Remote sensing of coastal algal blooms using unmanned aerial vehicles (UAVs)

Synergistic controls of water column stability and groundwater phosphate on coastal algal blooms

Algal blooms have been identified as one major threat to coastal safety and marine ecosystem functioning, but the dominant mechanism regulating the formation of algal blooms remains controversial, ranging from physical control (via water column stability), the chemical control (via coastal nutrients) to joint control. Here we leveraged the unique data collected in the Hong Kong water over the annual cycle and past three decades, including direct observations of algal blooms and coastal nutrients and process model output of water column stability, and evaluated the differential competing hypotheses in regulating algal blooms. Our results demonstrate that the joint mechanism rather than the single mechanism effectively predicts all algal blooms. Meanwhile, we observed that the adequate nutrients (phosphate, PO43-) significantly originate from coastal groundwater. The production and fluctuation of PO43- in beach aquifers are primarily governed by groundwater temperature, leading to a sustained and sufficient supply of PO43- in a low groundwater temperature environment. Furthermore, along with submarine groundwater discharge (SGD), the ongoing release of PO43- in groundwater enters coastal waters and serves as sufficient nourishment for promoting algal blooms in coastal areas. These results highlight the importance of both physical and chemical mechanisms, as well as SGD, in regulating coastal algal blooms. These findings have practical implications for the prevention of coastal algal blooms and provide insights into mariculture, water security, and the sustainability of coastal ecosystems.

Spatiotemporal pattern of coastal water pollution and its driving factors: implications for improving water environment along Hainan Island, China

In the context of human activities and climate change, the gradual degradation of coastal water quality seriously threatens the balance of coastal and marine ecosystems. However, the spatiotemporal patterns of coastal water quality and its driving factors were still not well understood. Based on 31 water quality parameters from 2015 to 2020, a new approach of optimizing water quality index (WQI) model was proposed to quantitatively assess the spatial and temporal water quality along tropical Hainan Island, China. In addition, pollution sources were further identified by factor analysis and the effects of pollution source on water quality was finally quantitatively in our study. The results showed that the average water quality was moderate. Water quality at 86.36% of the monitoring stations was good while 13.53% of the monitoring stations has bad or very bad water quality. Besides, the coastal water quality had spatial and seasonal variation, along Hainan Island, China. The water quality at "bad" level was mainly appeared in the coastal waters along large cities (Haikou and Sanya) and some aquaculture regions. Seasonally, the average water quality in March, October and November was worse than in other months. Factor analysis revealed that water quality in this region was mostly affected by urbanization, planting and breeding factor, industrial factor, and they played the different role in different coastal zones. Waters at 10.23% of monitoring stations were at the greatest risk of deterioration due to severe pressure from environmental factors. Our study has significant important references for improving water quality and managing coastal water environment.

UAV and satellite remote sensing for inland water quality assessments: a literature review

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.

UAV-based remote sensing of turbidity in coastal environment for regulatory monitoring and assessment

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.

A low-cost edge AI-chip-based system for real-time algae species classification and HAB prediction

Harmful Algal Blooms (HAB) are damaging to ecosystem functions and pose challenges to environmental and fisheries management. The key to HAB management and understanding the complex algal growth dynamics is the development of robust systems for real-time monitoring of algae populations and species. Previous algae classification studies mainly rely on the combination of an in-situ imaging flow cytometer and an off-site lab-based algae classification model such as Random Forest (RF) for the analysis of high-throughput images. An on-site AI algae monitoring system on top of an edge AI chip embedded with the proposed Algal Morphology Deep Neural Network (AMDNN) model is developed to achieve real-time algae species classification and HAB prediction. Based on a detailed examination of real-world algae images, dataset augmentation is first performed: consisting of orientation, flipping, blurring, and Resizing with Aspect ratio Preserved (RAP). The dataset augmentation is shown to significantly improve classification performance which is superior to that of the competitive RF model. And the attention heatmaps show that for relatively regular-shaped algal species (e.g., Vicicitus), the model weights the color and texture information heavily; while the shape-related features are more important for complex-shaped algae (e.g., Chaetoceros). The AMDNN is tested on a dataset of 11,250 algae images containing the 25 most common HAB classes in Hong Kong subtropical waters with 99.87% test accuracy. Based on the fast and accurate algae classification, the AI-chip-based on-site system is applied to a one-month dataset in February 2020; the predicted trends of total cell counts and targeted HAB species counts are in good agreement with observations. The proposed edge AI algae monitoring system provides a platform for the development of practical HAB early warning systems that can effectively support environmental risk and fisheries management.

