Area Monitoring Research Articles

Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning

Detecting surface deformation associated with low-magnitude (Mw≤5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the Mw=3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.

Implementation Of Water Quality Index For Measuring Groundwater Quality

Groundwater is one of the major sources of water consumption in developing countries. The aim of the study was to investigate the variations in physicochemical characteristics and metal content and to calculate the Water Quality Index (WQI) to assess the groundwater quality of seven districts (Central, East, West, South, Malir, Korangi, and Kemari) of Karachi City, Pakistan. A stratified sampling approach was used to collect groundwater samples and turbidity, pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Nitrate-N, and arsenic concentrations were investigated to profile WQI. The physicochemical parameters from districts South, West, Korangi, and Kemari were as per APHA standards and found suitable for drinking purposes. EC and TDS were in ranges of (2660-3870μS/cm) and (1596-2322mg/L) exceeding the standard limits resulting in poor drinking water quality respectively in districts Central, Malir, and East. These districts fail to meet the APHA standards and become unfit for human consumption on the WQI scale. However, TSS and arsenic was not detected in any of the collected samples. The correlation matrix for the[1] correlation coefficient (R-value) of tested parameters with WQI indicated significant linear and high R-value among tested parameters. The statistical analysis showed that turbidity, pH, EC, TDS, and Nitrate-N have significant effects on WQI. EC and TDS showed a direct correlation with WQI (R=1.0) whereas turbidity significantly correlates with the presence of nitrate in drinking water (R=0.64). These findings help the decision makers to plan mitigation strategies for comprehensive water quality monitoring of the respective areas and ensure the availability of clean and safe drinking water to the public.

Assessing the suitability of a surveillance fauna-monitoring program for detecting future changes in reptile occupancy

Context Ecological surveillance monitoring typically targets multiple taxonomic groups by using standardised sampling across large spatial scales. Although surveillance monitoring confers advantages over hypothesis-driven monitoring in its broad taxonomic and spatial scope, the approach has been criticised for its disconnect from ecological management and failure to provide insights on the drivers of ecological change Aims To assess the adequacy of a plot-based general fauna-monitoring program for sampling reptiles as indicators of ecosystem health in a semi-arid upland region of the Northern Territory, Australia. Methods We surveyed reptiles at 90 sites, stratified between major landform and vegetation types, and using standard fauna-sampling methods, across the 2568 km2 Tjoritja National Park in the MacDonnell Ranges. We compiled a full inventory of the reptile fauna of the study area and identified species with potential utility as ecological indicators. We then used single-season occupancy models and power analyses to evaluate the adequacy of sampling for detecting potential future changes in occupancy. Key results We detected 57 of the 68 reptile species known from the protected area, 17 of which are potentially useful indicators of ecological health, mostly related to fire management. There was insufficient power to detect moderate (50%) future changes in reptile occupancy for all but the single most detected species. For the two ecological indicator species with sufficient detections for occupancy modelling, a positive association with a keystone structure (dense spinifex grass) was confirmed. However, increasing detection probability or the number of surveys would result in only minor improvements in power to detect occupancy change in these species. Conclusions Although reptiles are potentially useful indicators of ecological health, particularly in relation to fire regimes, the number of sites required to detect future changes in reptile occupancy by using standardised plot-based monitoring in this protected area is prohibitively high. Implications Our results suggest that once ecological associations are understood, monitoring ecological health remotely by using techniques such as fire-scar mapping to track proportions of long-unburnt vegetation should be considered over labour-intensive surveillance monitoring for reptiles. Targeted monitoring of threatened and other reptile species of conservation or cultural concern may also be warranted.

