Marine Environmental Monitoring Research Articles

Near-real-time atmospheric and oceanic science products of Himawari-8 and Himawari-9 geostationary satellites over the South China Sea

Abstract. The initial release of near-real-time (NRT) atmospheric and oceanic science products from Japanese Himawari-8 and Himawari-9 (H8/9) geostationary (GEO) satellites over the South China Sea (SCS) was unveiled in 2024. The primary objective behind crafting these NRT H8/9 satellite products is to facilitate weather and marine environment monitoring, enhance maritime security, and aid ocean navigation, among other purposes. As part of this investigation, a novel NRT data processing system was devised to generate a variety of regional H8/9 GEO satellite science products within a resolution of 10 min and a gridded resolution of 0.05° × 0.05° from 3 November 2022 to the present. This algorithm system was built upon the preceding Fengyun (FY) geostationary satellite algorithm test bed (FYGAT), which was the prototype of the FY-4 GEO meteorological satellite science product operational processing system. These regional H8/9 GEO satellite science products encompass a range of crucial data such as cloud mask, fraction, height, phase, optical, and microphysical properties; layered precipitable water; and sea surface temperature. We subjected these products to rigorous evaluations against high-quality analogous satellite products and reanalysis data spanning 1 year in 2023. The validations underscore a strong consistency between the H8/9 GEO satellite atmospheric and oceanic science products over the SCS and the referenced products. Nevertheless, slight discrepancies in these satellite science products were identified, primarily stemming from variations in sensor/dataset characteristics, retrieval algorithms, and geometric conditions. These outcomes demonstrate the suitability of the first edition of NRT atmospheric and oceanic science products of H8/9 satellites over the SCS in supporting the intended quantitative applications. This NRT GEO satellite data record is publicly accessible through the File Transfer Protocol (FTP) provided by the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) in China. Free access to the dataset is possible via https://doi.org/10.6084/m9.figshare.25015853 (Liu et al., 2024).

Fishes Associated with a Vulnerable Marine Ecosystem Network in the Central Mediterranean Sea

In order to collect information on ichthyofauna of a deep-sea vulnerable marine ecosystem (VME) network along the Apulian margin (central Mediterranean Sea), two low-impact sampling tools were used in three VMEs characterized by cold-water corals (CWC), namely Bari Canyon (BC), off Monopoli (Mn), and off Santa Maria di Leuca (SML). Using an experimental longline, 53 deployments were carried out between a 314 and 650 m depth for a total of 217 fishing hours, whereas when using the baited lander MEMO (Marine Environment MOnitoring system), 31 deployments were carried out between 427 and 792 m, for a total of 223 h of video recordings. A total of 37 taxa were recorded, comprising 13 Chondrichthyes and 24 Osteichthyes. The similarities in species observed among the VMEs confirm the presence of a network of CWC-VMEs along the Apulian margin, whereas some differences detected are due to the different abundance of some species, such as Galeus melastomus, Helicolenus dactylopterus, and Phycis blennoides. The presence of commercial species, vulnerable/endangered cartilaginous fishes, and large and sexually mature individuals of G. melastomus, H. dactylopterus, and Pagellus bogaraveo in all the VMEs confirms that the network of CWC-VMEs along the Apulian margin can act as a network of refuge areas and an essential fish habitat (EFH) for species threatened by fishing activities.

Self-Enhanced Electrochemiluminesence of Dye-Doped Polymer Dots for Coreactant-Free Visualized Detection of Iodide Ions.

The monitoring of radioactive iodide levels is of great significance in environmental science and cancer radiotherapy. In this work, a high-throughput, radiation-resistant, and visualized electrochemiluminescence (ECL) strategy was developed for detection of iodide ions. Herein, the hydrophobic ruthenium derivative (Ru(bpy)3[B(C6F5)4]2) (bpy = bipyridyl) was doped in tertiary amine-coupled polymer dots (N-PFO Pdots) to synthesize self-enhanced Pdots (Ru@Pdots), which showed extremely high ECL intensity in absence of coreactant. Due to the efficient ECL resonance energy transfer between Ru(bpy)3[B(C6F5)4]2 and N-PFO, the Ru@Pdots exhibited 18 times higher ECL intensity compared with bare N-PFO Pdots. Besides, Ru@Pdots also showed 220-times higher ECL intensity compared with Ru(bpy)3[B(C6F5)4]2 doped coreactant-dependent Pdots (Ru@PFO Pdots). Using Ru@Pdots as ECL emitters, an ECL imaging array was designed for iodide ion detection, which exhibited a detection range of 0.8 nM-4 μM and a limit of detection of 0.1 nM. In this strategy, iodide ions were oxidized as iodide free radicals on the surface of the electrode, which could further consume the nitrogen radical of Ru@Pdots and effectively quench the ECL signal. This method also showed good specificity, radiation-resistant performance, and accuracy in actual seawater sample testing, which indicated its value in marine environmental monitoring, nuclear security, and cancer radiotherapy.

