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Kieu HT, Pak HY, Trinh HL, Pang DSC, Khoo E, Law AWK. UAV-based remote sensing of turbidity in coastal environment for regulatory monitoring and assessment. MARINE POLLUTION BULLETIN 2023; 196:115482. [PMID: 37864857 DOI: 10.1016/j.marpolbul.2023.115482] [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: 04/23/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 10/23/2023]
Abstract
The adoption of Unmanned Aerial Vehicle (UAV) remote sensing for the regulatory monitoring of turbidity plumes induced by land reclamation operations remains a difficult task. Compared to UAV remote sensing on ambient turbidity in estuaries and rivers, such monitoring of construction-induced turbidity plumes requires significantly higher spatial resolutions and accuracy as well as wider turbidity ranges with nonlinear reflectance. In this study, a pilot-scale deployment of UAV-based hyperspectral sensing is carried out for this objective, with specific new elements developed to overcome the challenges and minimise the uncertainties involved. In particular, Machine learning (ML) models for the turbidity determination were trained by the large dataset collected to better capture the non-linearity of the relationship between the water leaving reflectance and turbidity level. The models achieve a good accuracy with a R2 score of 0.75 that is deemed acceptable in view of the uncertainties associated with construction and land reclamation work.
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Affiliation(s)
- Hieu Trung Kieu
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Hui Ying Pak
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore; Interdisciplinary Graduate Programme, Graduate College, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Ha Linh Trinh
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Dawn Sok Cheng Pang
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Eugene Khoo
- Engineering and Project Management Division, Maritime and Port Authority of Singapore, Singapore 119963, Singapore
| | - Adrian Wing-Keung Law
- Environmental Process Modelling Centre, Nanyang Environment and Water Research Institute, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore; School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.
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S SS, Sunny GM, Sherin CK, Vishnu NNS, Reddy B, Sudheesh V, Prachi M, Kumar S, Vijayan AK, Gupta GVM. Variability of particulate organic carbon and assessment of satellite retrieval algorithms over the eastern Arabian Sea. ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:656. [PMID: 35941250 DOI: 10.1007/s10661-022-10264-9] [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: 03/26/2021] [Accepted: 06/28/2022] [Indexed: 06/15/2023]
Abstract
Particulate organic carbon (POC) and its variability were studied to assess the accuracy of ocean colour retrieval algorithms over the eastern Arabian Sea (EAS) as it controls the carbon sequestration, oxygen minimum zone and biogeochemical (C, N and P) cycles. The seasonality in the physical and biological processes strongly influenced the distribution of POC along the EAS. Higher POC and chlorophyll a (chl a) during the spring inter monsoon (SIM) in the north EAS were due to detrainment bloom. The lower POC:chl a ratios during the winter monsoon (WM) (299.8 ± 190.8) than the SIM (482.1 ± 438.3) were due to the influence of freshly derived organic matter with high nutrient levels. The moderate coefficient of regression values of POC with chl a (R2 = 0.49, N = 59) suggests the importance of dead organic materials in controlling the POC distribution in the EAS. Validation between satellite and in situ POC using the four ocean colour retrieval algorithms showed that the algorithm based on the ratio of remote sensing reflectance (Rrs) performed better (R2 = 0.6, N = 17). It also showed a linear trend of POC with absorption coefficients suggesting it as an optical proxy for the POC retrieval.
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Affiliation(s)
- Shaju S S
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India
- Department of Chemical Oceanography, Cochin University of Science and Technology, Kochi, India
| | | | - C K Sherin
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India
| | - N N S Vishnu
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India
| | - Bikram Reddy
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India
| | - V Sudheesh
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India
- Central University of Kerala, Kasargod, India
| | - M Prachi
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India
| | - Sanjeev Kumar
- Physical Research Laboratory, Department of Space, Ahmedabad, India
| | - Anil Kumar Vijayan
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India.