Canopy light transmittance-based crop growth monitoring and leaf area index estimation in a garlic field

ABSTRACT A smart agricultural system is necessary for monitoring crop growth and stress conditions using near-ground-based remote sensing techniques. Crop growth can be estimated using several standard crop growth parameters. However, obtaining timed sequential data for observing leaf area index (LAI) is challenging, and normalized difference vegetation index (NDVI) estimation in crop fields requires the installation of sensors on frames structure that are taller than the crop. Canopy light transmittance (CLT) indicates the degree of decrease in the amount of light passing through some material. It was conventionally used to understand canopy radiative transfer. This study examined the viability of CLT as a novel crop parameter for monitoring crop growth conditions. The CLT, LAI, and NDVI of a garlic field were recorded for five years. The correlation between daily CLT and LAI was higher than that between NDVI and LAI. Thus, CLT has the potential to sequentially estimate crop LAI values, particularly for capturing the temporal patterns of LAI. In addition, like NDVI, CLT showed sensitivity in representing the canopy structure and the amount of biomass because CLT is conceptually related to the sky gap fraction. Thus, CLT has the potential to serve as a novel growth parameter for continuous crop growth monitoring.

Study on Optimization of Placement Method for Transmission Line Monitoring Devices Based on Probability of Meteorological Disasters

Abstract To ensure the reliability and safety of the power grid system, it is of great significance to carry out electric meteorological monitoring for transmission channels. In this paper, a method for optimizing the placement of transmission line monitoring devices based on the probability of meteorological disasters is proposed. By considering the climatic characteristics near the transmission line, the probability of occurrence of adverse meteorological faults for each transmission tower in the transmission line is analyzed and calculated by using existing meteorological and GIS elevation data. Meanwhile, the historical failure data and voltage levels of transmission lines are combined to achieve the optimization of the installation position of monitoring devices in transmission channels. This method was applied to a 220 kV transmission line for practical analysis, achieving dense monitoring in areas with high failure rates and sparse monitoring in areas with low failure rates.

Risk and Energy Based Optimization for Fire Monitoring System in Utility Tunnel Using Cellular Automata

Fire is one of the biggest threats to the safety of utility tunnels, and establishing camera-based monitoring systems is conducive to early fire finding and better understanding of the evolution of tunnel fires. However, conventional monitoring systems are being faced with the challenge of high energy consumption. In this paper, the camera operation in a utility tunnel was optimized considering both fire risk and energy consumption. Three design variables were investigated, namely the camera sight, the number of cameras in simultaneous operation, and the duration of camera operation. Cellular automata were used as a simple but effective method to simulate the spread of fire in a utility tunnel. Results show that as the number of cameras in simultaneous operation increases, the probability of fire capture also increases, but the energy consumption decreases. A shorter duration of camera operation can lead to a higher probability of fire capture, and meanwhile, lower energy consumption. For the duration of camera operation shorter than or equal to the allowable time, the probability of fire capture is significantly higher than that for the duration longer than the allowable time. Increasing the camera sight will significantly increase the probability of fire capture and lower the total energy consumption when a blind monitoring area exists. The total energy consumption of a camera-based monitoring system roughly satisfies hyperbolic correlation with the duration of camera operation, while the probability of fire capture can be predicted based on the number of cameras in simultaneous operation through a power model. The optimal design for the modeled tunnel section is two cameras in simultaneous operation with a tangent monitoring area. The duration of camera operation should be as short as possible, at least shorter than the allowable time. The study is expected to provide a reference for the sustainable design of energy-saving utility tunnels with lower fire risk.

A cost-effective ECG monitoring in rural areas: leveraging artificial neural networks for efficient healthcare solutions

Cardiovascular diseases engender serious public health concerns in developing nations since access to specialized medical equipment is often limited and standard treatment expenses can be prohibitive. This study proposes an efficient and relatively affordable electrocardiogram (ECG) monitoring system that reads and analyzes a person's electrocardiogram data to provide affordable and quality healthcare solutions. The device initially extracts features from electrocardiogram records by reading electrical signals in the heart. Extracted data are then analyzed by a trained deep learning model to determine precisely if the heart is in a healthy state or undergoing complexities. Experimental results showed that the fine-tuned ANN architecture outperformed the state-of-the-art architectures in this field with an accuracy of 98.95%. The data can also be sent to specialists through an MQTT server if necessary, allowing for remote diagnosis and treatment. The system is intended to be deployed in countries where rural regions lack access to specialized healthcare equipment and professionals. Additionally, the device is inexpensive and, hence can be made accessible to people with limited affordability.