MCLORA: Maritime ad-hoc communication system based on LORA

Maritime communication plays a crucial role in fields such as ocean resource exploration and marine environmental monitoring. Existing maritime communication methods either face challenges in equipment deployment or are limited by high power requirements, making sustained operation difficult. The emergence of LoRa presents an opportunity in this regard, with its characteristics of low power consumption and long communication range, meeting the demands for long-term maritime communication. However, LoRa’s underlying implementation is not open-source, and LoRaWAN itself adopts a star topology, limiting communication between nodes. Therefore, we have devised a communication packet header working at the application layer to enable peer-to-peer communication between nodes. Our on-campus field tests have shown that our system can achieve node-to-node communication, networking functionalities, with a packet delivery rate more than 94%, and max data transmission rate can achieve 1027 bps. In the sea test, the communication rate of our node remained basically around 1035 bps due to the absence of objects blocking the line of sight, and packet delivery rate was more than 96%.The byte error rates of all experiments were less than 0.5%.

Artificial Intelligence-Assisted Environmental DNA Metabarcoding and High-Resolution Underwater Optical Imaging for Noninvasive and Innovative Marine Environmental Monitoring

To effectively protect the marine environment, it is crucial to establish effective environ mental monitoring platforms. Traditional marine environmental monitoring methods heavily rely on morphological identification and field expertise, with the sampling process being disruptive and potentially destructive to vulnerable marine environments. In light of emerging biomonitoring needs and biodiversity declines, we reviewed the urgently needed, ongoing advances in developing effective, noninvasive, and innovative monitoring methods and systems to examine the complex marine environment for better strategic conservation and protection, using the coral ecosystem as one of the representative forefront examples in marine protection. This review summarizes current trends and efforts in transitioning into more standardizable and automatable utilizations of environmental DNA metabarcoding-based monitoring strategies and high-resolution underwater optical imaging monitoring systems as two of the promising pillars for the next generation of noninvasive biomonitoring and associated applications. The assistance of artificial intelligence for environmental DNA metabarcoding and high-resolution underwater optical imaging into an empowered, all-rounded monitoring platform for enhanced monitoring capacity is discussed as a highly potent direction for future research exploration. This review will be a cornerstone reference for the future development of artificial intelligence-assisted, noninvasive, and innovative marine environmental monitoring systems.

Impact of the Fujiwhara effect from tropical cyclones Seroja and Odette on ocean dynamic in Southern Indonesia: Insights from argo data and model analysis

Tropical cyclone (TC) disturbances can cause changes in ocean dynamics before, during, and afterwards. We employed an Argo data and ocean model product of Copernicus Marine Environment Monitoring Service (CMEMS) to address the gap in observations of the ocean response from the severe TC Seroja and its interactions with the tropical low TC Odette, known as the rare Fujiwhara effect phenomenon. Our study in southern Indonesia seawater was conducted between March and April 2021, beginning with reconciling the deviation of the CMEMS ocean model product by assessing the model accuracy to the Argo float data to build confidence in using the model for further analysis. Comparison revealed that the temperature model datasets have a higher correlation coefficient (∼0.95) with the Argo observation than the salinity model datasets. Further, we investigated the temporal evolution of vertical profiles and upper ocean responses both inside and beyond the TCs route zone. We discovered that the upper ocean response results in a colder and saltier surface with a recovery time of more than 15 days. Furthermore, as a result of the strong surface cooling, mixed layers inside the Seroja track were rapidly thickening following the passage of TC. Our findings provide insights into how the TCs Seroja and Odette affect the ocean layers, especially the primary ocean features and behaviours (temperature and salinity), using the Argo and model datasets.

Strategic deployment in the deep: Principled underwater sensor placement optimization with three-dimensional acoustic map.