Marine environmental monitoring with unmanned vehicle platforms: Present applications and future prospects

Basic monitoring of the marine environment is crucial for the early warning and assessment of marine hydrometeorological conditions, climate change, and ecosystem disasters. In recent years, many marine environmental monitoring platforms have been established, such as offshore platforms, ships, or sensors placed on specially designed buoys or submerged marine structures. These platforms typically use a variety of sensors to provide high-quality observations, while they are limited by low spatial resolution and high cost during data acquisition. Satellite remote sensing allows monitoring over a larger ocean area; however, it is susceptible to cloud contamination and atmospheric effects that subject the results to large uncertainties. Unmanned vehicles have become more widely used as platforms in marine science and ocean engineering in recent years due to their ease of deployment, mobility, and the low cost involved in data acquisition. Researchers can acquire data according to their schedules and convenience, offering significant improvements over those obtained by traditional platforms. This study presents the state-of-the-art research on available unmanned vehicle observation platforms, including unmanned aerial vehicles (UAVs), underwater gliders (UGs), unmanned surface vehicles (USVs), and unmanned ships (USs), for marine environmental monitoring, and compares them with satellite remote sensing. The recent applications in marine environments have focused on marine biochemical and ecosystem features, marine physical features, marine pollution, and marine aerosols monitoring, and their integration with other products are also analysed. Additionally, the prospects of future ocean observation systems combining unmanned vehicle platforms (UVPs), global and regional autonomous platform networks, and remote sensing data are discussed.

A remotely controlled automated field measurement system for light extinction in coastal waters

The Secchi disk depth (SD) is an important parameter in aquatic ecosystem monitoring. As algal growth depends on solar irradiation, the SD - a measure of light extinction - gives an indirect indication of the chlorophyll concentration. However, most SD measurements are manually based and too sparse to resolve water quality variations during algal blooms. A remotely controlled automatic system for field measurement of light extinction has been developed and installed in three marine fish culture zones in Hong Kong. The visual images of the disk at different prescribed depths and the surrounding water are taken. Based on the contrast theory and image analysis, the recorded light intensity distributions can be analyzed to give the SD and the light extinction coefficient. The method has been extensively verified by field data over a wide range of water quality and hydro-meteorological conditions. The proposed system enables high frequency SD measurements on demand for environmental management and emergency response.

Automated Secchi disk depth measurement based on artificial intelligence object recognition

Water transparency affects the degree of sunlight penetration in water, which is important to many water quality processes. It can be visually measured by lowering a Secchi disk (SD) into water and recording its disappearance depth - the Secchi disk depth (SDD). High frequency SDD measurement is manpower intensive, precluding better understanding of the daily and diurnal variation of water transparency. For the first time, an artificial intelligence based object detection algorithm was employed for the automatic detection of SD from images, mimicking SDD measurement by human eyes. The trained model was validated on a large number of images (about 2000 for a single day in daytime) obtained from a remote-controlled imaging system in a fish farm in a Hong Kong embayment, demonstrating high detection accuracy of 93 %. The work opens up opportunities in the nowcast and forecast of short-term water quality changes (e.g. algal blooms) in coastal waters.

UAV remote sensing applications in marine monitoring: Knowledge visualization and review

With the booming development of information technology and the growing demand for remote sensing data, unmanned aerial vehicle (UAV) remote sensing technology has emerged. In recent years, UAV remote sensing technology has developed rapidly and has been widely used in the fields of military defense, agricultural monitoring, surveying and mapping management, and disaster and emergency response and management. Currently, increasingly serious marine biological and environmental problems are raising the need for effective and timely monitoring. Compared with traditional marine monitoring technologies, UAV remote sensing is becoming an important means for marine monitoring thanks to its flexibility, efficiency and low cost, while still producing systematic data with high spatial and temporal resolutions. This study visualizes the knowledge domain of the application and research advances of UAV remote sensing in marine monitoring by analyzing 1130 articles (from 1993 to early 2022) using a bibliometric approach and provides a review of the application of UAVs in marine management mapping, marine disaster and environmental monitoring, and marine wildlife monitoring. It aims to promote the extensive application of UAV remote sensing in the field of marine research.

