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Wasehun ET, Hashemi Beni L, Di Vittorio CA. UAV and satellite remote sensing for inland water quality assessments: a literature review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:277. [PMID: 38367097 DOI: 10.1007/s10661-024-12342-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/08/2024] [Indexed: 02/19/2024]
Abstract
High spatial and temporal resolution data is crucial to comprehend the dynamics of water quality fully, support informed decision-making, and allow efficient management and protection of water resources. Traditional in situ water quality measurement techniques are both time-consuming and labor-intensive, resulting in databases with limited spatial and temporal frequency. To address these challenges, satellite-driven water quality assessment has emerged as an efficient and effective solution, offering comprehensive data on larger-scale water bodies. Numerous studies have utilized multispectral and hyperspectral remote sensing data from various sensors to assess water quality, yielding promising results. However, the recent popularity of unmanned aerial vehicle (UAV) remote sensing can be attributed to its high spatial and temporal resolution, flexibility, ability to capture data at different times of day, and relatively low cost compared to traditional platforms. This study presents a comprehensive review of the current state of the art in monitoring water quality in small inland water bodies using satellite and UAV remote sensing data. It encompasses an overview of atmospheric correction algorithms and the assessment of different water quality parameters. Furthermore, the review addresses the challenges associated with monitoring water quality in these bodies of water and emphasizes the potential of UAVs to overcome these challenges by providing accurate and reliable data.
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Affiliation(s)
- Eden T Wasehun
- Applied Science and Technology, North Carolina A &T State University, 1601 E Market St, Greensboro, NC, 27411, USA
| | - Leila Hashemi Beni
- Department of Build Environment, North Carolina A &T State University, 1601 E Market St, Greensboro, NC, 27411, USA.
| | - Courtney A Di Vittorio
- Department of Engineering, Wake Forest University, 1834 Wake Forest Rd, Winston-Salem, NC, 27109, USA
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Singh R, Saritha V, Pande CB. Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India. ENVIRONMENTAL RESEARCH 2024; 241:117638. [PMID: 37972812 DOI: 10.1016/j.envres.2023.117638] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/19/2023]
Abstract
Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.
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Affiliation(s)
- Raj Singh
- Department of Environmental Science, GITAM Deemed to be University, Visakhapatnam, 530045, India
| | - Vara Saritha
- Department of Environmental Science, GITAM Deemed to be University, Visakhapatnam, 530045, India.
| | - Chaitanya B Pande
- Indian Institute of Tropical Meteorology, Pune, 411008, India; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah, 64001, Iraq
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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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Mohammed MA, Lakhan A, Abdulkareem KH, Abd Ghani MK, Marhoon HA, Kadry S, Nedoma J, Martinek R, Zapirain BG. Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆. J Adv Res 2023:S2090-1232(23)00297-7. [PMID: 37839503 DOI: 10.1016/j.jare.2023.10.005] [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: 07/08/2023] [Revised: 09/13/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023] Open
Abstract
INTRODUCTION The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity. OBJECTIVE This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met. METHODS We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes. RESULTS Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system. CONCLUSION Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture.
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Affiliation(s)
- Mazin Abed Mohammed
- Department of Artificial Intelligence, College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq; Department of Telecommunications, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic; Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic.
| | - Abdullah Lakhan
- Department of Cybersecurity and Computer Science, Dawood University of Engineering and Technology, Karachi City 74800, Sindh, Pakistan; Department of Telecommunications, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic; Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
| | - Karrar Hameed Abdulkareem
- College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq; College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
| | - Mohd Khanapi Abd Ghani
- Department of Software Engineering, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
| | - Haydar Abdulameer Marhoon
- Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq; College of Computer Sciences and Information Technology, University of Kerbala, Karbala, Iraq
| | - Seifedine Kadry
- Seifedine is with the Department of Applied Data Science, Noroff University College, Kristiansand, Norway
| | - Jan Nedoma
- Department of Telecommunications, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
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Examining the sensitivity of simulated EnMAP data for estimating chlorophyll-a and total suspended solids in inland waters. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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Estimation of River Discharge Using Unmanned Aerial Vehicle (UAV) Based on Manning Formula for an Ungauged Alpine River in the Eastern Qilian Mountains. WATER 2022. [DOI: 10.3390/w14132100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
River discharge is crucial to water resources development and ecological protection. However, in some arid areas of northwest China, it is still difficult to measure discharge accurately. In this study, unmanned aerial vehicle (UAV) imagery has been used to estimate river discharge at two river sections in the upper reaches of the Shiyang River in the eastern part of the Qilian Mountains based on the Manning formula. The estimated discharges at those two sections are 1.16 m3/s and 3.11 m3/s, respectively. Taking the discharges measured by an acoustic Doppler current profiler (ADCP) as the reference, the relative error of the estimates is below 5%, which is accurate enough for water resources management in mountain basin regions. Multiple high-resolution satellite images were also used to calculate water discharges at the two sections, which were in good agreement with the discharges estimated from UAVs. This study demonstrates the feasibility of using UAVs to estimate river discharge, which is of great significance for future regional-scale water resource assessments.
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Abstract
In this paper, the quality of a source of drinking water is assessed by measuring eight water quality (WQ) parameters using 710 samples collected from a water-stressed region of India, Jodhpur Rajasthan. The entire sample was divided into ten groups representing different geographic locations. Using American Public Health Association (APHA) specified methodology, eight WQ parameters, viz., pH, total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), calcium hardness (Ca-H), residual chlorine, nitrate (as NO3−), and chloride (Cl−), were selected for describing the water quality for potability use. The quality of each parameter is examined as a function of the zone. Taking the average parametric values of different zones, a unique number was used to describe the overall quality of water. It was found that the average value of each parameter varies significantly with zones. Further, we used neural network (NN) modeling to map the nonlinear relationship between the above eight parametric inputs and the water quality index as the output. It can be observed that the NN designed in the present work acquired sufficient learning and can be satisfactorily used to predict the relational pattern between the input and the output. It can further be observed that the water quality index (WQI) from this work is highly efficient for a successful assessment of water quality in the study area. The major challenge to uniquely describing the drinking water quality lies in understanding the cumulative effect of various parameters affecting the quality of water; the quantified figure is subjected to debate, and this paper addresses the difficulty through a novel approach. The framework presented in this work can be automated with appropriate equipment and shall help government agencies understand changing water quality for better management.
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