Incidence trends and spatial distribution of thyroid cancer in the Chinese female population from 1990 to 2019

ObjectiveThis study aimed to analyze the incidence trends and spatial distribution characteristics of thyroid cancer among Chinese females from 1990 to 2019, thereby providing a scientific foundation and data support for the development of prevention and control policies. MethodsThyroid cancer incidence data from the Global Burden of Disease (GBD) research and the annual report from the Chinese Tumor Registration were utilized. The standardized thyroid cancer incidence rate among Chinese females 1990 to 2019 was described to understand the changes in developmental trends. The JoinPoint Regression Model was employed using Excel 2019, GraphPad Prism 8, JoinPoint Regression Program 4.8.0.1, and ArcGIS 10.2. ResultsThyroid cancer's standardized incidence among female Chinese continued to increase at 1.7% per year (annual average percentage change [AAPC] = 1.7, P < 0.001), and the spatial distribution was clustered. The main high-incidence areas were North, East, and Northwest China. ConclusionsThyroid cancer incidence in Chinese women is rapidly increasing, and its spatial distribution is concentrated. Strengthening monitoring, prevention, and control efforts in the relevant areas is warranted.

Dual-sensitized heterojunction Ag2S/ZnS/NiS composites with entire visible-light region absorption for ultrasensitive photoelectrochemical detection of tobramycin

In this study, an ultrasensitive photoelectrochemical (PEC) aptasensor based on dual-sensitized heterojunction Ag2S/ZnS/NiS composites as a signal probe was proposed for the detection of tobramycin (TOB) by combining a cascaded quadratic signal amplification strategy. Specifically, compared to the limited visible light-harvesting capability of single sensitized composites, Ag2S/ZnS/NiS composites with p-n and n-n heterojunction could greatly improve the light energy utilization to tremendously strengthen the optical absorption in the entire visible-light region. Moreover, dual-sensitized heterojunction could effectively hinder the rapid recombination of photoelectrons and holes (carriers) to obtain a good photocurrent for improving the sensitivity of the aptasensor. Furthermore, a cascaded quadratic signal amplification strategy was applied to convert trace target TOB into plentiful gold nanoclusters (Au NCs) labelled double-stranded DNA for the construction of PEC aptasensor, with a broad linear detection range from 0.01 to 100 ng mL−1 and a low detection limit of 3.38 pg mL−1. Importantly, this study provided a versatile and sensitive PEC biosensing platform for TOB analysis, and demonstrated its successful application for TOB detection in milk samples. This protocol provides a novel dual-sensitized heterojunction composites to develop a highly efficient and harmfulless PEC aptasensor, which is expected to be used in food safety, environmental monitoring and other areas.

Fabrication and Development of the Embedded Linux Based on the ARM System

Abstract With the development of the embedded system and improvement of the requirements from customers, embedded GUI which was used to communicate between users and embedded system became to be a key in researching the embedded system. This article introduced what is embedded system and what is embedded GUI. It described the porting of an embedded GUI on ARM Linux platform, including the establishment of compile environment, configuring and modifying of compile options and setting of runtime parameters. Above all, this article introduces the development of Embedded Linux GUI, describes the GUI development environment of Qt /Embedded and Qt, one procedure of the app location development based on Qt/Embedded is described in detail. This system adopts the B/S mode and uses the ARM11 development board with S3C6410 as the core. Sensors are used to obtain information, and the obtained data is judged and analyzed. When the preset conditions are not met, an alarm function is triggered. At the same time, data transmission is carried out through the Zigbee wireless module, and the receiving end analyzes and displays the received data. Each device is connected to the Zigbee module, which distinguishes it by assigning different network addresses and constantly transmits the status of the monitoring area to the receiving end. The receiving end distinguishes the monitoring status of different locations based on the different network addresses, increasing mobility.