| | - G V M Gupta
- Centre for Marine Living Resources and Ecology, Ministry of Earth Sciences, Kochi, India
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Satellite Observation of the Long-Term Dynamics of Particulate Organic Carbon in the East China Sea Based on a Hybrid Algorithm. REMOTE SENSING 2022. [DOI: 10.3390/rs14133220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
The distribution pattern and flux variation of POC in the continental shelf seas are essential for understanding the carbon cycle in marginal seas. The hydrodynamic environment and complicated estuarine processes in the East China Sea result in challenging estimates and substantial spatio-temporal variability in terms of POC concentrations. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study to effectively and dynamically monitor the surface POC concentration in the East China Sea in a long-term series for the first time using MODIS/Aqua remote sensing satellite data from 2003 to 2020. A hybrid retrieval model based on the mutual combination of the color index algorithm (CIPOC) and the empirical band ratio algorithm was applied in this study. The MODIS/Aqua remote sensing satellite data from 2003 to 2020 were employed for the first time to dynamically monitor the surface POC concentrations in the East China Sea for a long time series. The results demonstrated that the performance (R2 = 0.84, RMSE = 156.14 mg/m3, MAPE = 43.30%, bias = −64.79 mg/m3) exhibited by this hybrid retrieval algorithm confirms the usability of inversion studies of surface POC in the East China Sea. Different drivers such as river discharge, phytoplankton, wind, and the sea surface current field jointly influence the spatial and temporal distribution of POC concentrations in the East China Sea. This paper also verifies that the hybrid algorithm can be applied to retrieval tasks for POC in different seas with similar optical properties to the waters of the East China Sea. In conclusion, the long-term series East China Sea POC data record, which was established based on MODIS/Aqua, provides supplementary information for in-situ sampling, which will aid the long-term monitoring of POC fluxes in shelf seas. At the same time, it has also improved our understanding of the transport and spatio-temporal variability of POC in the East China Sea, enhancing our comprehension of the impact of POC on environmental changes and carbon cycling in marginal seas.
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Wang H, Liao R, Xiong Z, Wang Z, Li J, Zhou Q, Tao Y, Ma H. Simultaneously Acquiring Optical and Acoustic Properties of Individual Microalgae Cells Suspended in Water. BIOSENSORS 2022; 12:176. [PMID: 35323446 PMCID: PMC8945936 DOI: 10.3390/bios12030176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 03/10/2022] [Accepted: 03/13/2022] [Indexed: 06/14/2023]
Abstract
Microalgae play a vital role in aquatic ecological research, but the fine classification of these tiny and various microalgae cells is still challenging for the community. In this paper, we propose a multimodality technique to simultaneously acquire the polarized light scattering, fluorescence and laser-induced acoustic wave signals originated from individual microalgae cells in water. Experiments of different species of Spirulina and different states of Microcystis have been conducted to test our experiment setup, and the results demonstrate that this method can well discriminate microalgae cells with pigment or microstructural differences. Moreover, with these modalities, the consumption of absorbed energy is evaluated quantitively, and a possible way to assess photosynthesis on a single-cell level is presented. This work is expected to be a powerful technique to probe the biophysical states of microalgae in the aquatic ecosystem.
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Affiliation(s)
- Hongjian Wang
- Shenzhen Key Laboratory of Marine IntelliSensing and Computation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (J.L.)
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China;
| | - Ran Liao
- Shenzhen Key Laboratory of Marine IntelliSensing and Computation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (J.L.)
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China;
| | - Zhihang Xiong
- Department of Photoelectric Technology, Foshan University, Guangzhou 528000, China;
| | - Zhao Wang
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (Z.W.); (Q.Z.); (Y.T.)
| | - Jiajin Li
- Shenzhen Key Laboratory of Marine IntelliSensing and Computation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (H.W.); (J.L.)
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China;
| | - Qian Zhou
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (Z.W.); (Q.Z.); (Y.T.)
| | - Yi Tao
- Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; (Z.W.); (Q.Z.); (Y.T.)
| | - Hui Ma
- Guangdong Research Center of Polarization Imaging and Measurement Engineering Technology, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China;
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