1
|
Natarajan L, Vajravelu M, Chandrasekaran M, Ramakrishnan SG, Kaviarasan T, Vipin Babu P, Dash SK, Ramu K, Murthy MVR. Capability of space borne multispectral image for detecting discoloration in optically complex coastal waters. MARINE POLLUTION BULLETIN 2024; 207:116860. [PMID: 39159570 DOI: 10.1016/j.marpolbul.2024.116860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 08/05/2024] [Accepted: 08/13/2024] [Indexed: 08/21/2024]
Abstract
Coastal pollutants, from harmful algal blooms, sewage and industrial discharges, pose severe risks to marine ecosystems and public health. Recently, Promenade Beach in Puducherry, Southeast-India, experienced reddish-brown water discoloration, suspected to result from either algal blooms or suspended matter. This study monitored the spatial extent and characteristics of the discoloration using Sentinel-2 satellite images from September to November 2023, with field observations and laboratory analyses. Analyses included measurements of chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and the Normalized Difference Chlorophyll Index (NDCI) to differentiate between algal blooms and other pollutants. The satellite data indicated extents of discoloration, with high TSM concentrations (>45 g/m3) and negative NDCI values suggesting absence of algal blooms. No mortality of aquatic organisms was observed during this discoloration, indicating no deleterious impact on aquatic life. This approach highlights the importance of combining satellite technology with field data for effective coastal pollution monitoring, essential for protecting marine ecosystems.
Collapse
Affiliation(s)
- Logesh Natarajan
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India.
| | - Manigandan Vajravelu
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India
| | - Muthukumar Chandrasekaran
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India
| | - Sankar Ganesh Ramakrishnan
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India
| | - Thanamegam Kaviarasan
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India
| | - P Vipin Babu
- Puducherry Pollution Control Committee, Puducherry 605005, India
| | - Sisir Kumar Dash
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India
| | - Karri Ramu
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India
| | - M V Ramana Murthy
- National Centre for Coastal Research (NCCR), Ministry of Earth Sciences, NIOT Campus, Velachery-Tambaram Road, Pallikaranai, Tamil Nadu 600100, India
| |
Collapse
|
2
|
Ge Y, Shen F, Sklenička P, Vymazal J, Baxa M, Chen Z. Machine learning for cyanobacteria inversion via remote sensing and AlgaeTorch in the Třeboň fishponds, Czech Republic. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174504. [PMID: 38971250 DOI: 10.1016/j.scitotenv.2024.174504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 06/21/2024] [Accepted: 07/03/2024] [Indexed: 07/08/2024]
Abstract
Cyanobacteria blooms in fishponds, driven by climate change and anthropogenic activities, have become a critical concern for aquatic ecosystems worldwide. The diversity in fishpond sizes and fish densities further complicates their monitoring. This study addresses the challenge of accurately predicting cyanobacteria concentrations in turbid waters via remote sensing, hindered by optical complexities and diminished light signals. A comprehensive dataset of 740 sampling points was compiled, encompassing water quality metrics (cyanobacteria levels, total chlorophyll, turbidity, total cell count) and spectral data obtained through AlgaeTorch, alongside Sentinel-2 reflectance data from three Třeboň fishponds (UNESCO Man and Biosphere Reserve) in the Czech Republic over 2022-2023. Partial Least Squares Regression (PLSR) and three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were developed based on seasonal and annual data volumes. The SVM algorithm demonstrated commendable performance on the one-year data validation dataset from the Svět fishpond for the prediction of cyanobacteria, reflected by the key performance indicators: R2 = 0.88, RMSE = 15.07 μg Chl-a/L, and RPD = 2.82. Meanwhile, SVM displayed steady results in the unified one-year validation dataset from Naděje, Svět, and Vizír fishponds, with metrics showing R2 = 0.56, RMSE = 39.03 μg Chl-a/L, RPD = 1.50. Thus, Sentinel data proved viable for seasonal cyanobacteria monitoring across different fishponds. Overall, this study presents a novel approach for enhancing the precision of cyanobacteria predictions and long-term ecological monitoring in fishponds, contributing significantly to the water quality management strategies in the Třeboň region.
Collapse
Affiliation(s)
- Ying Ge
- Department of Landscape and Urban Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic
| | - Feilong Shen
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic
| | - Petr Sklenička
- Department of Landscape and Urban Planning, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic
| | - Jan Vymazal
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic
| | - Marek Baxa
- ENKI, o.p.s., Dukelská 145, 37901 Třeboň, Czech Republic
| | - Zhongbing Chen
- Department of Applied Ecology, Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic.
| |
Collapse
|
3
|
Tian Y, Deng N, Xu J, Wen Z. A fine-grained dataset for sewage outfalls objective detection in natural environments. Sci Data 2024; 11:724. [PMID: 38956054 PMCID: PMC11219831 DOI: 10.1038/s41597-024-03574-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024] Open
Abstract
Pollution sources release contaminants into water bodies via sewage outfalls (SOs). Using high-resolution images to interpret SOs is laborious and expensive because it needs specific knowledge and must be done by hand. Integrating unmanned aerial vehicles (UAVs) and deep learning technology could assist in constructing an automated effluent SOs detection tool by gaining specialized knowledge. Achieving this objective requires high-quality image datasets for model training and testing. However, there is no satisfactory dataset of SOs. This study presents a high-quality dataset named the images for sewage outfalls objective detection (iSOOD). The 10481 images in iSOOD were captured using UAVs and handheld cameras by individuals from the river basin in China. This study has carefully annotated these images to ensure accuracy and consistency. The iSOOD has undergone technical validation utilizing the YOLOv10 series objective detection model. Our study could provide high-quality SOs datasets for enhancing deep-learning models with UAVs to achieve efficient and intelligent river basin management.
