Water quality assessment in a wetland complex using Sentinel 2 satellite images

Torres-Bejarano, Franklin; Arteaga-Hernández, F.; Rodríguez-Ibarra, D.; Avila, Doris Mejia; González-Márquez, Luis Carlos

doi:10.1007/s13762-020-02988-3

Cited by 16 publications

(5 citation statements)

References 42 publications

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“…In channels (43,44,45), the Conv5 output shows blue over right part of lake, getting activated at moderate outbreak probability. Channels (49,50,51) correspond to the upper part of lake near the neck showing a light green haze. Thus, there exists a differentiation over water area marked by yellow in final output prediction map.…”

Section: Discussionmentioning

confidence: 99%

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Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)

Bhatia¹,

Devang²,

Singh³

2021

Preprint

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Avian botulism caused by a bacterium, Clostridium botulinum, causes a paralytic disease in birds often leading to high fatality, and is usually diagnosed using molecular techniques. Diagnostic techniques for confirming the outbreak of Avian botulism include: Mouse Bioassay, ELISA, PCR (based on post-mortem), all of which are time-consuming, laborious and require invasive sample collection from affected sites or dead birds. All these in-vitro techniques are non-preventive and their processing time may further contribute to the bacterial growth in the affected regions. In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i.e. fused satellite images of warm-water lakes/marshy-lands or similar waterbody sites that are important for avian fauna) backed by on-ground reporting evidence of outbreaks. In the current version, BAWD consists of multi-spectral satellite images, covering a total ground area of 904 sq.km from two open source satellite projects (Sentinel and Landsat). BAWD consists of 17 topographically diverse global sites spanning across 4 continents, where locations were monitored over a time-span of 3 years (2016-2020). Using BAWD and state-of-the-art deep-learning techniques we propose a first-ever Artificial Intelligence based (AI) model to predict potential outbreak of Avian botulism called AVI-BoT (Aerosol Visible, Infra-red (NIR/SWIR) and Bands of Thermal). AVI-BoT uses fused multi-spectral satellite images of water-bodies (10-bands) as input to generate a spatial prediction map depicting probability of potential Avian botulism outbreaks. We also train and investigate a simpler (5-band) Causative-Factor model (based on prominent physiological factors reported in literature as conducive for outbreak) to predict Avian botulism. Using AVI-BoT, we achieve a training accuracy of 0.94 and validation accuracy of 0.96 on BAWD, far superior in comparison to our model based on causative factors. For detailed feature-space analysis and explain-ability of the proposed 10-band AVI-BoT model, we further train four additional spectral models on BAWD using: (i) Aerosol, (ii) Visible (3-bands), (iii) IR (3-bands), and (iv) Thermal (3bands). Further, we analyze using AVI-BoT, two recent and highly complex outbreak case-studies: (i) Sambhar Lake (India, November 2019), and (ii) Klamath National Wildlife Refuge (U.S.A., October 2020). The model is found to closely converge with ground-reporting observations at both these sites with a fine-grain spatio-temporal prediction capability. The proposed technique presents a scale-able, low-cost, non-invasive methodology for continuous monitoring of bird-habitats against botulism outbreaks with the potential of saving valuable fauna lives.

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Section: Discussionmentioning

confidence: 99%

“…3. Temperature seems to be have dominant role in the shallow region like upper region of the bigger water body (49,50,51). 5.…”

Section: Discussionmentioning

confidence: 99%

Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)

Bhatia¹,

Devang²,

Singh³

2021

Preprint

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“…La Ciénaga is formed by the dynamics of the Sinú River in the lower area of the basin, between the municipalities of Chimá, Cotorra, Lorica, Momil, and Purísima and represents the basis of the socioeconomic, cultural, ecological, and environmental prosperity of neighboring sectors [22] [27]. Some of the most important activities that take place in the swamps are fishing, agriculture, and livestock; In the same way, the construction of irrigation districts, embankments, and dikes has been carried out, coupled with a population increase, pressures are generated not only on the water resource but also on the ecosystem dynamics between species [26].…”

Section: Study Areamentioning

confidence: 99%

Remote sensing application using Landsat 8 images for water quality assessments

Cruz-Montes

Durango-Banquett

Torres-Bejarano

et al. 2023

J. Phys.: Conf. Ser.

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This study aims to implement remote sensing to determine water quality parameters. Based on the water quality parameters measurements on February 5, 2021, and the LANDSAT 8 satellite images reflectance values, statistical models were generated by the RLM method: Stepwise Regression in Matlab software and digital models through QGIS and ArcGIS Pro GIS. The models obtained for pH, temperature, and turbidity were above 0.6 R2, while dissolved oxygen was above 0.8, showing a good correlation between in situ and estimated data. It is important to avoid cloudy conditions covering the study area because they limit the mathematical models’ application, altering or preventing the results from being generated. The implementation results of this type of technology are of great relevance for future projects, where water quality assessments can be made by using national and international regulations according to the use either for human consumption or for aquatic life protection.

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“…Moderate Resolution Imaging Spectroradiometer images have been used to retrieve salinity [11,30], chlorophyll-a [30][31][32][33], colored dissolved organic material [30,34,35], TSS [31,36], TN [35][36][37][38], and TP [36][37][38]. Sentinel-2 images have been used to retrieve salinity [28,39], chlorophyll-a [39][40][41][42], TN [39,43], TP [39,43], colored dissolved organic material [40][41][42], turbidity [40,41,44], and electrical conductivity (EC) [44]. Thermal infrared data have been used to retrieve water temperature [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Bygate,

Ahmed

2024

Remote Sensing

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Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems.

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Water quality assessment in a wetland complex using Sentinel 2 satellite images

Cited by 16 publications

References 42 publications

Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)

Bird-Area Water-Bodies Dataset (BAWD) and Predictive AI Model for Avian Botulism Outbreak (AVI-BoT)

Remote sensing application using Landsat 8 images for water quality assessments

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

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