Underwater acoustic sensors are vital for monitoring marine environments and detecting targets, but their optimal placement presents challenges, particularly in deep-sea environments. This paper addresses the question of determining the optimal sensor placement in a specific ocean region through a principled optimization approach. While previous studies mainly utilized heuristic algorithms without exploiting problem-specific structures, this work explores leveraging the complex three-dimensional acoustic environment through principled modeling and tailored optimization. Specifically, intricate three-dimensional multi-directional acoustic maps are constructed for each sensor. Based on these maps, the sensor placement problem is then cast as an integer linear programming, allowing the study to leverage established theoretical results from operations research. Additionally, an alternative algorithm with its performance indicator is presented to find near-optimal solutions efficiently and can empirically reach over 99% coverage of the optimal solution. Experimental results using real-life data from the South China Sea demonstrate the effectiveness of the proposed approach in achieving much larger detection coverage compared to random and empirical strategies. Notably, the alternative fast algorithm approaches the optimal solution in significantly less time. Furthermore, experiments show that any further simplification of this approach leads to the performance degradation.

Research on Multiple AUVs Task Allocation with Energy Constraints in Underwater Search Environment

The allocation of tasks among multiple Autonomous Underwater Vehicles (AUVs) with energy constraints in underwater environments presents an NP-complete problem with far-reaching consequences for marine exploration, environmental monitoring, and underwater construction. This paper critically examines the contemporary methodologies and technologies in the task allocation for multiple AUVs, with a particular focus on strategies that optimize navigation time with energy consumption constraints. By conceptualizing the multiple AUVs task allocation issue as a Capacitated Vehicle Routing Problem (CVRP) and addressing it using the SCIP solver, this study seeks to identify effective task allocation strategies that enhance the operational efficiency and minimize the mission duration in energy-restricted underwater settings. The findings of this research provide valuable insights into efficient task allocation under energy constraints, providing useful theoretical implications and practical guidance for optimizing task planning and energy management in multiple AUVs systems. These contributions are demonstrated through the improved solution quality and computational efficiency.

Sea clutter prediction based on fusion of Fourier transform and graph neural network

ABSTRACT Radar is a crucial tool for remote sensing and monitoring of marine environments. However, its effectiveness is significantly influenced by sea clutter. The complex interplay between radar parameters and various maritime environmental factors gives rise to a dynamic and intricate sea clutter pattern. The conventional approach to sea clutter prediction only considers the temporal dependence, neglecting the spatial changes. To address this limitation, this study proposes the Fusion of Fourier Transform and Graph Neural Network (FFTaGNN) to enhance the accuracy of multi-dimensional sea clutter data forecasting. FFTaGNN captures the correlations and time dependencies among sequences in the spectral domain. By combining the discrete Fourier transform (DFT) and graph Fourier transform (GFT), it extracts the temporal correlation characteristics and establishes correlations between multidimensional sea clutter data sequences. Importantly, FFTaGNN can automatically discover data correlations between sequences without relying on predetermined priors. To validate the effectiveness of the model, an experimental verification process is conducted, considering different grazing angles and sea clutter High Range Resolution Profile (HRRP) data. The results of the experiment demonstrate that the proposed strategy achieves a minimum Root Mean Square Error (RMSE) of 0.0574 in predicting sea clutter HRRP data. This technique holds great potential in effectively suppressing sea clutter, thereby enhancing the overall performance of radar systems in marine environments and small target detection capabilities at the sea surface.

Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System

Over the past few decades, the oceans have stored the majority of the excess heat in the climate system resulting from anthropogenic emissions. An accurate, long-term sea surface temperature (SST) dataset is essential for monitoring and researching the changes to the global oceans. A variety of SST datasets have been produced by various institutes over the years, and here, we present a new SST data record produced originally within the Copernicus Marine Environment Monitoring Service (which is therefore named CMEMS v2.0) and assess: (1) its accuracy compared to independent observations; (2) how it compares with the previous version (named CMEMS v1.2); and (3) its performance during two major volcanic eruptions. By comparing both versions of the CMEMS datasets using independent in situ observations, we show that both datasets are within the target accuracy of 0.1 K, but that CMEMS v2.0 is closer to the ground truth. The uncertainty fields generated by the two analyses were also compared, and CMEMS v2.0 was found to provide a more accurate estimate of its own uncertainties. Frequency and vector analysis of the SST fields determined that CMEMS v2.0 feature resolution and horizontal gradients were also superior, indicating that it resolved oceanic features with greater clarity. The behavior of the two analyses during two volcanic eruption events (Mt. Pinatubo and El Chichón) was examined. A comparison with the HadSST4 gridded in situ dataset suggested a cool bias in the CMEMS v2.0 dataset versus the v1.2 dataset following the Pinatubo eruption, although a comparison with sparser buoy-only observations yielded less clear results. No clear impact of the El Chichón eruption (which was a smaller event than Mt. Pinatubo) on CMEMS v2.0 was found. Overall, with the exception of a few specific and extreme events early in the time series, CMEMS v2.0 possesses high accuracy, resolution, and stability and is recommended to users.

