Optical classification of inland waters based on an improved Fuzzy C-Means method

Bi, Shun; Li, Yunmei; Xu, Jie; Liu, Ge; Song, Kaishan; Mu, Meng; Lyu, Heng; Miao, Shihai; Xu, Jiafeng

doi:10.1364/oe.27.034838

Cited by 32 publications

(26 citation statements)

References 53 publications

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“…As changes in natural water are usually continuous, but we are trying to push these into very strict limits, then these lines are expected to happen. In another study, fuzzy logic is often used to blend algorithms for optical water quality parameter algorithms [41,103,104]. Since the OWT classification we used uses similarity calculations, each pixel had a similarity assessment for every OWT.…”

Section: Discussionmentioning

confidence: 99%

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

et al. 2020

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Currently, water monitoring programs are mainly based on in situ measurements; however, this approach is time-consuming, expensive, and may not reflect the status of the whole water body. The availability of Multispectral Imager (MSI) and Ocean and Land Colour Instrument (OLCI) free data with high spectral, spatial, and temporal resolution has increased the potential of adding remote sensing techniques into monitoring programs, leading to improvement of the quality of monitoring water. This study introduced an optical water type guided approach for boreal regions inland and coastal waters to estimate optical water quality parameters, such as the concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM), the absorption coefficient of coloured dissolved organic matter at a wavelength of 442 nm (aCDOM(442)), and the Secchi disk depth, from hyperspectral, OLCI, and MSI reflectance data. This study was based on data from 51 Estonian and Finnish lakes and from the Baltic Sea coastal area, which altogether were used in 415 in situ measurement stations and covered a wide range of optical water quality parameters (Chl-a: 0.5–215.2 mg·m−3; TSM: 0.6–46.0 mg·L−1; aCDOM(442): 0.4–43.7 m−1; and Secchi disk depth: 0.2–12.2 m). For retrieving optical water quality parameters from reflectance spectra, we tested 132 empirical algorithms. The study results describe the best algorithm for each optical water type for each spectral range and for each optical water quality parameter. The correlation was high, from 0.87 up to 0.93, between the in situ measured optical water quality parameters and the parameters predicted by the optical water type guided approach.

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Section: Discussionmentioning

confidence: 99%

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

et al. 2020

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“…Conversely, a CI close to 1 means a high confusion. This confusion may be due to uncertainty from clustering, or this can represent transition areas from a specific spectral class to another one [36]. However, this study focused on seabed pixels that are not expected to be highly time-dependent compared to water pixels.…”

Section: Extensions Of the Methods With Fuzzy Clusteringmentioning

confidence: 99%

“…The clustering step proposed in this study could be improved in future studies by using a real fuzzy-C means (FCM) clustering approach relying on the tuning of the fuzziness parameter [36,37]. The fuzzy maximum likelihood estimation (FMLE) clustering is another method that uses posterior conditional probability instead of Euclidian distance when computing the membership values [33].…”

Section: Extensions Of the Methods With Fuzzy Clusteringmentioning

confidence: 99%

Unsupervised Optical Classification of the Seabed Color in Shallow Oligotrophic Waters from Sentinel-2 Images: A Case Study in the Voh-Koné-Pouembout Lagoon (New Caledonia)

2022

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Monitoring chlorophyll-a concentration or turbidity is crucial for understanding and managing oligo- to mesotrophic coastal waters quality. However, mapping bio-optical components from space in such shallow settings remains challenging because of the strong interference of the complex bathymetry and various seabed colors. Correcting the total satellite reflectance signal from the seabed reflectance in ocean color with high resolution sensors is promising. This article shows how unsupervised clustering approaches can be applied to Sentinel-2 images to classify seabed colors in shallow waters of a tropical oligotrophic lagoon in New Caledonia. Data processing included Lyzenga correction for estimating the water column reflectance, optical spectra standardization for attenuating water absorption effects and clustering using the unsupervised k-means method. This methodological approach was applied on the 497, 560, 664 and 704 nm optical bands of the selected Sentinel-2 image. When applied on non-standardized data, our unsupervised classification retrieved three seafloor clusters, whereas five seafloor clusters could be retrieved using standardized data. For each of these two trials, the computed membership values explained more than 75% of the inertia in each Sentinel-2 wavelength band used for the clustering. However, the accuracy of the method was slightly improved when applied on standardized data. Confusion index mapping of the unsupervised clustering retrieved from these data emphasized the relevance and robustness of our methodological approach. Such an approach for seabed colors classification in optically complex shallow settings will be particularly helpful to improve remote sensing of biogeochemical indicators such as chlorophyll-a concentration and turbidity in fragile coastal environments.

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“…The mean R rs spectra of OWT3 in the visible band were smooth and the magnitude was approximately twice that of other OWTs. This was because the scattering associated with sediment is very strong and masks the spectral fluctuations caused by the absorption of water components [47]. In contrast, the reflection peak of OWT5 was obvious in the visible band.…”

Section: Owts Classified By R Rs Measured In Situmentioning

confidence: 97%

“…Clustering analysis can classify objects with similar attributes into the same type and has been successfully used in case I or II water classification studies. In [47] was proposed a fuzzy clustering algorithm (FCMm) by improving the fuzzy c-means algorithm. Using this method, the fuzzy parameters can be adaptively calculated in accordance with the output dataset to further improve the clustering effect.…”

Section: Construction Of the Owt Librarymentioning

confidence: 99%

Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification

Cai

et al. 2021

Remote Sensing

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With the acceleration of urbanization, increasing water pollution means that monitoring and evaluating urban water quality are of great importance. Although highly accurate, traditional evaluation methods are time consuming, laborious, and vastly insufficient in terms of the continuity of spatiotemporal coverage. In this study, a water quality assessment method based on remote sensing reflectance optical classification and the traditional grading principle is proposed. In this method, an optical water type (OWT) library was first constructed using the measured in situ remote sensing reflectance dataset based on fuzzy clustering technology. Then, comprehensive scoring rules were established by combining OWTs and 12 water quality parameters, and water quality was graded into different urban water quality levels (UWQLs) based on the scoring results. Using the proposed method, the relative water quality of urban waterbodies was qualitatively evaluated at the macro level based on images from the multispectral imager of Sentinel-2. In addition, there was a significant positive correlation between the UWQLs and the water quality index (WQI). These results indicate the potential of this method for quantitative assessment of urban water quality, providing a new way to evaluate water quality using remote sensing algorithms in the future.

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Optical classification of inland waters based on an improved Fuzzy C-Means method

Cited by 32 publications

References 53 publications

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters

Unsupervised Optical Classification of the Seabed Color in Shallow Oligotrophic Waters from Sentinel-2 Images: A Case Study in the Voh-Koné-Pouembout Lagoon (New Caledonia)

Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification

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