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برآورد کدورت آب با استفاده از سنجش از دور و الگوریتم جنگل تصادفی، مطالعه موردی: دریاچه شهدای خلیج فارس چیتگر تهران | ||
| محیط شناسی | ||
| مقاله 1، دوره 50، شماره 1، خرداد 1403، صفحه 1-15 اصل مقاله (1.11 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jes.2023.350214.1008365 | ||
| نویسندگان | ||
| بهناز کریمی1؛ سید حسین هاشمی* 2؛ حسین عقیقی3 | ||
| 1دانشگاه شهید بهشتی، پژوهشکده علوم محیطی، تهران، ایران | ||
| 2دانشگاه شهید بهشتی | ||
| 3دانشگاه شهید بهشتی، پژوهشکده GIS و سنجش از دور، تهران | ||
| چکیده | ||
| کدورت آب از مهم‌ترین پارامترهای کیفیت آب محسوب می‌شود که معرف شفافیت آب و مؤثر بر تغذیه‌گرایی است. این پژوهش با هدف برآورد مقدار کدورت آب با استفاده از داده‌های سنجش از دور و تکنیک جنگل تصادفی انجام شده است. بدین منظور، از داده‌های پایش کیفیت آب دریاچه شهدای خلیج فارس چیتگر تهران که دریاچه‌ای شهری و کم‌عمق، با کاربری تفرج و منظر شهری است، استفاده شد. تصاویر ماهواره-های لندست-8 و سنتینل-2 پس از انطباق تاریخ داده‌های میدانی و تصاویر ماهواره‌ای برای دوره زمانی سال 1395 تا 1400، انتخاب و داده‌ها به دو گروه جهت تولید و اعتبارسنجی مدل تقسیم شدند. نخست عملیات پیش پردازش روی تصاویر ماهواره‌ای انجام شد. سپس با استفاده از تکنیک جنگل تصادفی باندهای مؤثر شناسایی گردیدند، پس از آن، ترکیب‌های باندی بهینه انتخاب و مدل‌های رگرسیون برازش و اعتبارسنجی شدند. مدل به‌دست آمده، میزان کدورت آب را با Adj.R2=0.6، RMSE=1.07 NTU و NRMSE=12% در ماهواره لندست-8 و Adj.R2=0.73، RMSE=1.23 NTU و NRMSE=9% در ماهواره سنتینل-2 و با توان آماری 80 درصد برای دریاچه چیتگر پیش‌بینی کرد. بدین ترتیب، مدل برآوردی بهینه با کمک تکنیک جنگل تصادفی براساس داده‌های ماهواره سنتینل-2 به‌دست آمد و مدل پیش‌بینی توانست مقادیر کدورت آب را در دریاچه چیتگر با دقت قابل قبولی برآورد کند. | ||
| کلیدواژه‌ها | ||
| سنجش از دور؛ کدورت آب؛ پایش کیفیت آب؛ جنگل تصادفی | ||
| عنوان مقاله [English] | ||
| Estimation of Water Turbidity by Remote Sensing and Random Forest Algorithm, Case Study: Chitgar Persian Gulf Martyrs Lake, Tehran | ||
| نویسندگان [English] | ||
| Behnaz Karimi1؛ Seyed Hossein Hashemi2؛ Hossein Aghighi3 | ||
| 1Shahid Beheshti University, Environmental Sciences Research Institute, Tehran, Iran | ||
| 2Shahid Beheshti University | ||
| 3Shahid Beheshti University, GIS & RS research center, Tehran, Iran | ||
| چکیده [English] | ||
| Water turbidity is one of the most important parameters of water quality, which represents the transparency of water and is effective in eutrophication. This research was done to estimate the amount of water turbidity using remote sensing data and the random forest technique. For this purpose, the water quality monitoring data of Chitgar Lake in Tehran were used, which is an artificial shallow lake with recreational and urban scenery usage. The Landsat 8 OLI/TIRS and Sentinel 2 MSI satellite images were extracted after matching the date of field observation data and satellite images from 2016 to 2021. Data were divided into calibration and validation datasets. After performing pre-processing processes on satellite images, important bands were recognized using the random forest method. Afterward, appropriate band composition and algorithms were selected and regression models were fitted and validated. The optimum model was able to estimate water turbidity with Adj.R2=0.6, RMSE=1.07 NTU, and NRMSE=12% for Landsat-8 as well as with Adj.R2=0.73, RMSE=1.23 NTU and NRMSE=9% for Sentinel-2 satellite and estimated with a power of 80% for Chitgar Lake. Consequently, the optimal predictive model in Sentinel-2 was chosen with the assistance of the random forest. Moreover, the predictive model was able to estimate the water turbidity in Chitgar Lake with acceptable accuracy. | ||