Highly transparent inorganic superhydrophilic coatings with sustainable anti-fogging and mechanically robust underwater anti-oil-fouling capabilities

Inorganic coatings with high transparency and superhydrophilicity have attracted tremendous attention because of their potential applications in marine environment monitoring, and oily wastewater treatment. However, the transparency and superhydrophilicity are usually mutually exclusive. Herein, we present a new facile strategy for fabricating highly transparent and mechanically robust superhydrophilic inorganic coatings with outstanding anti-fogging ability and anti-oil-fouling performance for high-viscosity oils. In this strategy, amorphous calcium phosphate (ACP) coatings are mineralized on the surface of polydopamine and polyethylenimine (PDA/PEI) membranes under the synergistic effect of magnesium ions, citric acid, and poly(acrylic acid) at room temperature, forming an organic-inorganic composite structure with a uniform texture on various substrates. Owing to the strong hydration capacity of ACP, these coatings are highly efficient at preventing surface fogging and avoiding underwater crude oil fouling. Meanwhile, the ACP coating without grain boundaries or pores can reduce light scattering, thus maintaining the high transparency of the substrate. Moreover, the organic-inorganic composite structure endows the coatings with robust mechanical properties. This PDA/PEI-ACP mineralized coating can be deposited on various substrates, showing potential applications in underwater optical lenses, oily wastewater treatment, microfluidic devices, and other areas.

Underwater image restoration method based on Walsh–Hadamard transform and attenuation coefficient estimation

Abstract Underwater images often exhibit color distortion and low contrast due to the scattering and absorption of light as it travels through water. Changes in lighting conditions further complicate the restoration and enhancement of these images. Improving the quality of underwater images is crucial for advancements in fields such as marine biology research, underwater measurement, and environmental monitoring. This paper proposes an underwater image restoration method based on the Image Formation Model (IFM), utilizing the Walsh–Hadamard transform and attenuation coefficient estimation. Traditional methods rely on dark channel prior and maximum intensity prior to estimate background light (BL) and transmission maps (TMs), often performing poorly in various underwater environments. Our method uses image blur to estimate BL and depth maps and derives three-channel attenuation coefficients using the gray-world theory to obtain a more accurate TM. Experimental results on real underwater images show that our method effectively eliminates color deviation and contrast distortion while preserving image details, significantly outperforming other IFM-based restoration techniques. Compared to the closest competing algorithms, our method achieves better UIQM and UCIQE scores.

Assessment of heavy metals and proximate composition in jellyfish (Lobonemoides robustus Stiasny, 1920) collected from Cox's Bazar coast: Human health risk assessment

Jellyfish are known for experiencing periodic blooms in population, which occur when their density increases suddenly. The present study assessed the level of heavy metals and proximate composition in the jellyfish Lobonemoides robustus collected from Cox's Bazar coast of Bangladesh. This is the first study conducted in Bangladesh. Most of the studied metals were not possible to detect in L. robustus samples because concentrations were below the detection limit. Ca, Na, Se, and Mg were found to have safe levels in the L. robustus while the amount of Pb was recorded 0.39 ppm. The findings of Target Hazard Quotient, and Carcinogenic Risk indicate that the L. robustus is safe for human consumption (both for children and adults). Hence, it is suitable for consumption and can be exported. This study emphasizes the need for regular marine environment monitoring to ensure that the seafood harvested from these waters is safe for consumption.

