Assessment of the lake water quality using Landsat 8 OLI imagery: a case study of Manchar Lake, Pakistan

Imran, Uzma; Zaidi, Arjumand; Mahar, Rasool Bux; Khokhar, Waheed Ali

doi:10.1007/s12517-022-10372-3

Cited by 3 publications

(2 citation statements)

References 45 publications

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“…The correlation coefficient between the data from this combination of bands and the measured values of chlorophyll a is 0.923, and a linear model is constructed as follows: y = 28.017x − 5.352 (9) where y denotes the concentration of chlorophyll a and x denotes the combination of the three bands: R −1 rs (λ 668.44nm ) − R −1 rs (λ 723.98nm ) * R rs (λ 771.04nm ). The model yields a coefficient of determination R 2 = 0.852 and root mean square error RMSE = 0.460 mg/L.…”

Section: Chlorophyll a Model Constructionmentioning

confidence: 99%

“…Compared with traditional water quality monitoring methods, remote sensing monitoring methods have the advantages of a large range and high efficiency, and information can be obtained in real time. Multisource remote sensing data, such as the MODIS [5], TM [6], ETM+ [7], Qui Bird [8], OLI [9], SeaWiFS [10], and MERIS [11] products, have been used in the monitoring of lake water quality. For example, Liu et al [12] used Landsat data to model and invert the water quality parameters of Erlong Lake and explored the seasonal changes in different water quality indicators, and Zhu et al [13] inverted the water quality parameters of a complex river network based on multispectral data.…”

Section: Introductionmentioning

confidence: 99%

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Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)

Zhang,

Hu,

Jiao

2024

Water

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In response to the rapid changes in the chlorophyll-a concentration and eutrophication issues in lakes, with Dianchi Lake as an example, a remote sensing estimation model for chlorophyll-a, total phosphorus, and total nitrogen in Dianchi Lake was constructed using the three band method and ratio band method based on the visible-light shortwave infrared (AHSI) hyperspectral satellite data from Gaofen 5 (GF-5) and the water quality data collected at Dianchi Lake. The model results were compared with the multispectral data from the Gaofen 1 (GF-1) wide field-of-view (WFV) camera. The accuracy evaluation results indicate that the overall mean absolute percentage error of the remote sensing estimation models for chlorophyll a, total phosphorus, and total nitrogen are 7.658%, 4.511%, and 4.577%, respectively, which can meet the needs of lake water quality monitoring and evaluation. According to the remote sensing simulation results, chlorophyll a is mainly distributed in the northern part of Dianchi Lake, with phosphorus and nitrogen pollution throughout Dianchi Lake and relatively more abundant in the central and southern regions. The pollution is mainly concentrated in the northern and southern regions of Dianchi Lake, which is consistent with the actual situation. Further confirming the feasibility of using GF-5 satellite AHSI data for water quality parameter retrieval can provide new technical means for relevant departments to quickly and efficiently monitor the inland lake water environment.

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Section: Chlorophyll a Model Constructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)

Zhang,

Hu,

Jiao

2024

Water

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Distribution, Source Identification, and Ecological Risk Assessment of Selected Trace Elements in Sediments from Manchar Lake, Pakistan

Imran

Ullah

Mahar

et al. 2022

Soil and Sediment Contamination: An International Journal

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Water quality assessment of Loktak Lake, Manipur using Landsat 9 imagery

Singh,

Yurembam,

Jhajharia

et al. 2024

Water Practice &Amp; Technology

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The role of freshwater lakes in providing water resources and supporting ecosystems is essential. Monitoring water quality using remote sensing (RS) technologies is crucial for sustainable management practices. A study on Loktak Lake was done using RS algorithms to predict post-monsoon water quality. The multiplication band model (B1 × B6) demonstrated a moderate correlation with dissolved oxygen (DO) values (mg/l) with (coefficient of determination, R2 = 0.47, root mean square error, RMSE = 0.23, and standard error of estimation, SEE = 0.23). The band combination (B2/B4) was strongly correlated with electrical conductivity (EC) values (μs/cm) (R2 = 0.60, RMSE = 9.44, and SEE = 9.69). For total dissolved solids (TDS) (mg/l), with an R2 = 0.61, RMSE = 5.95, and SEE = 6.09, Band 2 demonstrated a strong correlation between field values and satellite imagery. The post-monsoon water quality map of the lake indicates lower concentrations of DO, EC, and TDS on the western side and elevated values on the eastern side. The research concluded that RS algorithms can be effectively used to predict water quality parameters in Loktak Lake, specifically DO, EC, and TDS. The findings suggest that effective pollution management is needed on the western side of the lake.

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Assessment of the lake water quality using Landsat 8 OLI imagery: a case study of Manchar Lake, Pakistan

Cited by 3 publications

References 45 publications

Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)

Water Quality Parameter Retrieval with GF5-AHSI Imagery for Dianchi Lake (China)

Distribution, Source Identification, and Ecological Risk Assessment of Selected Trace Elements in Sediments from Manchar Lake, Pakistan

Water quality assessment of Loktak Lake, Manipur using Landsat 9 imagery

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