Assessment of aquatic ecosystem quality in Dharoi Reservoir using Sentinel-2 satellite imagery

Saurabh Kumar Gupta, Shruti Kanga, Suraj Kumar Singh

Article ID: 4477
Vol 7, Issue 1, 2024

VIEWS - 246 (Abstract) 65 (PDF)

Abstract


Background: Dharoi Reservoir, located in Gujarat, India, is a vital freshwater resource supporting agriculture, industry, and local communities. Chl-a, a key indicator of water quality, reflects the trophic state and ecological balance of aquatic systems. Objective(s): This study aims to provide comprehensive insights into the water quality dynamics of Dharoi Reservoir, offering valuable information for environmental management and sustainable water resource planning. Methods: This study employs high-resolution Sentinel-2 satellite imagery to analyze Chl-a concentrations in the reservoir during October 2020. The Chl-a index, calculated by dividing Sentinel-2 bands B5 and B4, reveals a spatial distribution of Chl-a concentrations. Results: The Chl-a index ranges from 73.78 to 100. The mean Chl-a index is 91.6 with a standard deviation of 3.27, indicating elevated and variable Chl-a concentrations. Conclusions: The findings contribute to the understanding of the reservoir’s ecological health and assist in making informed decisions for water quality management. This research exemplifies the integration of remote sensing technology and environmental stewardship, promoting sustainable water management practices in the region. Policy recommendations: No policy recommendations are explicitly stated in the abstract, but they could be inferred from the conclusions. For example, one possible policy recommendation is to monitor and regulate the sources of nutrient inputs into the reservoir, such as agricultural runoff, sewage, and industrial effluents, to reduce the risk of eutrophication and algal blooms. Another possible policy recommendation is to implement adaptive management strategies that consider the seasonal and spatial variability of Chl-a concentrations and their impacts on water quality and availability.


Keywords


Dharoi Reservoir; Chlorophyll-a (Chl-a); Sentinel-2 satellite imagery; water quality; ecological health; sustainable water management

Full Text:

PDF


References


1. Brierley G, Sahoo S, Danino M, et al. A plural knowledges model to support sustainable management of dryland rivers in western India. River Research and Applications. Published online November 2023. doi: 10.1002/rra.4219

2. Roy A, Chatterjee S, Kumar Sinha U, et al. Recharge and vulnerability assessment of groundwater resources in North west India: Insights from isotope-geospatial modelling approach. Geoscience Frontiers. 2024; 15(1): 101721. doi: 10.1016/j.gsf.2023.101721

3. Joshi GS, Makhasana P. Impact of Climate Change on Hydrological Parameters in Dharoi Dam Reservoir Basin, India. The International Journal of Climate Change: Impacts and Responses. 2020; 12(1): 13-29. doi: 10.18848/1835-7156/cgp/v12i01/13-29

4. Patel A, Yadav SM. Improving the reservoir inflow prediction using TIGGE ensemble data and hydrological model for Dharoi Dam, India. Water Supply. 2023; 23(11): 4489-4509. doi: 10.2166/ws.2023.274

5. Gupta P, Singh SK, Gupta P, et al. Application of Remote Sensing and GIS Techniques for Identification of Changes in Land Use and Land Cover (LULC): A Case Study. Indian Journal Of Science And Technology. 2023; 16(46): 4456-4468. doi: 10.17485/ijst/v16i46.2530

6. Kotecha MJ, Tripathi G, Singh SK, et al. GIS-Based Novel Ensemble MCDM-AHP Modeling for Flash Flood Susceptibility Mapping of Luni River Basin, Rajasthan. In: Rai PK (editors). River Conservation and Water Resource Management. Advances in Geographical and Environmental Sciences. Springer, Singapore. 2023. pp 267–313.

7. Debnath J, Debbarma J, Debnath A, et al. Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm. Environmental Monitoring and Assessment. 2024; 196(2). doi: 10.1007/s10661-023-12240-3

8. Karimian H, Huang J, Chen Y, et al. A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms. Environmental Science and Pollution Research. 2023; 30(32): 79402-79422. doi: 10.1007/s11356-023-27886-2

9. Odermat D, Gitelson A. Mapping Chlorophyll-a Concentration in a Turbid Reservoir Using Sentinel-2. Remote Sensing of Environment. 2018; 210: 76-89.