Investigating the influence of geometry on the performance of a wave-powered marine observation buoy: The wave-to-wire approach

As an important marine monitoring platform, marine observation buoys have been widely used in marine environmental monitoring, marine resource surveys, etc., attracting great attention worldwide. However, the lack of a reliable power supply is a crucial problem that seriously restricts buoys’ operation lifetime and carrying capacity. The floating buoy with an inbuilt wave energy harvester is a promising solution to the long-term and sustainable energy supply problem. This study develops a fully coupled wave-to-wire (W2W) mathematical model where the relevant friction parameters and power-take-off system parameters are identified from flume experimental results. Besides, the influence of geometry on the buoy’s electric power performance is investigated under regular and irregular waves, respectively. Numerical results imply that the cylinder-cone-shape buoy produces the most electric power when the three shapes have the same geometric sizes, while the cylinder-sphere-shape buoy performs the best W2W efficiency among the three shapes with the same mass. Moreover, a high W2W efficiency of up to 23.13% is gained under a regular wave. Finally, according to the similarity theory, the averaged electric power of a full-size buoy is estimated at 4.84kW in realistic sea states.

Catch and characteristics of Decapterus sp. fishing areas in The Southern Waters of East Java - Bali

Abstract One of the essential potential resources of small pelagic fish to the livelihood of fishermen is Decapterus sp. The purpose of this study was to examine the catch and features of the Decapterus sp fishing region in Indian Ocean waters from East Java to Bali. The fisheries data for Decapterus sp during 2019 - 2021 were provided by Ministry of Maritime Affairs and Fisheries, Republic of Indonesia. The chl-a, and SST data were downloaded from Copernicus Marine Environment Monitoring Service (https://marine.copernicus.eu/). The fisheries and environmental data were then grouped by season. The results showed that the highest catch per unit efforts (CPUE) value occurred during transition 2 season with value of 2.89 followed by west season with value of 2.65. The smallest value of CPUE occurred during transition 1 (1.98). In general, most of Decapterus sp were caught in SST range between 27 – 29°C and chl-a range between 0.1 – 0.3 mg/m3. These results can be used as a basis for management of Decapterus sp especially in southern waters of East Java - Bali.

Adaptive Path Planning for Subsurface Plume Tracing with an Autonomous Underwater Vehicle

Autonomous underwater vehicles (AUVs) have been increasingly applied in marine environmental monitoring. Their outstanding capability of performing tasks without human intervention makes them a popular tool for environmental data collection, especially in unknown and remote regions. This paper addresses the path planning problem when AUVs are used to perform plume source tracing in an unknown environment. The goal of path planning is to locate the plume source efficiently. The path planning approach is developed using the Double Deep Q-Network (DDQN) algorithm in the deep reinforcement learning (DRL) framework. The AUV gains knowledge by interacting with the environment, and the optimal direction is extracted from the mapping obtained by a deep neural network. The proposed approach was tested by numerical simulation and on a real ground vehicle. In the numerical simulation, several initial sampling strategies were compared on the basis of survey efficiency. The results show that direct learning based on the interaction with the environment could be an appropriate survey strategy for plume source tracing problems. The comparison with the canonical lawnmower path used in practice showed that path planning using DRL algorithms could be potentially promising for large-scale environment exploration.

Enhancing Underwater Object Detection and Classification Using Advanced Imaging Techniques: A Novel Approach with Diffusion Models

Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object detection and classification tasks. This study utilizes the TrashCan dataset, comprising diverse underwater scenes and objects, to validate the proposed method’s efficacy. This study proposes an advanced imaging technique YOLO (you only look once) network (AIT-YOLOv7) for detecting objects in underwater images. This network uses a modified U-Net, which focuses on informative features using a convolutional block channel and spatial attentions for color correction and a modified swin transformer block for resolution enhancement. A novel diffusion model proposed using modified U-Net with ResNet understands the intricate structures in images with underwater objects, which enhances detection capabilities under challenging visual conditions. Thus, AIT-YOLOv7 net precisely detects and classifies different classes of objects present in this dataset. These improvements are crucial for applications in marine ecology research, underwater archeology, and environmental monitoring, where precise identification of marine debris, biological organisms, and submerged artifacts is essential. The proposed framework advances underwater imaging technology and supports the sustainable management of marine resources and conservation efforts. The experimental results demonstrate that state-of-the-art object detection methods, namely SSD, YOLOv3, YOLOv4, and YOLOTrashCan, achieve mean accuracies (mAP@0.5) of 57.19%, 58.12%, 59.78%, and 65.01%, respectively, whereas the proposed AIT-YOLOv7 net reaches a mean accuracy (mAP@0.5) of 81.4% on the TrashCan dataset, showing a 16.39% improvement. Due to this improvement in the accuracy and efficiency of underwater object detection, this research contributes to broader marine science and technology efforts, promoting the better understanding and management of aquatic ecosystems and helping to prevent and reduce the marine pollution, as emphasized in SDG 14.