Combining chemical analysis and toxicological methods to access the ecological risk of complex contamination in Daye Lake

As one of the nine primary non-ferrous metal smelting bases in China, Daye Lake basin was polluted due to diverse human activities. But so far the pollution status and related ecological risks of this region have not been detailly investigated. In current study, pollutants including heavy metals, polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) in eight sediment samples from Daye Lake were quantified. 18S rRNA gene sequencing was employed to profile the nematode community structure within these sediments. Model organism Caenorhabditis elegans (C. elegans) were further applied for a comprehensive ecological risk assessment of Daye Lake. Notably, Cadmium (Cd) was identified as a key driver of ecological risk, reaching an index of 1287.35. At sample point S4, OCPs particularly p,p′-DDT, displayed an extreme ecological risk with a value of 23.19. Cephalobidae and Mononchida showed strong sensitivity to pollutant levels, reinforcing their suitability as robust bioindicators. The composite pollutants in sampled sediments caused oxidative stress in C. elegans, with gene Vit-2 and Mtl-1 as sensitive biomarkers. By employing the multiple analysis methods, our data can offer valuable contributions to environmental monitoring and health risk assessment for composite polluted areas.

Perturbation Results for Distance-edge-monitoring Numbers*

Foucaud et al. recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. Given a graph G = (V(G), E(G)), a set M ⊆ V(G) is a distance-edge-monitoring set if for every edge e ∈ E(G), there is a vertex x ∈ M and a vertex y ∈ V(G) such that the edge e belongs to all shortest paths between x and y. The smallest size of such a set in G is denoted by dem(G). Denoted by G – e (resp. G\u) the subgraph of G obtained by removing the edge e from G (resp. a vertex u together with all its incident edges from G). In this paper, we first show that dem(G – e) – dem(G) ≤ 2 for any graph G and edge e ∈ E(G). Moreover, the bound is sharp. Next, we construct two graphs G and H to show that dem(G) – dem(G\u) and dem(H \ v) – dem(H) can be arbitrarily large, where u ∈ V(G) and v ∈ V(H). We also study the relation between dem(H) and dem(G), where H is a subgraph of G. In the end, we give an algorithm to judge whether the distance-edge-monitoring set still remain in the resulting graph when any edge of a graph G is deleted.

Land-Use–Land Cover Changes in the Urban River’s Buffer Zone and Variability of Discharge, Water, and Sediment Quality—A Case of Urban Catchment of the Ngerengere River in Tanzania

The physical integrity of the Ngerengere River and its three tributaries drains within Morogoro Municipality were evaluated by assessing the variations in land-use–land cover (LULC) in the river’s buffer zone, the discharge, and the contamination of river water and sediment from nutrients and heavy metals. Integrated geospatial techniques were used to classify the LULC in the river’s buffer zone. In contrast, the velocity area method and monitoring data from the Wami-Ruvu Basin were used for the discharge measurements. Furthermore, atomic absorption spectrophotometry was used during the laboratory analysis to determine the level of nutrients and heavy metals in the water and river sediment across the 13 sampling locations. The LULC assessment in the river’s buffer during the sampling year of 2023 showed that bare land and built-up areas dominate the river’s buffer, with a coverage of 28% and 38% of the area distribution. The higher discharge across the sampling stations was in the upstream reaches at 3.73 m3/s and 2.36 m3/s at the confluences. The highest concentrations of heavy metals in the water for the dry and wet seasons were 0.09 ± 0.01, 0.25 ± 0.01, 0.03 ± 0.02, 0.73 ± 0.04, 4.07 ± 0.08, and 3.07 ± 0.04 mg/L, respectively, for Pb, Cr, Cd, Cu, Zn, and Ni. The order of magnitude of the heavy metal concentration in the sediments was Zn &gt; Ni &gt; Cr &gt; Cu &gt; Cd &gt; Pb, while the highest NO2−, NO3−, NH3, and PO43− in the water and sediment were 2.05 ± 0.01, 0.394 ± 0.527 0.66 ± 0.05, and 0.63 ± 0.01 mg/L, and 2.64 ± 0.03, 0.63 ± 0.01, 2.36 ± 0.01, and 48.16 ± 0.01 mg/kg, respectively, across all sampling seasons. This study highlights the significant impact of urbanization on river integrity, revealing elevated levels of heavy metal contamination in both water and sediment, the variability of discharge, and alterations in the LULC in the rivers’ buffer. This study recommends the continuous monitoring of the river water quality and quantity of the urban rivers, and the overall land-use plans for conserving river ecosystems.

