Temporal Monitoring Research Articles

Total Suspended Solids in Teluk Awur, Jepara using Red Reflectance from Landsat-8

The coastal waters of Teluk Awur, Jepara are affected by human activities such as aquaculture, tourism, agriculture, and settlements. The number of human activities on land will contribute to high suspended solids (TSS) entering coastal waters by run-off. The high TSS in coastal areas will affect the entry of light intensity, disrupting the photosynthesis process and ecosystem. This study compares TSS values based on Landsat images using several TSS algorithms such as Wirasatriya, Budhiman, Ajiperwata, and Parwati algorithms and the algorithm resulting from the July 2023 field data calibration against red reflectance. The algorithm's performance from calibration has a smaller error value with RMSE = 8.51, MAPE = 25.77%, and Bias = 7.52. TSS estimation from Landsat 8 satellite imagery has a range of values of 30.56 - 62.55 mg/L (average 35.60 mg/L), while field measurements of TSS values are 22.40 - 64.52 mg/L (average 32.69 mg/L). This research will be valuable information for using the right Landsat-8 algorithm for temporal and sustainable TSS monitoring. It can be used in abrasion monitoring and management in the Teluk Awur coastal waters of Jepara.

A Comprehensive IoT Cloud-based Wind Station Ready for Real-time Measurements and Artificial Intelligence Integration

Accurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with edge devices, designed to provide real-time wind measurements in diverse environments. The presented system comprises a dedicated station frame, a self-sufficient solar power setup, an ultrasonic wind sensor, a data transmission node, cloud computing capabilities, and an end-user visualization application. Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. Using this method, all station data are processed every 3 seconds and stored ready for Artificial Intelligence usage with an approximate latency of 150–300 milliseconds, representing up to an 85% reduction in processing delay compared to similar solutions with over 1-second latencies. The lightweight and resistant frame design facilitates easy deployment, while the power setup with sub-watt efficiency ensures a reliable energy source. The ultrasonic wind sensor was subjected to calibration procedures and design improvements to increase its performance under specific rain conditions. Internet of Things communication via Hypertext Transfer Protocol enables efficient data transmission between the Automated Weather Station and the cloud server, which processes and stores the data for future analysis. The responsive and auto-adaptive web application allows user-friendly data visualization, making the wind data easily accessible and interpretable. The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.

Amazonian manatee critical habitat revealed by artificial intelligence‐based passive acoustic techniques

AbstractFor many species at risk, monitoring challenges related to low visual detectability and elusive behavior limit the use of traditional visual surveys to collect critical information, hindering the development of sound conservation strategies. Passive acoustics can cost‐effectively acquire terrestrial and underwater long‐term data. However, to extract valuable information from large datasets, automatic methods need to be developed, tested and applied. Combining passive acoustics with deep learning models, we developed a method to monitor the secretive Amazonian manatee over two consecutive flooded seasons in the Brazilian Amazon floodplains. Subsequently, we investigated the vocal behavior parameters based on vocalization frequencies and temporal characteristics in the context of habitat use. A Convolutional Neural Network model successfully detected Amazonian manatee vocalizations with a 0.98 average precision on training data. Similar classification performance in terms of precision (range: 0.83–1.00) and recall (range: 0.97–1.00) was achieved for each year. Using this model, we evaluated manatee acoustic presence over a total of 226 days comprising recording periods in 2021 and 2022. Manatee vocalizations were consistently detected during both years, reaching 94% daily temporal occurrence in 2021, and up to 11 h a day with detections during peak presence. Manatee calls were characterized by a high emphasized frequency and high repetition rate, being mostly produced in rapid sequences. This vocal behavior strongly indicates an exchange between females and their calves. Combining passive acoustic monitoring with deep learning models, and extending temporal monitoring and increasing species detectability, we demonstrated that the approach can be used to identify manatee core habitats according to seasonality. The combined method represents a reliable, cost‐effective, scalable ecological monitoring technique that can be integrated into long‐term, standardized survey protocols of aquatic species. It can considerably benefit the monitoring of inaccessible regions, such as the Amazonian freshwater systems, which are facing immediate threats from increased hydropower construction.

Temporal error monitoring: Does agency matter?

Error monitoring is the ability to report one's errors without relying on feedback. Although error monitoring is investigated mostly with choice tasks, recent studies have discovered that participants parametrically also keep track of the magnitude and direction of their temporal, spatial, and numerical judgment errors. We investigated whether temporal error monitoring relies on internal generative processes that lead to the to-be-judged first-order timing performance. We hypothesized that if the endogenous processes underlie temporal error monitoring, one can monitor timing errors in emitted but not observed timing behaviors. We conducted six experiments to test this hypothesis. The first two experiments showed that confidence ratings were negatively related to error magnitude only in emitted behaviors, but error directionality judgments of observed behaviors were more precise. Experiment 3 replicated these effects even after controlling for the motor aspects of first-order timing performance. The last three experiments demonstrated that belief of agency (i.e., believing that the error belongs to the self or someone else) was critical in accounting for the confidence rating effects observed in the first two experiments. The precision of error directionality judgments was higherin thenon-agency condition. These results show that confidence is sensitive to belief, and short-long judgment is sensitive to the actual agency of timing behavior (i.e., whether the behavior was emitted by the self or someone else).