| کلیدواژه‌ها [English] | ||
| Remote Sensing, Water Turbidity, Water Quality Monitoring, Random Forest | ||
| مراجع | ||
|
Aghighi, H., Alimohamma, A., Reza Sarad, M., & Ashourloo, D. (2008). Estimation of Water Turbidity in Gorgan Bay, South-East of Caspian Sea by Using IRS-LISS-III Images. Pakistan Journal of Biological Sciences, 11(5), 711–718. https://doi.org/10.3923/pjbs.2008.711.718
Akbar, T. A., Hassan, Q. K., & Achari, G. (2010). A remote sensing-based framework for predicting water quality of different water sources. Remote Sensing and Spatial Information Sciences, 34(xxx).
Atif, S., Syed Jamil Hasan, K., Suhaib bin, F., Saima, S., Adnan, A., Hafiz Uzair Ahmed, K., Aimen Fatima, A., & Fahad, A. (2018). Mapping Turbidity Levels in the Lake’s Water Using Satellite Remote Sensing Technique. International Journal of Economic and Environment Geology, 9(3).
Baughman, C., Jones, B., Bartz, K., Young, D., & Zimmerman, C. (2015). Reconstructing Turbidity in a Glacially Influenced Lake Using the Landsat TM and ETM+ Surface Reflectance Climate Data Record Archive, Lake Clark, Alaska. Remote Sensing, 7(10), 13692–13710. https://doi.org/10.3390/rs71013692
Bayat, J., Hashemi, S. H., Zolfagharian, M., Emam, A., & Nooshabadi, E. Z. (2019). Water quality management of an artificial lake, case study: The lake of the Martyrs of the Persian Gulf. In Sustainable and Safe Dams Around the World (pp. 1442–1449). CRC Press. https://doi.org/10.1201/9780429319778-127
Blix, K., Pálffy, K., Tóth, V. R., & Eltoft, T. (2018). Remote sensing of water quality parameters over Lake Balaton by using Sentinel-3 OLCI. Water (Switzerland), 10(10). https://doi.org/10.3390/w10101428
Bohn, V. Y., Carmona, F., Rivas, R., Lagomarsino, L., Diovisalvi, N., & Zagarese, H. E. (2018). Development of an empirical model for chlorophyll-a and Secchi Disk Depth estimation for a Pampean shallow lake (Argentina). The Egyptian Journal of Remote Sensing and Space Science, 21(2), 183–191. https://doi.org/10.1016/ j.ejrs.2017.04.005
Buma, W. G., & Lee, S.-I. (2020). Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa. Remote Sensing, 12(15), 2437. https://doi.org/10.3390/rs12152437
Chen, X., Chen, W., Bai, Y., & Wen, X. (2022). Changes in turbidity and human activities along Haihe River Basin during lockdown of COVID-19 using satellite data. Environmental Science and Pollution Research, 29(3), 3702–3717. https://doi.org/10.1007/s11356-021-15928-6
Chu, H.-J., He, Y.-C., Chusnah, W. N., Jaelani, L. M., & Chang, C.-H. (2021). Multi-Reservoir Water Quality Mapping from Remote Sensing Using Spatial Regression. Sustainability, 13(11), 6416. https://doi.org/10.3390/ su13116416
Ellero M. (2018). Water Quality Assessment using Landsat 8 and Sentinel-2: A case study of the Umdloti Estuary, KwaZulu-Natal, South Africa. KwaZulu Natal.
