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Tytuł artykułu

Synergic use of neural networks model and remote sensing algorithms to estimate water clarity indicators in Khanpur reservoir, Pakistan

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Freshwater reservoirs are limited and facing issues of over-exploitation, climate change effects and poor maintenance which have serious consequences for water quality. Developing countries face the challenge of collecting in situ information on ecological status and water quality of these reservoirs due to constraints of cost, time and infrastructure. In this study, a practical method of retrieval of two water clarity indicators, total suspended matter and secchi disk depth, using Sentinel-2 satellite data is adopted for preliminary assessment of water quality and trophic conditions in Khanpur reservoir, Pakistan. The study explores the synergy of utilizing two independent models, i.e., case 2 regional coast color analytical neural network model and semiempirical remote sensing algorithms to understand the spatiotemporal dynamics of water clarity patterns in the dammed reservoir, in the absence of ground measurements. The drinking water quality and trophic state of the reservoir water is determined based purely on satellite measurements. Out of the five months studied, the reservoir water has high turbidity and poor eutrophic status in three months. The results from both computational models are compared, which exhibit a high degree of statistical agreement. The study demonstrates the effective utilization of relevant analytical and semiempirical methods on satellite data to map water clarity indicators and understand their dynamics in both space and time. This solution is particularly useful for regions where routine ground sampling and observation of environmental variables are absent.
Czasopismo
Rocznik
Strony
1433--1443
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
  • Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Center for Remote Sensing, University of the Punjab, Lahore 54590, Pakistan
  • Remote Sensing, GIS and Climatic Research Lab (National Center of GIS and Space Applications), Center for Remote Sensing, University of the Punjab, Lahore 54590, Pakistan
  • Department of Space Science, University of the Punjab, Lahore 54590, Pakistan
Bibliografia
  • 1. Avdan ZY, Kaplan G, Goncu S, Avdan U (2019) Monitoring the water quality of small water bodies using high-resolution remote sensing data. ISPRS Int J Geoinf 8(12):553
  • 2. Bresciani M, Giardino C, Stroppiana D, Dessena MA, Buscarinu P, Cabras L et al (2019) Monitoring water quality in two dammed reservoirs from multispectral satellite data. Eur J Remote Sens 52:113–122
  • 3. Buma WG, Lee SI (2020) Evaluation of sentinel-2 and landsat 8 images for estimating chlorophyll-a concentrations in lake Chad, Africa. Remote Sens 12(15):2437
  • 4. Elhag M, Gitas I, Othman A, Bahrawi J, Gikas P (2019) Assessment of water quality parameters using temporal remote sensing spectral reflectance in arid environments, Saudi Arabia. Water 11(3):556
  • 5. ESA-European space agency (2021) Sentinel 2 https://sentinels.copernicus.eu/web/sentinel/missions/sentinel-2
  • 6. Facco DS, Guasselli LA, Ruiz LFC, Simioni JPD, Dick DG (2021) Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs. Geocarto Int 2021:1–21. https://doi.org/10.1080/10106049.2021.1899302
  • 7. Garg V, Aggarwal SP, Chauhan P (2020) Changes in turbidity along Ganga river using sentinel-2 satellite data during lockdown associated with COVID-19. Geomatics Nat Hazards Risk 11(1):1175–1195
  • 8. Ha NTT, Thao NTP, Koike K, Nhuan MT (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 Int J Geoinf 6(9):290
  • 9. Matthews MW (2011) A current review of empirical procedures of remote sensing in inland and near-coastal transitional waters. Int J Remote Sens 32(21):6855–6899