Unraveling the declining Indian ocean primary productivity and key drivers

Climate change severely affects the third-largest maritime regions, disrupting physical and biogeochemical processes and Ocean Net Primary Productivity (NPP), which affects the marine food web, fisheries, and livelihoods of surrounding regions. This study examined changes in NPP in the Indian Ocean and its drivers. The Ocean warming, equatorial and Pacific warm pools, stratification fluctuations, circulation changes, nutrient transport, monsoons, and different seasonal processes significantly influenced these microbial mats. The Copernicus Marine Environment Monitoring Service (CMEMS) reanalysis data shows a high declining NPP trend in the Indian Ocean (-0.85 mgC/m3/day/yr). The high warming rate (0.01–0.02 °C/yr) primarily drives regional NPP alterations. The causal discovery analysis shows that sea surface temperature (SST) provides high negative feedback to NPP during all-time delays. Nitrate and phosphate show a positive association, reinforcing positive feedback, and Redfield stoichiometry holds paramount significance in fostering the NPP. Negative feedback is observed from the Western Pacific Index (WP), Oceanic Niño Index (ONI), Multivariate ENSO Index (MEI), and Pacific Decadal Oscillation (PDO). This climate pattern can modify mesoscale atmospheric circulation, influence currents, and change temperature and nutrient availability. The positive connection between the Southern Oscillation Index (SOI) and NPP underscores the complex interplay between climate patterns in the region. The Indian Ocean Dipole (IOD) negatively feedbacks the NPP. Various other climate patterns and events indirectly affect IO biogeochemical processes.

Radioecological perspective: The concentration of natural radionuclides around the coast of PLTU Labuan

The utilization of coal in Power Plants (PLTUs) can lead to the generation of fly ash waste, which may contain natural radionuclides that can potentially contaminate the surrounding environment. Despite Labuan PLTU's land-coastal location, marine environment monitoring remains minimal. A recent study conducted sediment and seawater sampling, revealing varying levels of natural radionuclides 226Ra, 232Th, and 40K ranging from 3.4 to 27.5 Bq kg−1, 3.9 to 34.9 Bq kg−1, and 64.0 to 275.0 Bq kg−1 in sediments, respectively. Meanwhile, the activity concentration of natural radionuclides 226Ra and 232Th in seawater ranges from 0.5 to 1.1 Bq L−1 to 0.7–1.5 Bq L−1, respectively. These findings provide baseline data for marine radiation levels around PLTU Labuan. Additionally, risk assessments were conducted, indicating Raeq, Hin, and Hex ranges of 13.80–91.00 Bq kg−1, 0.04–0.25, and 0.05–0.31, respectively. The study concludes that Labuan's radiation levels are within safe limits for exposure.

Shorebirds: Threats and Strategies for Conservation

Shorebirds or waders inhabit in the coastal littoral zone and play critical role in the marine ecosystems and environmental monitoring. Ecological services provided by shorebirds include facilitating energy and nutrient transfer; enrich soils to increase biodiversity along with primary and secondary production. Shorebirds are used as indicators of pollution, environmental change, food chain, and climate change. These birds are vulnerable to hunting pressure, habitat changes, disturbance and other threats. Disturbance to roosting or feeding shorebirds is a major concern along coastlines. Shorebirds can adapt to regular disturbances at predictable distances; but some loud, unpredictable disturbances such as shouting, people running, jet skis, helicopters, kite boards, fast and noisy boats, and dogs are the most threatening to them. Anthropogenic stressors such as chemical pollution, fishing, habitat loss, invasive alien species/introduced species, marine debris, oil spills, poaching, and shipping pose threats to the shorebirds. Human activities can affect the shorebirds, either directly or by producing behavioural or physiological impacts. Present review attempts to provide an illustrative and updated account of shorebirds relevant to current status; natural, anthropogenic, and emerging and under-studied threats; and strategies to be adopted for better conservation and restoration of shorebirds. This study suggests that about threats to shorebirds, future research priorities should be given to assess the cumulative effects of anthropogenic stressors on shorebirds and to recommend effective preventive measures.