Effective coastal Escherichia coli monitoring by unmanned aerial vehicles (UAV) thermal infrared images

Coastal Escherichia coli (E. coli) significantly influence ocean safety and public health, thus requiring an effective E. coli pollution monitoring. However conventional detection relying on manual field sampling is time-consuming. Here, this study established an E. coli estimation model based on thermal remote sensing of unmanned aerial vehicles (UAV). This model was developed against one-year comprehensive field work in a representative sandy beach and further validated against 50 beaches in Hong Kong to evaluate its applicability. The estimated E. coli concentrations were in a reliable agreement with direct measurements. For this model, this study deployed the radon-222 (²²²Rn) as a bridging tracer to couple UAV thermal images and coastal E. coli concentrations. Coastal ²²²Rn can be reflected on the UAV thermal images, and there was a good positive correlation between the ²²²Rn activity and coastal E. coli concentration via one-year field data. Hence, coupling the ²²²Rn activity estimated from UAV thermal images and the relationship between ²²²Rn and E. coli, this study can readily monitor coastal E. coli by UAV. These findings highlighted that UAV technology is an effective approach to measure the E. coli concentrations and can further pave the way for an efficient coastal E. coli monitoring and public health risk warning.

A Framework for Survey Planning Using Portable Unmanned Aerial Vehicles (pUAVs) in Coastal Hydro-Environment

Recently, remote sensing using survey-grade UAVs has been gaining tremendous momentum in applications for the coastal hydro-environment. UAV-based remote sensing provides high spatial and temporal resolutions and flexible operational availability compared to other means, such as satellite imagery or point-based in situ measurements. As strict requirements and government regulations are imposed for every UAV survey, detailed survey planning is essential to ensure safe operations and seamless coordination with other activities. This study established a comprehensive framework for the planning of efficient UAV deployments in coastal areas, which was based on recent on-site survey experiences with a portable unmanned aerial vehicle (pUAV) that was carrying a heavyweight spectral sensor. The framework was classified into three main categories: (i) pre-survey considerations (i.e., administrative preparation and UAV airframe details); (ii) execution strategies (i.e., parameters and contingency planning); and (iii) environmental effects (i.e., weather and marine conditions). The implementation and verification of the framework were performed using a UAV–airborne spectral sensing exercise for water quality monitoring in Singapore. The encountered challenges and the mitigation practices that were developed from the actual field experiences were integrated into the framework to advance the ease of UAV deployment for coastal monitoring and improve the acquisition process of high-quality remote sensing images.

A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.

A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective

Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

Monitoring Cyanobacteria Bloom in Dianchi Lake Based on Ground-Based Multispectral Remote-Sensing Imaging: Preliminary Results

Some lakes in China have undergone serious eutrophication, with cyanobacterial blooms occurring frequently. Dynamic monitoring of cyanobacterial blooms is important. At present, the traditional lake-survey-based cyanobacterial bloom monitoring is spatiotemporally limited and requires considerable human and material resources. Although satellite remote sensing can rapidly monitor large-scale cyanobacterial blooms, clouds and other factors often mean that effective images cannot be obtained. It is also difficult to use this method to dynamically monitor and manage aquatic environments and provide early warnings of cyanobacterial blooms in lakes and reservoirs. In contrast, ground-based remote sensing can operate under cloud cover and thus act as a new technical method to dynamically monitor cyanobacterial blooms. In this study, ground-based remote-sensing technology was applied to multitemporal, multidirectional, and multiscene monitoring of cyanobacterial blooms in Dianchi Lake via an area array multispectral camera mounted on a rotatable cloud platform at a fixed station. Results indicate that ground-based imaging remote sensing can accurately reflect the spatiotemporal distribution characteristics of cyanobacterial blooms and provide timely and accurate data for salvage treatment and early warnings. Thus, ground-based multispectral remote-sensing data can operationalize the dynamic monitoring of cyanobacterial blooms. The methods and results from this study can provide references for monitoring such blooms in other lakes.