  • 10. Muduli PR, Kumar A, Kanuri VV, Mishra DR, Acharya P, Saha R et al (2021) Water quality assessment of the Ganges River during COVID-19 lockdown. Int J Environ Sci Technol 18(6):1645–1652
  • 11. Nauman S, Zulkafli Z, Bin Ghazali AH, Yusuf B (2019) Impact assessment of future climate change on streamflows upstream of Khanpur Dam, Pakistan using soil and water assessment tool. Water 11(5):1090
  • 12. Nazirova K, Alferyeva Y, Lavrova O, Shur Y, Soloviev D, Bocharova T, Strochkov A (2021) Comparison of in situ and remote-sensing methods to determine turbidity and concentration of suspended matter in the Estuary zone of the Mzymta river, Black Sea. Remote Sens 13(1):143
  • 13. Ouma YO, Noor K, Herbert K (2020) Modelling reservoir chlorophyll-a, TSS, and turbidity using sentinel-2A MSI and landsat-8 OLI satellite sensors with empirical multivariate regression. J Sens. https://doi.org/10.1155/2020/8858408
  • 14. PCRWR (2020) Wastewater assessment and treatment needs analysis of district Jhelum, Pakistan council of research in water resources (PCRWR), 52
  • 15. Pompeo M, Moschini-Carlos V, Bitencourt MD, Soria-Perpinya X, Vicente E, Delegido J (2021) Water quality assessment using sentinel-2 imagery with estimates of chlorophyll a, Secchi disk depth, and cyanobacteria cell number: the Cantareira system reservoirs (São Paulo, Brazil). Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-12975-x
  • 16. Pompei CME, Alves EDL, Vieira EM, Campos LC (2020) Impact of meteorological variables on water quality parameters of a reservoir and ecological filtration system. Int J Environ Sci Technol 17(3):1387–1396. https://doi.org/10.1007/s13762-019-02552-8
  • 17. Premkumar R, Venkatachalapathy R, Visweswaran S (2021) Mapping of total suspended matter based on sentinel-2 data on the Hooghly River, India. Indian J Ecol 48(1):159–165
  • 18. Sebastiá-Frasquet MT, Aguilar-Maldonado JA, Santamaría-Del-Ángel E, Estornell J (2019) Sentinel 2 analysis of turbidity patterns in a coastal lagoon. Remote Sens 11(24):2926
  • 19. Soomets T, Uudeberg K, Jakovels D, Brauns A, Zagars M, Kutser T (2020) Validation and comparison of water quality products in baltic lakes using sentinel-2 msi and sentinel-3 OLCI data. Sensors 20(3):742
  • 20. Sòria-Perpinyà X, Vicente E, Urrego P, Pereira-Sandoval M, Tenjo C, Ruíz-Verdú A, Moreno J (2021) Validation of water quality monitoring algorithms for sentinel-2 and sentinel-3 in Mediterranean inland waters with in situ reflectance data. Water 13(5):686
  • 21. Toming K, Kutser T, Uiboupin R, Arikas A, Vahter K, Paavel B (2017) Mapping water quality parameters with sentinel-3 ocean and land colour instrument imagery in the Baltic Sea. Remote Sens 9(10):1070
  • 22. Topp SN, Pavelsky TM, Jensen D, Simard M, Ross MR (2020) Research trends in the use of remote sensing for inland water quality science: moving towards multidisciplinary applications. Water 12(1):169
  • 23. Watanabe FSY, Alcântara E, Rodrigues TWP, Imai NN, Barbosa CCF, Rotta LHDS (2015) Estimation of chlorophyll-a concentration and the trophic state of the Barra Bonita hydroelectric reservoir using OLI/Landsat-8 images. Int J Environ Res Public Health 12(9):10391–10417
  • 24. WWF (2007) Pakistan’s waters at risk: water and health related issues in Pakistan & key recommendations. https://www.ircwash.org/sites/default/files/WWF-Pakistan-2007-Pakistans.pdf
  • 25. Zaheer M, Ahmad Z, Shahab A (2016) Hydrological modeling and characterization of the Khanpur watershed, Pakistan. J Am Water Works Assoc 108(5):262–268
  • 26. Zhou ZZ, Huang TL, Ma WX, Li Y, Zeng K (2015) Impacts of water quality variation and rainfall runoff on Jinpen reservoir, in Northwest China. Water Sci Eng 8(4):301–308
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-da27d557-2590-4f91-808b-639730765b4b
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