. در پژوهش حاضر طی دوره سال 2020-2013، با استفاده داده‌های نمونه‌برداری چهار ایستگاه رسوب‌سنجی بر روی رودخانه سفیدرود و همچنین تصاویر ماهواره‌ای لندست 8، به پایش تغییرات غلظت رسوب معلق (SCC) پرداخته شد. برای این منظور روابط رگرسیون چندگانه خطی بازتاب طیفی 7 تک باند و 21 نسبت باندی با SCC مشاهداتی و همچنین رگرسیون‌های خطی ساده، لگاریتمی، توانی و نمایی شاخص TSM با SCC مورد بررسی قرار گرفت و از بین مدل‌های رگرسیونی، مدلی که دارای بیشترین R2 با SCC بود، به عنوان مناسب‌ترین مدل برای تهیه نقشه تغییرات مکانی SCC استفاده شد. نتایج نشان داد که شاخص TSM (نسبت B4/B3) با SCC مشاهداتی دارای بیشترین همبستگی بوده، به طوری که مقدار R2 رابطه نمایی TSM با SCC مشاهداتی 74/0 می‌باشد. در ادامه با استفاده از مدل نمایی مذکور، نقشه تغییرات مکانی SCC تهیه شد و تغییرات SCC در طول بازهای رودخانه مورد بررسی قرار گرفت. نتایج نشان داد که مقدار SCC در دو سرشاخه سفیدرود (قزل‌اوزن و شاهرود) بیشتر است، اما پس ورود این رودخانه‌ها به مخزن سد منجیل (سفیدرود) مقادیر SCC در داخل مخزن به سبب ته نشین شدن SCC ;کاهش یافته و مقادیر آن در پایین دست مخزن در طول رودخانه سفیدرود نیز نسبت به سرشاخه‌ها کمتر است. یافته‌ها حاکی از آن است که از بین دو سر شاخه سفیدرود، رودخانه قزل‌اوزن با مقدار SCC بیشتر، نقش بیش‌تری در ته‌نشین شدن رسوبات در مخزن سد منجیل و کاهش ظرفیت ذخیره این سد دارد. به طور کلی نتایج این پژوهش نشان داد که با استفاده از اطلاعات ماهواره ای به ویژه شاخص TSM، امکان پایش تغییرات SCC در طول رودخانه با هزینه و فواصل زمانی کوتاه به طور بسیار کارآمدی امکان‌پذیر است.
1- Abbasi, A., Taghavi, L., & Sarai Tabrizi, M. (2021). Qualitative Zoning of Groundwater to Assessment Suitable Drinking Water Using GIS Software in Mohammad Shahr, Meshkinshahr, and Mahdasht in Alborz Province. Anthropogenic Pollution, 5(1), 138-149. doi: 10.22034/ap.2021.1907787.1076
2- Adjovu, G.E., Stephen, H., James, D. and Ahmad, S., 2023. Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters. Remote Sensing, 15(7): 1938.
3- Chelotti, G.B., Martinez, J.M., Roig, H.L. and Olivietti, D., 2019. Space-Temporal analysis of suspended sediment in low concentration reservoir by remote sensing. RBRH, 24: e17.
4- Cremon, É.H., da Silva, A.M.S. and Montanher, O.C., 2020. Estimating the suspended sediment concentration from TM/Landsat-5 images for the Araguaia River–Brazil. Remote Sensing Letters, 11(1): 47-56.
5- da Cunha, E.R., Santos, C.A.G., da Silva, R.M., Panachuki, E., de Oliveira, P.T.S., de Souza Oliveira, N. and dos Santos Falcão, K., 2022. Assessment of current and future land use/cover changes in soil erosion in the Rio da Prata basin (Brazil). Science of The Total Environment, 818: 151811.
6- Das, S., Kaur, S. and Jutla, A., 2021. Earth observations-based assessment of impact of COVID-19 lockdown on surface water Quality of Buddha Nala, Punjab, India. Water, 13(10): 1363.
7- Dodangeh, E., Soltani, S., Sarhadi, A. and Shiau, J.T., 2014. Application of L‐moments and Bayesian inference for low‐flow regionalization in Sefidroud basin, Iran. Hydrological Processes, 28(4): 1663-1676.