Collapse
Affiliation(s)
- Yuqing Tian
- School of Environment, Tsinghua University, Beijing, 100084, PR China
| | - Ning Deng
- School of Environment, Tsinghua University, Beijing, 100084, PR China
| | - Jie Xu
- Changjiang Basin Ecology and Environment Monitoring and Scientific Research Center, Changjiang Basin Ecology and Environment Administration, Ministry of Ecology and Environment, Wuhan, 430010, China.
| | - Zongguo Wen
- School of Environment, Tsinghua University, Beijing, 100084, PR China.
| |
Collapse
|
4
|
Wang Z, Liu K. Dynamic Evolution of Aquaculture along the Bohai Sea Coastline and Implications for Eco-Coastal Vegetation Restoration Based on Remote Sensing. PLANTS (BASEL, SWITZERLAND) 2024; 13:160. [PMID: 38256714 PMCID: PMC10818457 DOI: 10.3390/plants13020160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/24/2024]
Abstract
The expansion and intensification of coastal aquaculture around the Bohai Sea in China has reduced the tidal flats and damaged the coastal vegetation environment. However, there are few studies on the relationship between the evolution of coastal aquaculture and the variability of coastal vegetation, which limits our understanding of the impact of human activities on the coastal ecosystem. In this study, based on remote sensing technology, we firstly used a combination of a neural network classifier and manual correction to monitor the long-term dynamic changes in aquaculture in the Bohai Sea from 1984 to 2022. We then analyzed its evolution, as well as the relationship between the evolution of coastal aquaculture and the variability of coastal vegetation, in detail. Our study had three main conclusions. Firstly, the aquaculture along the coast of the Bohai Sea showed an expanding trend from 1984 to 2022, with an increase of 538%. Secondly, the spatiotemporal changes in the aquaculture centroids in different provinces and cities varied. The centroid of aquaculture in Liaoning Province was mainly distributed in the Liaodong Peninsula, and moved northwest; that in Hebei Province was distributed in the northeast and moved with no apparent pattern; the centroid of aquaculture in Tianjin was mainly distributed in the southeast and moved westward; and the centroid of aquaculture in Shandong Province was mainly distributed in the northwest and moved in a northwesterly direction. Finally, the expansion of aquaculture of the Bohai Sea has increased the regional NDVI and length of the corresponding coastline, and has made coastlines move toward the sea. Our results provide reliable data support and reference for ecologically managing aquaculture and coastal environmental protection in the Bohai Sea.
Collapse
Affiliation(s)
- Zhaohua Wang
- First Institute of Oceanography, MNR, Qingdao 266061, China;
| | - Kai Liu
- Dongying Research Institute for Oceanography Development, Dongying 257000, China
- Postdoctoral Workstation, National University Science and Technology Park, China University of Petroleum, Dongying 257000, China
| |
Collapse
|
5
|
Zhu X, Guo H, Huang JJ, Tian S, Xu W, Mai Y. An ensemble machine learning model for water quality estimation in coastal area based on remote sensing imagery. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 323:116187. [PMID: 36261960 DOI: 10.1016/j.jenvman.2022.116187] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
The accurate estimation of coastal water quality parameters (WQPs) is crucial for decision-makers to manage water resources. Although various machine learning (ML) models have been developed for coastal water quality estimation using remote sensing data, the performance of these models has significant uncertainties when applied to regional scales. To address this issue, an ensemble ML-based model was developed in this study. The ensemble ML model was applied to estimate chlorophyll-a (Chla), turbidity, and dissolved oxygen (DO) based on Sentinel-2 satellite images in Shenzhen Bay, China. The optimal input features for each WQP were selected from eight spectral bands and seven spectral indices. A local explanation strategy termed Shapley Additive Explanations (SHAP) was employed to quantify contributions of each feature to model outputs. In addition, the impacts of three climate factors on the variation of each WQP were analyzed. The results suggested that the ensemble ML models have satisfied performance for Chla (errors = 1.7%), turbidity (errors = 1.5%) and DO estimation (errors = 0.02%). Band 3 (B3) has the highest positive contribution to Chla estimation, while Band Ration Index2 (BR2) has the highest negative contribution to turbidity estimation, and Band 7 (B7) has the highest positive contribution to DO estimation. The spatial patterns of the three WQPs revealed that the water quality deterioration in Shenzhen Bay was mainly influenced by input of terrestrial pollutants from the estuary. Correlation analysis demonstrated that air temperature (Temp) and average air pressure (AAP) exhibited the closest relationship with Chla. DO showed the strongest negative correlation with Temp, while turbidity was not sensitive to Temp, average wind speed (AWS), and AAP. Overall, the ensemble ML model proposed in this study provides an accurate and practical method for long-term Chla, turbidity, and DO estimation in coastal waters.
Collapse
Affiliation(s)
- Xiaotong Zhu
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Hongwei Guo
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Jinhui Jeanne Huang
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China.
| | - Shang Tian
- College of Environmental Science and Engineering/Sino-Canada Joint R&D Centre for Water and Environment Safety,Nankai University, Tianjin, 300071, PR China
| | - Wang Xu
- Shenzhen Environmental Monitoring Center, Shenzhen, Guangdong, 518049, PR China
| | - Youquan Mai
- Shenzhen Environmental Monitoring Center, Shenzhen, Guangdong, 518049, PR China
| |
Collapse
|