Integrated high-precision monitoring method for surface subsidence in mining areas using D-InSAR, SBAS, and UAV technologies

The use of unmanned operations to monitor mining induced land subsidence is increasing. This study conducts a detailed comparative analysis of accuracy of measured ground deformation provided by Differential Interferometric Synthetic Aperture Radar (D-InSAR), Small Baseline Subset (SBAS), and Unmanned Aerial Vehicle (UAV) tilt photogrammetry with respect to levelling measurements. Based on such analysis we propose an integrated approach that combines multiple remote sensing methods to achieve a better global accuracy in the land subsidence monitoring in mining areas. Conducted at the Banji Coal Mine, this study collected subsidence data from April 10, 2021, to June 28, 2022, through D-InSAR, SBAS, and UAV techniques. After segmenting the subsidence basin into distinct zones, we qualitatively assessed each area with UAV-derived 3D models and quantitatively evaluated the precision of all applied techniques, benchmarking against leveling data. Our findings indicate that integrating D-InSAR, SBAS, and UAV technologies significantly enhances monitoring accuracy over any single method, demonstrating their combined effectiveness in different subsidence areas. Consequently, the synergistic integration of D-InSAR, SBAS, and UAV technologies, capitalizing on their complementary strengths, enables the achievement of intuitive, comprehensive, and high-precision monitoring of subsidence basins in mining areas.

Train Station Pedestrian Monitoring Pilot Study Using an Artificial Intelligence Approach.

Pedestrian monitoring in crowded areas like train stations has an important impact in the overall operation and management of those public spaces. An organized distribution of the different elements located inside a station will contribute not only to the safety of all passengers but will also allow for a more efficient process of the regular activities including entering/leaving the station, boarding/alighting from trains, and waiting. This improved distribution only comes by obtaining sufficiently accurate information on passengers' positions, and their derivatives like speeds, densities, traffic flow. The work described here addresses this need by using an artificial intelligence approach based on computational vision and convolutional neural networks. From the available videos taken regularly at subways stations, two methods are tested. One is based on tracking each person's bounding box from which filtered 3D kinematics are derived, including position, velocity and density. Another infers the pose and activity that a person has by analyzing its main body key points. Measurements of these quantities would enable a sensible and efficient design of inner spaces in places like railway and subway stations.

Drive-by Environmental Sensing Strategy to Reach Optimal and Continuous Spatio-Temporal Coverage Using Local Transit Network

Monitoring environmental features, such as air pollution, carbon dioxide emissions, noise, and heat, gives cities key data-driven insights to advise sustainable policies and city design. However, given the high variability of the environmental data, achieving good spatio-temporal resolution and coverage remains a major challenge. Even in well-monitored cities, such as Amsterdam, environmental sensors are usually placed in very few fixed locations, implying limited spatial coverage and an inability to adapt to changes in the urban environment. As cities evolve, they experience shifts in pollution sources, and fixed sensors might not adequately capture these changes without a costly and time-consuming reconfiguration process. To monitor the environmental qualities of Amsterdam’s roads, we present a “drive-by” sensing solution for a structured network of vehicles, meaning that sensors are designed to be deployed on buses and tramways, the trajectories and schedules of which are known. We propose a deployment strategy that combines the available fleets to reach optimal spatio-temporal coverage for different environmental features. For example, by optimizing the deployment of sensors on public transit vehicles, our proposal significantly enhances the monitoring of pollution-sensitive areas in Amsterdam. Depending on the desired spatio-temporal granularity and noting that one vehicle only hosts one sensor, the required number of sensors to be deployed on the structured network varies between 43 and 142, with the latter achieving the finest possible resolution.

High-accuracy fiber Bragg grating inclinometer

Inclination monitoring plays a significant role in research on deformation monitoring of slopes, inclination monitoring of bridges, earthquake monitoring, and other areas of monitoring. Existing electromagnetic signal-based inclinometers face practical issues such as difficulty adapting to harsh environments, poor large-scale networking capabilities, and unstable signal transmission. Hence, what we believe to be a novel inclinometer based on fiber sensing principles is proposed. The sensor employs suspension sensing based on the plumb principle, using bearings to overcome mechanical friction caused by rigid fixation between the mass block and the cantilever, thereby improving sensitivity and accuracy of the sensor. Key structural parameters of the sensor were optimized and simulated, followed by fabrication of the sensor and performance test on an inclination test platform. Experimental results indicate that, within a measurement range of ±9∘, the sensor exhibited a sensitivity of 305.2 pm/°, a resolution of approximately 3.3×10−4∘, an accuracy of 2%, a repeatability error of 1.9%, and favorable creep resistance stability for long-term measurement, thus addressing the requirements for slope deformation monitoring.