8- dos Santos, F.M., de Souza Pelinson, N., de Oliveira, R.P. and Di Lollo, J.A., 2023. Using the SWAT model to identify erosion prone areas and to estimate soil loss and sediment transport in Mogi Guaçu River basin in Sao Paulo State, Brazil. Catena, 222: 106872.
9- Du, Y., Song, K., Liu, G., Wen, Z., Fang, C., Shang, Y., Zhao, F., Wang, Q., Du, J. and Zhang, B., 2020. Quantifying total suspended matter (TSM) in waters using Landsat images during 1984–2018 across the Songnen Plain, Northeast China. Journal of environmental management, 262: 110334.
10- Efthimiou, N., 2019. The role of sediment rating curve development methodology on river load modeling. Environmental monitoring and assessment, 191: 1-19.
11- Fensholt R, Sandholt I, Pround SR, 2010. Assessment of MODIS sun-sensor geometry variations effect on observed NDVI using MSG SEVIRI geostationary data. International Journal of Remote Sensing, 31(23): 6163–6187.
12- Gao, B.C. 1996. NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3): 257–266.
13- Ghaffari, A., Nasseri, M., and Pasebani Someeh, A. (2022). Assessing the economic effects of drought using Positive Mathematical Planning model under climate change scenarios. Heliyon 8: e11941.
14- Hadiyan, P.P., Moeini, R. and Ehsanzadeh, E., 2020. Application of static and dynamic artificial neural networks for forecasting inflow discharges, case study: Sefidroud Dam reservoir. Sustainable Computing: Informatics and Systems, 27: 100401.
15- Hajiabadi, R. and Zarghami, M., 2014. Multi-objective reservoir operation with sediment flushing; case study of Sefidrud reservoir. Water resources management, 28 :5357-5376.
16- Hariyanto, T., Krisna, T.C., Pribadi, C.B. and Anwar, N., 2017. Development of total suspended sediment model using Landsat-8 OLI and in-situ data at the Surabaya Coast, East Java, Indonesia. The Indonesian Journal of Geography, 49(1): 73.
17- Im, J. Jensen, and J. Tullis, 2008. "Object‐based change detection using correlation image analysis and image segmentation," International Journal of Remote Sensing, vol. 29: 399-423.
18- Jaelani, L.M., Limehuwey, R., Kurniadin, N., Pamungkas, A., Koenhardono, E.S. and Sulisetyono, A., 2016. Estimation of Total Suspended Sediment and Chlorophyll-A Concentration from Landsat 8-Oli: The Effect of Atmospher and Retrieval Algorithm. IPTEK The Journal for Technology and Science, 27(1).
19- Jally, S.K., Mishra, A.K. and Balabantaray, S., 2021. Retrieval of suspended sediment concentration of the Chilika Lake, India using Landsat-8 OLI satellite data. Environmental Earth Sciences, 80: 1-18.
20- Jayaram, C., Patidar, G., Swain, D., Chowdary, V.M. and Bandyopadhyay, S., 2021. Total suspended matter distribution in the Hooghly river estuary and the Sundarbans: a remote sensing approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 9064-9070.
21- Jin, F., Yang, W., Fu, J. and Li, Z., 2021. Effects of vegetation and climate on the changes of soil erosion in the Loess Plateau of China. Science of the Total Environment, 773: 145514.
22- Kantakumar, L.N., Neelamsetti, P. 2015. Multi-temporal land use classification using hybrid approach. The Egyptian Journal of Remote Sensing and Space Science, 18(2): 289-295.
23- Kavzoglu, T. and Colkesen, I., 2009. A Kernel function analysis for support vector machines for land cover classification. International Journal of Applied earth observation and Geoinformation 11, 352-359.
24- Khosravi, K., Rostaminejad, M., Cooper, J.R., Mao, L. and Melesse, A.M., 2019. Dam break analysis and flood inundation mapping: The case study of Sefid-Roud Dam, Iran. In Extreme hydrology and climate variability (395-405). Elsevier.
25- Kwon, S., Noh, H., Seo, I.W. and Park, Y.S., 2023. Effects of spectral variability due to sediment and bottom characteristics on remote sensing for suspended sediment in shallow rivers. Science of The Total Environment, 878: 163125.