10. Saqib N, Rai PK, Kanga S, et al. Assessment of Ground Water Quality of Lucknow City under GIS Framework Using Water Quality Index (WQI). Water. 2023; 15(17): 3048. doi: 10.3390/w15173048

11. Singh S, Meraj G, Kumar P, et al. Decoding Chambal River Shoreline Transformations: A Comprehensive Analysis Using Remote Sensing, GIS, and DSAS. Water. 2023; 15(9): 1793. doi: 10.3390/w15091793

12. Gupta SK, Kanga S, Meraj G, et al. Uncovering the hydro-meteorological drivers responsible for forest fires utilizing geospatial techniques. Theoretical and Applied Climatology. 2023; 153(1-2): 675-695. doi: 10.1007/s00704-023-04497-y

13. Gholizadeh MH, Khatami R, Homayouni S. Assessment of Chlorophyll-a Concentration in Surface Waters Using Landsat 8 Images. Remote Sensing. 2017; 9(3): 215.

14. Gitelson AA, Gurlin D, Moses WJ, et al. A bio-optical algorithm for the remote estimation of the chlorophyll- a concentration in case 2 waters. Environmental Research Letters. 2009; 4(4): 045003. doi: 10.1088/1748-9326/4/4/045003

15. Cheng C, Wei Y, Lv G, et al. Remote sensing estimation of chlorophyll-a concentration in Taihu Lake considering spatial and temporal variations. Environmental Monitoring and Assessment. 2019; 191(2). doi: 10.1007/s10661-018-7106-4

16. Cen H, Jiang J, Han G, et al. Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea. Remote Sens. 2022; 14: 5461. doi: 10.3390/ rs14215461

17. Kupssinsku LS, Guimaraes TT, de Freitas R, et al. Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks. 2019 13th International Conference on Sensing Technology (ICST). Published online December 2019. doi: 10.1109/icst46873.2019.9047682

18. Amieva JF, Oxoli D, Brovelli MA. Machine and Deep Learning Regression of Chlorophyll-a Concentrations in Lakes Using PRISMA Satellite Hyperspectral Imagery. Remote Sensing. 2023; 15(22): 5385. doi: 10.3390/rs15225385

19. CEIC Data. CEIC Data, an ISI Emerging Markets Group Company. Available online: https://www.ceicdata.com/en (accessed on 4 January 2024).

20. Tomar JS, Kranjčić N, Đurin B, et al. Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): A Geospatial Approach. ISPRS International Journal of Geo-Information. 2021; 10(7): 447. doi: 10.3390/ijgi10070447

21. Hekmat H, Ahmad T, Singh SK, et al. Land Use and Land Cover Changes in Kabul, Afghanistan Focusing on the Drivers Impacting Urban Dynamics during Five Decades 1973–2020. Geomatics. 2023; 3(3): 447-464. doi: 10.3390/geomatics3030024

22. Chandel RS, Kanga S, Singh SK, et al. Assessing Sustainable Ecotourism Opportunities in Western Rajasthan, India, through Advanced Geospatial Technologies. Sustainability. 2023; 15(14): 11473. doi: 10.3390/su151411473

23. Farooq M, Mushtaq F, Meraj G, et al. Strategic Slum Upgrading and Redevelopment Action Plan for Jammu City. Resources. 2022; 11(12): 120. doi: 10.3390/resources11120120

24. Jang W, Kim J, Kim JH, et al. Evaluation of Sentinel-2 Based Chlorophyll-a Estimation in a Small-Scale Reservoir: Assessing Accuracy and Availability. Remote Sensing. 2024; 16(2): 315. doi: 10.3390/rs16020315

25. Ahmad T, Gupta SK, Singh SK, et al. Unveiling Nature’s Resilience: Exploring Vegetation Dynamics during the COVID-19 Era in Jharkhand, India, with the Google Earth Engine. Climate. 2023; 11(9): 187. doi: 10.3390/cli11090187

26. Viso-Vázquez M, Acuña-Alonso C, Rodríguez JL, et al. Remote Detection of Cyanobacterial Blooms and Chlorophyll-a Analysis in a Eutrophic Reservoir Using Sentinel-2. Sustainability. 2021; 13(15): 8570. doi: 10.3390/su13158570

27. Atique U, An KG. Landscape heterogeneity impacts water chemistry, nutrient regime, organic matter and chlorophyll dynamics in agricultural reservoirs. Ecological Indicators. 2020; 110: 105813. doi: 10.1016/j.ecolind.2019.105813

28. Mamun M, Atique U, An KG. Assessment of Water Quality Based on Trophic Status and Nutrients-Chlorophyll Empirical Models of Different Elevation Reservoirs. Water. 2021; 13(24): 3640. doi: 10.3390/w13243640




DOI: https://doi.org/10.24294/nrcr.v7i1.4477

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Saurabh Kumar Gupta, Shruti Kanga, Suraj Kumar Singh

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.