Fiber-optic gyroscope for rotational seismic ground motion monitoring of the Campi Flegrei volcanic area

The real-time monitoring of densely populated areas with high seismic and volcanic risk is of crucial importance for the safety of people and infrastructures. When an earthquake occurs, the Earth surface experiences both translational and rotational motions. The latter are usually not monitored, but their measurement and characterization are essential for a full description of the ground motion. Here we present preliminary observational data of a high-sensitivity rotational sensor based on a 2-km-long fiber-optic Sagnac gyroscope, presently under construction in the middle of the Campi Flegrei Volcanic Area (Pozzuoli, Italy). We have evaluated its performance by analyzing data continuously recorded during an acquisition campaign of five months. The experimental setup was composed of a digital nine-component seismic station equipped with both a rotational sensor and conventional seismic sensors (seismometers, accelerometers, and tiltmeters). During this experiment we detected seismic noise and ground rotations wavefield induced by small to medium local earthquakes (M D &lt;3). The prototype gyroscope shows a very promising sensitivity in the range of 5×10−7−8×10−9rad/s/Hz over the frequency bandwidth 5 mHz–50 Hz. Future upgrades and perspectives are discussed.

A new hybrid optimization algorithm for maximizing area coverage in wireless sensor networks

ABSTRACT The rapid advancements in technology influence develop our lives day-by-day. However, the area coverage issue exhibited by sensor nodes in WSN still seems a complicated issue even if the area coverage enhancement models are developed. This research work is developed on the basis of implementing an efficient area coverage maximising approach with an advanced optimisation model, Coati Modernized Beluga Whale Optimisation (CMBWO). The whole process of the area coverage maximising approach revolves in two important steps: (i) Sensor node coverage model and (ii) Area coverage optimisation. In the first step, a 2D WSN monitoring area set-up is considered and in which target monitoring points are assumed to solve the coverage issue. Most importantly, the distance between the sensor node and the target monitoring point is calculated using weighted Manhattan distance evaluation instead of conventional Euclidean distance evaluation. This improvement is introduced to calculate the precise position of the target monitoring point from the position of the sensor node. The next, area coverage optimisation step is handled by the CMBWO algorithm for maximising area coverage in WSN. This hybridisation is possible by modernising BWO via COA. This efficient hybridisation led to maximising the area coverage of sensor nodes in WSN.

Exposure of wetlands important for nonbreeding waterbirds to sea-level rise in the Mediterranean.

Sea-level rise (SLR) is expected to cause major changes to coastal wetlands, which are among the world's most vulnerable ecosystems and are critical for nonbreeding waterbirds. Because strategies for adaptation to SLR, such as nature-based solutions and designation of protected areas, can locally reduce the negative effects of coastal flooding under SLR on coastal wetlands, it is crucial to prioritize adaptation efforts, especially for wetlands of international importance for biodiversity. We assessed the exposure of coastal wetlands important for nonbreeding waterbirds to projected SLR along the Mediterranean coasts of 8 countries by modeling future coastal flooding under 7 scenarios of SLR by 2100 (from 44- to 161-cm rise) with a static inundation approach. Exposure to coastal flooding under future SLR was assessed for 938 Mediterranean coastal sites (≤30km from the coastline) where 145 species of nonbreeding birds were monitored as part of the International Waterbird Census and for which the monitoring area was delineated by a polygon (64.3% of the coastal sites monitored in the Mediterranean region). Thirty-four percent of sites were threatened by future SLR, even under the most optimistic scenarios. Protected study sites and study sites of international importance for waterbirds were, respectively, 1.5 and 2 times more exposed to SLR than the other sites under the most optimistic scenario. Accordingly, we advocate for the development of a prioritization scheme to be applied to these wetlands for the implementation of strategies for adaptation to SLR to anticipate the effects of coastal flooding. Our study provides major guidance for conservation planning under global change in several countries of the Mediterranean region.