El-Zeiny, A., & El-Kafrawy, S. (2017). Assessment of water pollution induced by human activities in Burullus Lake using Landsat 8 operational land imager and GIS. The Egyptian Journal of Remote Sensing and Space Science, 20, S49–S56. https://doi.org/10.1016/j.ejrs.2016.10.002
ESRI. (2022). Forest-based Classification and Regression (Spatial Statistics). https://pro.arcgis.com/en/pro-app /2.8/tool-reference/spatial-statistics/forestbasedclassificationregression.htm
Garg, V., Aggarwal, S. P., & Chauhan, P. (2020). Changes in turbidity along Ganga River using Sentinel-2 satellite data during lockdown associated with COVID-19. Geomatics, Natural Hazards and Risk, 11(1), 1175–1195. https://doi.org/10.1080/19475705.2020.1782482
Gholizadeh, M. H., & Melesse, A. M. (2017). Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing. Journal of Remote Sensing & GIS, 06(03). https://doi.org/10.4172/2469-4134. 1000207
Gholizadeh, M., Melesse, A., & Reddi, L. (2016). A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques. Sensors, 16(8), 1298. https://doi.org/10.3390/s16081298
Ha, N. T. T., Thao, N. T. P., Koike, K., & Nhuan, M. T. (2017). Selecting the Best Band Ratio to Estimate Chlorophyll-a Concentration in a Tropical Freshwater Lake Using Sentinel 2A Images from a Case Study of Lake Ba Be (Northern Vietnam). ISPRS International Journal of Geo-Information, 6(9), 290. https://doi.org/10.3390/ ijgi6090290
Hafeez, S., Sing Wong, M., Abbas, S., Y. T., Kwok, C., Nichol, J., Ho Lee, K., Tang, D., & Pun, L. (2019). Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies. In Monitoring of Marine Pollution. IntechOpen. https://doi.org/10.5772/intechopen.81657
Harrington, J. A., Schiebe, F. R., & Nix, J. F. (1992). Remote sensing of Lake Chicot, Arkansas: Monitoring suspended sediments, turbidity, and Secchi depth with Landsat MSS data. Remote Sensing of Environment, 39(1), 15–27. https://doi.org/10.1016/0034-4257(92)90137-9
He, Y., Jin, S., & Shang, W. (2021). Water Quality Variability and Related Factors along the Yangtze River Using Landsat-8. Remote Sensing, 13(12), 2241. https://doi.org/10.3390/rs13122241
Hossain, A. K. M. A., Mathias, C., & Blanton, R. (2021). Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite. Remote Sensing, 13(18), 3785. https://doi.org/10.3390/rs13183785
Huang J, Guo H, Guo X, & Singh V.P. (2020). Retrieval of Non-Optically Active Parameters for Small Scale Urban Waterbodies by a Machine Learning-Based Strategy. Preprints. https://doi.org/10.20944/preprints 202004.0111.v1
IBM. (2020, December 7). Random forest. IBM. https://www.ibm.com/cloud/learn/random-forest
Kalele, A. (2019). ESTIMATION AND MAPPING OF TURBIDITY IN THE LOWER CHARLES RIVER USING LANDSAT 8 OLI SATELLITE IMAGERY. Northeastern University Boston, Massachusetts.
