Water Quality in the Ma’an Archipelago Marine Special Protected Area: Remote Sensing Inversion Based on Machine Learning DOI Creative Commons
Zhixin Wang,

Zhenqi Zhang,

Hailong Li

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(10), P. 1742 - 1742

Published: Oct. 3, 2024

Due to the increasing impact of climate change and human activities on marine ecosystems, there is an urgent need study water quality. The use remote sensing for quality inversion offers a precise, timely, comprehensive way evaluate present state future trajectories In this paper, model utilizing machine learning was developed variations in Ma’an Archipelago Marine Special Protected Area (MMSPA) over long-time series Landsat images. concentrations chlorophyll-a (Chl-a), phosphate, dissolved inorganic nitrogen (DIN) sea area from 2002 2022 were inverted analyzed. spatial temporal characteristics these investigated. results indicated that random forest could reliably predict Chl-a, DIN MMSPA. Specifically, Chl-a showed coefficient determination (R2) 0.741, root mean square error (RMSE) 3.376 μg/L, absolute percentage (MAPE) 16.219%. Regarding distribution, parameters notably elevated nearshore zones, especially northwest, contrasted with lower offshore southeast areas. Predominantly, regions higher proximity aquaculture zones. Additionally, nutrients originating land sources, transported via rivers such as Yangtze River, well influenced by activities, have shaped nutrient distribution. Over long term, MMSPA has shown considerable interannual fluctuations during past two decades. As sanctuary, preserving superior healthy ecosystem very important. Efforts protection, restoration, management will demand labor. Remote demonstrated its worth proficient technology real-time monitoring, capable supporting sustainable exploitation resources safeguarding ecological environment.

Language: Английский

Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery DOI Creative Commons
Galen Richardson,

Neve Foreman,

Anders Knudby

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 311, P. 114302 - 114302

Published: July 4, 2024

In aquatic remote sensing, algorithms commonly used to map environmental variables rely on assumptions regarding the optical environment. Specifically, some assume that water is optically deep, i.e., influence of bottom reflectance measured signal negligible. Other opposite and are based an estimation bottom-reflected part signal. These may suffer from reduced performance when relevant not met. To address this, we introduce a general-purpose tool automates delineation deep shallow waters in Sentinel-2 imagery. This allows application for satellite-derived bathymetry, habitat identification, water-quality mapping be limited environments which they intended, thus enhance accuracy derived products. We sampled 440 images wide range coastal locations, covering all continents latitudes, manually annotated 1000 points each image as either or by visual interpretation. dataset was train six machine learning classification models - Maximum Likelihood, Random Forest, ExtraTrees, AdaBoost, XGBoost, neural networks utilizing both original top-of-atmosphere atmospherically corrected datasets. The were trained features including kernel means standard deviations band, well geographical location. A network emerged best model, with average 82.3% across two datasets fast processing time. Higher accuracies can achieved removing pixels intermediate probability scores predictions. made this model publicly available Python package. represents substantial step toward automatic imagery, sensing community downstream users ensure algorithms, such those bathymetry quality, applied only intended.

Language: Английский

Citations

9
Multi-scale Spatial Aware Neural Network Based on Neighboring Information for Inversion of Shallow Water Depth DOI
Z. Li,

Guizhou Zheng

Journal of the Indian Society of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 14, 2025

Language: Английский

Citations

1
Effects of typhoon events on coastal hydrology, nutrients, and algal bloom dynamics: Insights from continuous observation and machine learning in semi-enclosed Zhanjiang Bay, China DOI
Peng Zhang,

Huizi Long,

Zhihao Li

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 924, P. 171676 - 171676

Published: March 12, 2024

Language: Английский

Citations

5
Integrating GIS-Remote Sensing: A Comprehensive Approach to Predict Oceanographic Health and Coastal Dynamics DOI
Ramesh Krishnamoorthy, Kazuaki Tanaka, M. Amina Begum

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Language: Английский

Citations

0
Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network DOI Creative Commons
Yingying Jin, Feng Zhang,

X Q Wang

et al.