Distributed optical fiber sensors for real-time tracking of fouling buildup for tubular continuous polymerization reactors

Early online fouling detection is expected to be a real step forward in the operation of continuous tubular reactors. As an online technology, the Rayleigh backscatter based Distributed Optical Fiber Sensor (DOFS) technology was evaluated with respect to temperature measurement resolution, reproducibility and the best online calibration method. Different coatings and terminations were characterized for emulsion polymerization reactors. Commercially available sensors with acrylate primary coatings, dual acrylate coatings, and polyimide coatings were all compared with each other. It is shown that the presence of a secondary coating significantly alters sensor behaviour. Sensors with only a primary coating showed a temperature resolution of 0.1 °C. Online calibration for temperature readout was carried out and validation tested on segments of fiber integrated in a 3D-printed reactor channel (diameter 1.5 mm). Acrylate coated sensors showed a deviation of 20 % for the calibration coefficients when inside the reactor channel compared to the online calibration, leading to errors in temperature measurement. Polyimide coated sensors showed a deviation of just 0.6 % in this validation test, demonstrating good capabilities for online calibration with accurate temperature measurements. The possibility of spatial and temporal monitoring of fouling buildup through a heat exchanging wall equipped with DOFS was evaluated.

Mice monitor their timing errors

Animals often engage in representationally guided goal-directed behaviors. These behaviors are thus also subjected to representational uncertainty (e.g. timing uncertainty during waiting), which has been previously shown to adaptively guide behaviors normatively. These observations raise the question of whether non-human animals can track the direction and magnitude of their timing errors (i.e. temporal error monitoring). Only a few studies have investigated this question without addressing the key components of temporal error monitoring (e.g. due to differential reinforcement of metacognitive judgments and primary task representation). We conducted the critical test of temporal error monitoring in mice by developing a novel behavioral task that involved temporal production that exponentially favored temporal accuracy and minimized the contribution of sensorimotor noise. The response rate for an upcoming probabilistic reward following the timing performance was used as a proxy for confidence. We found that mice exhibited high reward expectancy after accurate and low reward expectancy after inaccurate timing performance. The reward expectancy decreased as a function of deviations from the target interval for the short and long reproductions; pointing to the symmetrical sensitivity of metacognition to shorter/longer than target responses. These findings suggest a complete temporal error monitoring ability for mice with human-like metacognitive features.

Multiscale and multimodal evaluation of autosomal dominant polycystic kidney disease development

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent kidney genetic disorder, producing structural abnormalities and impaired function. This research investigates its evolution on mouse models, utilizing a combination of histology imaging, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) to evaluate its progression thoroughly. ADPKD has been induced in mice via PKD2 gene knockout, followed by image acquisition at different stages. Histology data provides two-dimensional details, like the cystic area ratio, whereas CT and MRI facilitate three-dimensional temporal monitoring. Our approach allows to quantify the affected tissue at different disease stages through multiple quantitative metrics. A pivotal point is shown at approximately ten weeks after induction, marked by a swift acceleration in disease advancement, and leading to a notable increase in cyst formation. This multimodal strategy augments our comprehension of ADPKD dynamics and suggests the possibility of employing higher-resolution imaging in the future for more accurate volumetric analyses.

Editorial: Spatial and temporal monitoring of wildfire hazard under a climate change environment: prevention, mitigation and management

Editorial: Spatial and temporal monitoring of wildfire hazard under a climate change environment: prevention, mitigation and management

Crop Growth Analysis Using Automatic Annotations and Transfer Learning in Multi-Date Aerial Images and Ortho-Mosaics

Growth monitoring of crops is a crucial aspect of precision agriculture, essential for optimal yield prediction and resource allocation. Traditional crop growth monitoring methods are labor-intensive and prone to errors. This study introduces an automated segmentation pipeline utilizing multi-date aerial images and ortho-mosaics to monitor the growth of cauliflower crops (Brassica Oleracea var. Botrytis) using an object-based image analysis approach. The methodology employs YOLOv8, a Grounding Detection Transformer with Improved Denoising Anchor Boxes (DINO), and the Segment Anything Model (SAM) for automatic annotation and segmentation. The YOLOv8 model was trained using aerial image datasets, which then facilitated the training of the Grounded Segment Anything Model framework. This approach generated automatic annotations and segmentation masks, classifying crop rows for temporal monitoring and growth estimation. The study’s findings utilized a multi-modal monitoring approach to highlight the efficiency of this automated system in providing accurate crop growth analysis, promoting informed decision-making in crop management and sustainable agricultural practices. The results indicate consistent and comparable growth patterns between aerial images and ortho-mosaics, with significant periods of rapid expansion and minor fluctuations over time. The results also indicated a correlation between the time and method of observation which paves a future possibility of integration of such techniques aimed at increasing the accuracy in crop growth monitoring based on automatically derived temporal crop row segmentation masks.