Kapalanga, T. S., Hoko, Z., Gumindoga, W., & Chikwiramakomo, L. (2021). Remote-sensing-based algorithms for water quality monitoring in Olushandja Dam, north-central Namibia. Water Supply, 21(5), 1878–1894. https://doi.org/ 10.2166/ ws.2020.290
Karch, J. (2020). Improving on adjusted R-squared. Collabra: Psychology, 6(1). https://doi.org/10.1525/collabra.343
Katlane, R., Dupouy, C., Kilani, B. el, & Berges, J. C. (2020). Estimation of Chlorophyll and Turbidity Using Sentinel 2A and EO1 Data in Kneiss Archipelago Gulf of Gabes, Tunisia. International Journal of Geosciences, 11(10), 708–728. https://doi.org/10.4236/ijg.2020.1110035
Li, X., Ding, J., & Ilyas, N. (2021). Machine learning method for quick identification of water quality index (WQI) based on Sentinel-2 MSI data: Ebinur Lake case study. Water Science and Technology: Water Supply, 21(3). https://doi.org/10.2166/ws.2020.381
Lim, J., & Choi, M. (2015). Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea. Environmental Monitoring and Assessment, 187(6), 384. https://doi.org/10.1007/ s10661-015-4616-1
Moore, G. K. (1980). Satellite remote sensing of water turbidity. Hydrological Sciences Bulletin, 25(4). https://doi.org/10.1080/02626668009491950
Nas, B., Ekercin, S., Karabörk, H., Berktay, A., & Mulla, D. J. (2010). An Application of Landsat-5TM Image Data for Water Quality Mapping in Lake Beysehir, Turkey. Water, Air, & Soil Pollution, 212(1–4), 183–197. https://doi.org/10.1007/s11270-010-0331-2
Pavelsky, T. M., & Smith, L. C. (2009). Remote sensing of suspended sediment concentration, flow velocity, and lake recharge in the Peace-Athabasca Delta, Canada. Water Resources Research, 45(11). https://doi.org/10.1029/ 2008WR007424
Quang, N., Sasaki, J., Higa, H., & Huan, N. (2017). Spatiotemporal Variation of Turbidity Based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam. Water, 9(8), 570. https://doi.org/10.3390/w9080570
Ritchie, J. C., Zimba, P. V., & Everitt, J. H. (2003). Remote Sensing Techniques to Assess Water Quality. Photogrammetric Engineering & Remote Sensing, 69(6), 695–704. https://doi.org/10.14358/PERS.69.6.695
Rubin, H. J., Lutz, D. A., Steele, B. G., Cottingham, K. L., Weathers, K. C., Ducey, M. J., Palace, M., Johnson, K. M., & Chipman, J. W. (2021). Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning. Remote Sensing, 13(8), 1434. https://doi.org/10.3390/rs13081434
Sebastiá-Frasquet, M.-T., Aguilar-Maldonado, J. A., Santamaría-Del-Ángel, E., & Estornell, J. (2019). Sentinel 2 Analysis of Turbidity Patterns in a Coastal Lagoon. Remote Sensing, 11(24), 2926. https://doi.org/10.3390/ rs11242926
Topp, S. N., Pavelsky, T. M., Jensen, D., Simard, M., & Ross, M. R. V. (2020). Research trends in the use of remote sensing for inland water quality science: Moving towards multidisciplinary applications. In Water (Switzerland) (Vol. 12, Issue 1). https://doi.org/10.3390/w12010169
Verzani, J. (2004). Using R for Introductory Statistics. In Using R for Introductory Statistics. New York: Chapman and Hall/CRC. https://doi.org/10.4324/9780203499894
Wang, L., Xu, M., Liu, Y., Liu, H., Beck, R., Reif, M., Emery, E., Young, J., & Wu, Q. (2020). Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine. Remote Sensing, 12(20), 3278. https://doi.org/10.3390/rs12203278
Wass, P. D., Marks, S. D., Finch, J. W., Leeks, G. J. L., & Ingram, J. K. (1997). Monitoring and preliminary interpretation of in-river turbidity and remotely sensed imagery for suspended sediment transport studies in the Humber catchment. Science of The Total Environment, 194–195, 263–283. https://doi.org/10.1016/S0048-9697(96) 05370-3
Zhang, S., & Gao, H. (2020). Using the digital elevation model (DEM) to improve the spatial coverage of the MODIS-based reservoir monitoring network in South Asia. Remote Sensing, 12(5). https://doi.org/10.3390/rs12050745
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آمار تعداد مشاهده مقاله: 545 تعداد دریافت فایل اصل مقاله: 433 |
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