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(1), P. 151 - 151

Published: Jan. 16, 2025

In a marine environment, the concentration of chlorophyll is an important indicator quality, which also considered used to predict ecological further means predicting red tide disasters. Although existing methods for have achieved encouraging performance, there are still two limitations: (i) they primarily focus on correlation between variables while ignoring negative noise from non-predictive and (ii) unable distinguish impact that at future time points. order overcome these obstacles, we propose Multi-Attention Collaborative Network (MACN)-based triangle-structured prediction system. particular, MACN consists branch networks, with one named NP-net, focusing variables, other T-net, applied target variable. NP-net incorporates variable-distillation attention eliminate effects irrelevant its outputs as auxiliary information T-net. T-net works variable, both encoder decoder related use output assistance in learning prediction. Two actual datasets experiments, show performs better than various kinds state-of-the-art techniques.

Language: Английский

Citations

0
Interpretable Machine Learning-Based Spring Algal Bloom Forecast Model for the Coastal Waters of Zhejiang DOI
Guoqiang Huang, Min Bao, Zhao Zhang

et al.

Journal of Ocean University of China, Journal Year: 2025, Volume and Issue: 24(1), P. 1 - 12

Published: Jan. 16, 2025

Language: Английский

Citations

0
The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions DOI Creative Commons
Cassia Brocca Caballero, Vitor S. Martins, Rejane S. Paulino

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113244 - 113244

Published: Feb. 21, 2025

Language: Английский

Citations

0
Developing a real-time water quality simulation toolbox using machine learning and application programming interface DOI

Gi-Hun Bang,

Na-Hyeon Gwon,

Min‐Jeong Cho

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124719 - 124719

Published: Feb. 28, 2025

Language: Английский

Citations

0
Investigation of Water Quality in Izmir Bay With Remote Sensing Techniques Using NDCI on Google Earth Engine Platform DOI Creative Commons
Osman Salih Yılmaz, Uğur Acar, Füsun Balık Şanlı

et al.

Transactions in GIS, Journal Year: 2025, Volume and Issue: 29(1)

Published: Jan. 12, 2025

ABSTRACT In this study, the effects of algal blooms occurring in Izmir Bay summer 2024 on marine ecosystems were investigated using remote sensing techniques Google Earth Engine platform. The normalized difference chlorophyll index (NDCI) was calculated from January to end September and chlorophyll‐a density analyzed. Additionally, an NDCI time series analysis conducted between 2018 at designated points. values, which fluctuated narrowly until 2022, showed a sharp increase 2024. NDCI, vary −0.4 0.2 up 0.8 toward months, indicate that are occurring, concentrated critical areas such as Karşıyaka, Bayraklı, Alsancak Port. These findings revealed connection sudden fish deaths bay during blooms, well deterioration water quality.

Language: Английский

Citations

0
Predictive Modeling of Cyanobacterial Blooms and Diurnal Variation Analysis Based on GOCI DOI Open Access
Chuanxiu Luo, Xiang Wang, Yuan Chen

et al.

Water, Journal Year: 2025, Volume and Issue: 17(5), P. 749 - 749

Published: March 4, 2025

Algal bloom is a major ecological and environmental problem caused by abnormal algal reproduction in water, it poses serious threat to the aquatic ecosystem, drinking water safety, public health. Because of high dynamic spatiotemporal heterogeneity outbreaks, process often presents significant changes short time. Therefore, has important scientific research value practical application significance construct an accurate effective warning model. This study constructs integrated model combining sequence features, attention mechanisms, random forest using machine learning algorithms for prediction, based on watercolor geostationary satellite observations meteorological data from GOCI South Korea. In process, spatial resolution Sentinel-2 also utilized sample extraction. With 10-m resolution, provides more precise information compared 500-m GOCI, which significantly enhances accuracy model, especially monitoring local body changes. The experimental results demonstrate that exhibits excellent stability prediction blooms. average AUC 0.88, F1 score 0.72, 0.79 when identifying change hourly scale. At same time, this summarized four typical diurnal modes effluent bloom, including dispersal mode, persistent outbreak dispersal-regression subsidence revealing main characteristics bloom. provided strong technical support environment quality safety management showed good prospect.

Language: Английский

Citations

0