Application of Drone Technologies in Surface Water Resources Monitoring and Assessment: A Systematic Review of Progress, Challenges, and Opportunities in the Global South

Accurate and timely information on surface water quality and quantity is critical for various applications, including irrigation agriculture. In-field water quality and quantity data from unmanned aerial vehicle systems (UAVs) could be useful in closing spatial data gaps through the generation of near-real-time, fine resolution, spatially explicit information required for water resources accounting. This study assessed the progress, opportunities, and challenges in mapping and modelling water quality and quantity using data from UAVs. To achieve this research objective, a systematic review was adopted. The results show modest progress in the utility of UAVs, especially in the global south. This could be attributed, in part, to high costs, a lack of relevant skills, and the regulations associated with drone procurement and operational costs. The progress is further compounded by a general lack of research focusing on UAV application in water resources monitoring and assessment. More importantly, the lack of robust and reliable water quantity and quality data needed to parameterise models remains challenging. However, there are opportunities to advance scientific inquiry for water quality and quantity accounting by integrating UAV data and machine learning.

A real time data driven algal bloom risk forecast system for mariculture management

In eutrophic coastal waters, harmful algal blooms (HAB) often occur and present challenges to environmental and fisheries management. Despite decades of research on HAB early warning systems, the field validation of algal bloom forecast models have received scant attention. We propose a daily algal bloom risk forecast system based on: (i) a vertical stability theory verified against 191 past algal bloom events; and (ii) a data-driven artificial neural network (ANN) model that assimilates high frequency data to predict sea surface temperature (SST), vertical temperature and salinity differential with an accuracy of 0.35^oC, 0.51^oC, and 0.58 psu respectively. The model does not rely on past chlorophyll measurements and has been validated against extensive field data. Operational forecasts are illustrated for representative algal bloom events at a marine fish farm in Tolo Harbour, Hong Kong. The robust model can assist with traditional onsite monitoring as well as artificial-intelligence (AI) based methods.

Two-decade variations of fresh submarine groundwater discharge to Tolo Harbour and their ecological significance by coupled remote sensing and radon-222 model

Although submarine groundwater discharge (SGD) comprises an insignificant proportion of the global hydrologic cycle, it contributes significantly to chemical fluxes into the coastal waters due to concentrated constituents in coastal groundwater. Large nutrient loadings derived from SGD can lead to a series of environmental and ecological problems such as algal blooms, resulting in water discoloration, severe dissolved oxygen depletion, and eventually beach closures and massive fish kills. Previous studies have demonstrated the relationship between algal blooms and SGD obtained from direct measurement with seepage meters or from geo-tracer (i.e., radon and radium) based models; these traditional methods are time-consuming, laborious and point monitoring, and can hardly achieve a high spatiotemporal resolution SGD estimation, which is vital in revealing the effects of SGD to algal blooms over a long period. Alternatively, remote sensing methods for high spatiotemporal resolution SGD localization and quantification are applicable and effective. The temperature difference or anomaly between groundwater and coastal water extracted from satellite thermal images can be used as the indicator to localize and detect SGD especially its fresh component (or fresh SGD). In this study, multi-year (2005, 2011 and 2018) radon samples in Tolo Harbour were used to train regression models between in-situ radon (Rn) activity and the temperature anomaly by Landsat satellite thermal images. The models were used to estimate two-decade variations of fresh SGD in Tolo Harbour. The synergistic analysis between the time series of fresh SGD derived from regression models and high spatiotemporal resolution ecological metrics (chlorophyll-a, algal cell counts, and E.coli) leads to the findings that the increase of the fresh SGD associated with high nutrient concentrations is witnessed 10-20 days before the observations of algal bloom events. This study makes the first attempt to demonstrate the strong relation between the SGD and algal blooms over a vicennial span, and also provides a cost effective and robust technique to estimate SGD on a bay scale.