26- Lei, S., Xu, J., Li, Y., Li, L., Lyu, H., Liu, G., Chen, Y., Lu, C., Tian, C. and Jiao, W., 2021. A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze. Environmental Pollution, 270: 116288.
27- Lei, S., Xu, J., Li, Y., Li, L., Lyu, H., Liu, G., Chen, Y., Lu, C., Tian, C. and Jiao, W., 2021. A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze. Environmental Pollution, 270: 116288.
28- Manoppo, A.K. and Budhiman, S., 2017. Estimation on the concentration of total suspended matter in Lombok Coastal using Landsat 8 OLI, Indonesia. In IOP Conference Series: Earth and Environmental Science (54(1): 012073). IOP Publishing.
29- Martinez, J.M., Espinoza‐Villar, R., Armijos, E. and Silva Moreira, L., 2015. The optical properties of river and floodplain waters in the Amazon River Basin: Implications for satellite‐based measurements of suspended particulate matter. Journal of Geophysical Research: Earth Surface, 120(7): 1274-1287.
30- Martinez, M.J. and Cox, A.L., 2023. Remote‐Sensing Method for Monitoring Suspended‐Sediment Concentration on the Middle‐Mississippi and Lower‐Missouri Rivers. Journal of Contemporary Water Research & Education, 177(1): 17-30.
31- Mohammadi, J., Fataei, E., Aghchekandi, A. O., & Taghavi, L. (2023). Investigation and determination of land use effects on surface water quality in semi-arid areas: Case study on Qarasu River in Iran. Anthropogenic Pollution (Anthropog. pollut),7(2):1-7.
32- Nukapothula, S., Yunus, A.P., Chuqun, C. and Lin, X., 2023. Impact of extreme climatic events on the total suspended matter concentrations in coastal waters using OceanSat-2 observations. Physics and Chemistry of the Earth, Parts A/B/C, 131: 103435.
33- Othman, F., Sadeghian, M.S., Ebrahimi, F. and Heydari, M., 2013. A study on sedimentation in sefidroud dam by using depth evaluation and comparing the results with USBR and FAO methods. Int. Proc. Chem. Biol. Environ. Eng, 51(9): 6.
34- Patel, B., Prajapati, A., Sarangi, R.K., Devliya, B. and Patel, H., 2023. Validation of the Total Suspended Matter (TSM) algorithm using in situ datasets over the Bay of Bengal Coastal Water. Marine Geodesy, 46(6):548-561.
35- Paulista, R.S.D., de Almeida, F.T., de Souza, A.P., Hoshide, A.K., de Abreu, D.C., da Silva Araujo, J.W. and Martim, C.C., 2023. Estimating Suspended Sediment Concentration using Remote Sensing for the Teles Pires River, Brazil. Sustainability, 15(9): 7049.
36- Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L. and Martinez, M., 2018. Suspended sediment concentration estimation from landsat imagery along the lower missouri and middle Mississippi Rivers using an extreme learning machine. Remote Sensing, 10(10): 1503.
37- Pham, Q.V., Ha, N.T.T., Pahlevan, N., Oanh, L.T., Nguyen, T.B. and Nguyen, N.T., 2018. Using Landsat-8 images for quantifying suspended sediment concentration in Red River (Northern Vietnam). Remote Sensing, 10(11): 1841.
38- Pushparaj, J., Hegde, A.V. (2017), Evaluation of pan-sharpening methods for spatial and spectral quality. Applied Geomatics, 9(1): 1-12.
39- Qiu, Z., Xiao, C., Perrie, W., Sun, D., Wang, S., Shen, H., Yang, D. and He, Y., 2017. Using L andsat 8 data to estimate suspended particulate matter in the Y ellow R iver
estuary. Journal of Geophysical Research: Oceans, 122(1): 276-290. 40- Quang, N.H., Sasaki, J., Higa, H. and Huan, N.H., 2017. Spatiotemporal variation of turbidity based on landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam. Water, 9(8): 570.
