Assessment of Human-Induced Effects on Sea/Brackish Water Chlorophyll-a Concentration in Ha Long Bay of Vietnam with Google Earth Engine

Quang, Nguyen Hong; Nguyen, Minh; Paget, Matt; Anstee, Janet; Viet, Nguyen Duc; Nones, Michael; Tuấn, Vũ Anh

doi:10.3390/rs14194822

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…In each column, the different WQ indicator parameters are predicted by the same models. The chla presented the most sensitive variable and had an ability to be accurately extracted from the Sentinel-2 data, as supported by Quang et al [40]. The TSS was the most divergent parameter from the diagonal blue line, and the COD and DO were moderately correlated with the remote sensing data.…”

Section: Machine Learning Modelling Water Quality Parameters Using Th...supporting

confidence: 55%

“…We generated a spatial SWQ distribution for the entire study area based on the best model regression result of the SWQ parameters with the SSR data in the modelling phase. For water and land separation, we used the normalized different water index (NDWI) by applying a threshold [40]. We used the open-source GIS software QGIS to generate all SWQ maps.…”

Section: Water Quality Mapping Methodsmentioning

confidence: 99%

“…Despite these issues, the RS technique has improved in its abilities in WQ parameter estimates and monitoring, particularly in larger areas. However, due to the complexity of coastal marine ecosystems [39] (with hundreds of WQ parameters), only some of the WQ indicators can be currently extracted using the RS technique [40]. Although the RS applied in this field has a long historical development from statistical regression models (linear/multi-linear regression) and parametric regression methods to current machine learning (ML) models, there are still gaps in this field.…”

Section: Remote Sensing Approaches In Seawater Qualitymentioning

confidence: 99%

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Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh’s Coastal Zone of Vietnam

et al. 2023

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Coastal zones are critically important ecosystems that are closely tied to human activities, such as tourism, urbanization, transport, and aquaculture. However, managing and monitoring sea water in the coastal areas is often challenging due to the diversity of the pollution sources. Traditional approaches of onsite measurement and surveys have limitations in terms of cost, efficiency and productivity compared with modern remote sensing methods, particularly for larger and longer observations. Optical remote sensing imagery has been proven to be a good data source for water quality assessment in general and for seawater studies in particular with the use of advanced techniques of data processing such as machine learning (ML) algorithms. However, optical remote sensing data also have their own disadvantages as they are much affected by climatic conditions, atmospheric gas and particles as a source of noise in the data. This noise could be reduced, but it is still unavoidable. This study aims to model seawater quality parameters (total suspended solids (TSS), chlorophyll-a (chla), chemical oxygen demand (COD), and dissolved oxygen (DO)) along a 134 km sea coastal area of the Binh Dinh province by applying the current robust machine learning models of decision tree (DT), random forest (RF), gradient boosting regression (GBR), and Ada boost regression (ABR) using Sentinel-2 imagery. To reduce the atmospheric effects, we conducted onsite measurements of sea surface reflectance (SSR) using the German RAMSES-TriOS instrument for calibration of the Sentinel-2 level 2A data before inputting them to the ML models. Our modeling results showed an improvement of the model accuracy using calibrated SSR compared with the original Sentinel-2 level 2A SSR data. The RF predicted the most accurate seawater quality parameters compared with in situ field-measured data (mean R2 = 0.59 using original Sentinel-2 level 2A SSR and R2 = 0.70 using calibrated SSR). The chla was the most precise estimate (R2 = 0.74 when modelled by the RF model) flowing by DO, COD and TSS. In terms of seawater quality estimation, this accuracy is at a good level. The results of the seawater quality distributions were strongly correlated with coastal features where higher values of TSS, chla, COD, and DO are near the river mouths and urban and tourist areas. These spatial water quality data could be extremely helpful for local governments to make decisions when the modelling is continuously conducted (using big data processing), and it is highly recommended for more applications.

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Section: Machine Learning Modelling Water Quality Parameters Using Th...supporting

confidence: 55%

Section: Water Quality Mapping Methodsmentioning

confidence: 99%

Section: Remote Sensing Approaches In Seawater Qualitymentioning

confidence: 99%

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Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh’s Coastal Zone of Vietnam

et al. 2023

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Mapping Cropland Extent in Pakistan Using Machine Learning Algorithms on Google Earth Engine Cloud Computing Framework

Latif

Umer

2023

IJGI

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An actual cropland extent product with a high spatial resolution with a precision of up to 60 m is believed to be particularly significant in tackling numerous water security concerns and world food challenges. To advance the development of niche, advanced cropland goods such as crop variety techniques, crop intensities, crop water production, and crop irrigation, it is necessary to examine how cropland products typically span narrow or expansive farmlands. Some of the existing challenges are processing by constructing precision-high resolution cropland-wide items of training and testing data on diverse geographical locations and safe frontiers, computing capacity, and managing vast volumes of geographical data. This analysis includes eight separate Sentinel-2 multi-spectral instruments data from 2018 to 2019 (Short-wave Infrared Imagery (SWIR 2), SWIR 1, Cirrus, the near infrared, red, green, blue, and aerosols) have been used. Pixel-based classification algorithms have been employed, and their precision is measured and scrutinized in this study. The computations and analyses have been conducted on the cloud-based Google Earth Engine computing network. Training and testing data were obtained from the Google Earth Engine map console at a high spatial 10 m resolution for this analysis. The basis of research information for testing the computer algorithms consists of 855 training samples, culminating in a manufacturing field of 200 individual validation samples measuring product accuracy. The Pakistan cropland extent map produced in this study using four state-of-the-art machine learning (ML) approaches, Random Forest, SVM, Naïve Bayes & CART shows an overall validation accuracy of 82%, 89% manufacturer accuracy, and 77% customer accuracy. Among these four machine learning algorithms, the CART algorithm overperformed the other three, with an impressive classification accuracy of 93%. Pakistan’s average cropland areas were calculated to be 370,200 m2, and the cropland’s scale of goods indicated that sub-national croplands could be measured. The research offers a conceptual change in the development of cropland maps utilizing a remote sensing multi-date.

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Assessment of Human-Induced Effects on Sea/Brackish Water Chlorophyll-a Concentration in Ha Long Bay of Vietnam with Google Earth Engine

Cited by 2 publications

References 48 publications

Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh’s Coastal Zone of Vietnam

Calibration of Sentinel-2 Surface Reflectance for Water Quality Modelling in Binh Dinh’s Coastal Zone of Vietnam

Mapping Cropland Extent in Pakistan Using Machine Learning Algorithms on Google Earth Engine Cloud Computing Framework

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