Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs

Facco, Douglas Stefanello; Guasselli, Laurindo Antônio; Ruiz, Luis Fernando Chimelo; Simioni, João Paulo Delapasse; Dick, Daiane Gerhardt

doi:10.1080/10106049.2021.1899302

Cited by 4 publications

(5 citation statements)

References 118 publications

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“…Initially, the water pixels in each image were identified using the normalized difference water index (NDWI), and subsequently, only the reservoir area delineated using appropriate NDWI threshold was retained for the calculation of semiempirical algorithms. Since suspended matter in water causes turbid conditions, the qualitative estimation of TSM was done using normalized difference turbidity index (NDTI), the effectiveness of which has been reported by Garg et al ( 2020 ) and Facco et al ( 2021 ) for mapping turbidity in reservoirs. On the other hand, SDD has been qualitatively estimated using band ratio ( R 560(nm) / R 709(nm) ), as recommended by Sòria-Perpinyà et al ( 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…Similar to many other developing countries, in Pakistan, in situ monitoring records of water quality of freshwater bodies and reservoirs are lacking due to several logistic constraints laid by limited monetary, infrastructural and legal provisions (Facco et al 2021 ). The inland freshwater lakes in all provinces of Pakistan are being polluted rapidly, with the ecological status of many under threat.…”

Section: Introductionmentioning

confidence: 99%

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

Faizi

Mahmood

2022

Acta Geophys.

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Faizi

Mahmood

2022

Acta Geophys.

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“…Therefore, in Table 10, we have also included solutions based on remote sensing. In some cases, classification is used to quantify the turbidity [52][53][54][55], which is an imperfect solution compared with regression models. Of these papers, two of them use satellite images [52,53], and two of them use proximal sensing images gathered in the laboratory [54,55].…”

Section: Comparison With Existing Proposalsmentioning

confidence: 99%

“…In some cases, classification is used to quantify the turbidity [52][53][54][55], which is an imperfect solution compared with regression models. Of these papers, two of them use satellite images [52,53], and two of them use proximal sensing images gathered in the laboratory [54,55]. Among the papers that used the classification of turbidity to identify its source, the number of papers that used optical sensors is extremely limited.…”

Section: Comparison With Existing Proposalsmentioning

confidence: 99%

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

Parra,

Ahmad,

Sendra

et al. 2024

Chemosensors

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Turbidity is one of the crucial parameters of water quality. Even though many commercial devices, low-cost sensors, and remote sensing data can efficiently quantify turbidity, they are not valid tools for the classification it. In this paper, we design, calibrate, and test a novel optical low-cost sensor for turbidity quantification and classification. The sensor is based on an RGB light source and a light detector. The analyzed samples are characterized by turbidity values from 0.02 to 60 NTUs, and have four different sources. These samples were generated to represent natural turbidity sources and leaves in the marine areas close to agricultural lands. The data are gathered using 64 different combinations of light, generating complex matrix data. Machine learning models are compared to analyze this data, including training, validation, and test datasets. Moreover, different alternatives for data preprocessing and feature selection are assessed. Concerning the quantification of turbidity, the best results were obtained using averaged data and principal components analyses in conjunction with exponential gaussian process regression, achieving an R2 of 0.979. Regarding the classification of the turbidity, an accuracy of 91.23% is obtained with the fine K-Nearest-Neighbor classifier. The cases in which data were misclassified are characterized by turbidity values lower than 5 NTUs. The obtained results represent an improvement over the current solutions in terms of turbidity quantification and a completely novel approach to turbidity classification.

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“…Among multispectral sensors, our results agree with, 38,39 which state that S2 is better than L8 considering its spatial and spectral ability to identify water pixels and other coverages. In 40,9 the authors used Landsat 8 water spectral indices to analyze the spatio temporal dynamics of the reservoir.…”

Section: Swa and Rwl Relationship And Better Thresholdsmentioning

confidence: 99%

A timely efficient and robust multi-source and multitemporal routine for determination of surface water area in large water reservoirs

Facco¹,

Guasselli²,

Zanotta³

et al. 2021

IJH

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The Brazilian electrical system has gone through conflicts resulting from recent water crisis. Timely indicators are crucial for properly acting in order to mitigate upcoming problems. In this work we evaluate the potential of Reservoir Water Level (RWL) and Surface Water Area (SWA) indices for estimating physical parameters in the management of water resources. We tested Landsat 8 (L8) and Sentinel-2 (S2) optical image time series, Sentinel-1 (S1) radar, spectral indices and validation with Jason-3 (J3) altimetry. The methodology was developed in the Google Earth Engine (GEE) operational routine, which streamlined the SWA mapping. The best results were between S2 and NDWI and threshold 0, with R² = 0.88 and RMSE of 11.59 km². As main limitations, we highlight the cloud cover for the optical images, which can decrease the temporal sampling, as well as the SAR backscatter response in the presence of bare soil and aquatic vegetation. We could attest that periodic remote sensing data are particularly useful for timely updating spatial variations of RWL and SWA in reservoirs.

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Comparison of PBIA and GEOBIA classification methods in classifying turbidity in reservoirs

Cited by 4 publications

References 118 publications

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

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

Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity

A timely efficient and robust multi-source and multitemporal routine for determination of surface water area in large water reservoirs

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