41- Riquetti, N.B., Mello, C.R., Leandro, D., Guzman, J.A. and Beskow, S., 2022. Assessment of the soil-erosion-sediment for sustainable development of South America. Journal of Environmental Management, 321: 115933.
42- Sa’ad, F.N.A., Tahir, M.S., Jemily, N.H.B., Ahmad, A. and Amin, A.R.M., 2021. Monitoring total suspended sediment concentration in spatiotemporal domain over Teluk Lipat utilizing Landsat 8 (OLI). Applied Sciences, 11(15): 7082.
43- Safizadeh, E., Karimi, D., Gahfarzadeh, H. R., & Pourhashemi, S. A. (2021). Investigation of physicochemical properties of water in downstream areas of selected dams in Aras catchment and water quality assessment (Case study: Aras catchment in the border area of Iran and Armenia). Anthropogenic Pollution, 5(1), 41-48. doi:
10.22034/ap.2021.1912491.1082 44- Toming, K., Kutser, T., Uiboupin, R., Arikas, A., Vahter, K. and Paavel, B., 2017. Mapping water quality parameters with sentinel-3 ocean and land colour instrument imagery in the Baltic Sea. Remote Sensing, 9(10): 1070.
45- Womber, Z.R., Zimale, F.A., Kebedew, M.G., Asers, B.W., DeLuca, N.M., Guzman, C.D., Tilahun, S.A. and Zaitchik, B.F., 2021. Estimation of suspended sediment concentration from remote sensing and in situ measurement over Lake Tana, Ethiopia. Advances in Civil Engineering, 2021: 1-17.
46- Xiao, Y., Chen, J., Xu, Y., Guo, S., Nie, X., Guo, Y., Li, X., Hao, F. and Fu, Y.H., 2023. Monitoring of chlorophyll-a and suspended sediment concentrations in optically complex inland rivers using multisource remote sensing measurements. Ecological Indicators, 155: 111041.
47- Xu, H. 2006. Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens, 27(14): 3025–3033.
48- Xu, S., Ehlers, M. (2017), HYPERSPECTRAL IMAGE SHARPENING BASED ON EHLERS FUSION. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W7: 941-947.
49- Yang, H., Kong, J., Hu, H., Du, Y., Gao, M. and Chen, F., 2022. A review of remote sensing for water quality retrieval: progress and challenges. Remote Sensing, 14(8): 1770.
50- Yepez, S., Laraque, A., Martinez, J.M., De Sa, J., Carrera, J.M., Castellanos, B., Gallay, M. and Lopez, J.L., 2018. Retrieval of suspended sediment concentrations using Landsat-8 OLI satellite images in the Orinoco River (Venezuela). Comptes Rendus Geoscience, 350(1-2): 20-30.
51- Yia, L. Binga, L. Qian-lia, P. Chenc, and L. Yuana, 2012. "A Change Detection Method for Remote Sensing Image Based on Multi-Feature Differencing Kernel Svm," ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 1: 227-235.
52- Yu, Z., Wang, J., Li, Y., Shum, C.K., Wang, B., He, X., Xu, H., Xu, Y. and Zhou, B., 2022. Remote sensing of suspended sediment in high turbid estuary from sentinel-3A/OLCI: A case study of Hangzhou Bay. Frontiers in Marine Science, 9: 1008070.
53- Zeng, M., Peng, J. and Liu, F., 2022. Research on Inversion of Suspended Sediment Concentration in Estuary Surface Based on Remote Sensing and GIS. Security and Communication Networks, 2022.
54- Zhang, J., Yang, J., Reinartz, P. (2016), The optimized block-regression-based fusion algorithm for pan sharpening of very high-resolution satellite imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.
55- Zhang, Y., Zhang, Y., Shi, K., Zha, Y., Zhou, Y. and Liu, M., 2016. A Landsat 8 OLI-based, semianalytical model for estimating the total suspended matter concentration in the slightly turbid Xin’anjiang Reservoir (China). IEEE journal of selected topics in applied earth observations and remote sensing, 9(1): 398-413.
56- Zhu, W., Huang, L., Sun, N., Chen, J. and Pang, S., 2020. Landsat 8‐observed water quality and its coupled environmental factors for urban scenery lakes: A case study of West Lake. Water Environment Research, 92(2): 255-265.