Quantification of immiscible displacement in a rough-walled fracture through direct visualization

Subsurface substance migration in the fractured rock aquifer is mainly controlled by fractures, and immiscible fluid-fluid displacement in fractures is important to many geophysical processes and engineering activities. Using a fracture-visualization system, we present the qualitative and quantitative assessment of fracture geometry associated with fluid movement and distribution in the rough fracture. Based on fracture geometry and statistical analysis, we first conducted a quantitative study of fracture surface roughness and aperture distribution. Then, fractal dimensions of displacement front and residual oil distribution were determined by image processing procedures. Influenced by wettability and micro-scale roughness, at the end of the displacement, residual oil saturation of molded sample is lower (6.45%–25.74%), and displacement pattern is more uniform, indicating that displacement effect is better. Due to smaller differences in residual oil saturation (9.08%) under different injection directions, the impact of wettability on the displacement process is greater than that of anisotropic roughness. Additionally, the fractal dimension of the displacement front increased under low injection rates initially but decreased when the rate was increased later. Overall, visualized temporal monitoring of experimental images enabled us to provide a preliminary assessment of the impact of anisotropic roughness and the material constituting the fracture wall on invading fluid saturation and the fractal dimension of the displacement front under various injection rates.

Characterizing annual leaf area index changes and volume growth using ALS and satellite data in forest plantations

While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. Remote sensing offers a practical alternative for extensive assessments. Satellite imagery provides broad-scale, long-term monitoring; however, may lack detail needed to guide specific forest management actions. Conversely, Airborne Laser Scanning (ALS) provides accurate LAI estimates at fine spatial detail but is limited by cost and temporal monitoring constraints. Combining ALS data with satellite observations could enhance plantation management decisions by balancing extensive coverage with detailed observations. This study explores the integration of ALS and satellite remote sensing as a comprehensive alternative for assessing LAI and stand volume growth rate (m3/ha/year) in operational Pinus radiata plantations in central-south Chile. Our approach comprised four major steps. First, we applied the Beer-Lambert law using ALS vertical profiles to estimate LAI across a forest plantation (LAIALS). We found that ALS accurately estimated LAI across 121 plots (R2 = 0.82 and RMSE = 0.51). Second, we built a simple linear regression to link LAIALS with the Normalized Difference Moisture Index (NDMI) derived from surface reflectance information from the Landsat/Sentinel-2 satellites, resulting in an R2 of 0.53 and an RMSE of 1.17. This step showed a higher correlation with satellite data compared to using only ground-based LAI estimates (R2 = 0.38; RMSE = 1.18). Third, we transformed biweekly NDMI time series to LAI, then derived peak annual LAI as an indicator of mean annual increment (MAI) (R2 = 0.51; RMSE = 5.27 m³/ha/year). This allowed us to characterize stand growth and LAI on a yearly wall-to-wall basis. Throughout the modelling steps, we incorporated error propagation, allowing final estimates to be error bounded. This integrated approach serves as a tool for identifying and visualizing growth irregularities, guiding adaptive management strategies to maintain or enhance stand productivity over time.

Spatial and Temporal Water Quality Monitoring in the Crocodile River of Mpumalanga, South Africa

Spatial and Temporal Water Quality Monitoring in the Crocodile River of Mpumalanga, South Africa

Temporal Monitoring of Simulated Burials in an Arid Environment Using RGB/Multispectral Sensor Unmanned Aerial Vehicles

For the first time, RGB and multispectral sensors deployed on UAVs were used to facilitate grave detection in a desert location. The research sought to monitor surface anomalies caused by burials using manual and enhanced detection methods, which was possible up to 18 months. Near-IR (NIR) and Red-Edge bands were the most suitable for manual detection, with a 69% and 31% success rate, respectively. Meanwhile, the enhanced method results varied depending on the sensor. The standard Reed–Xiaoli Detector (RXD) algorithm and Uniform Target Detector (UTD) algorithm were the most suitable for RGB data, with 56% and 43% detection rates, respectively. For the multispectral data, the percentages varied between the algorithms with a hybrid of the RXD and UTD algorithms yielding a 56% detection rate, the UTD algorithm 31%, and the RXD algorithm 13%. Moreover, the research explored identifying grave mounds using the normalized digital surface model (nDSM) and evaluated using the normalized difference vegetation index (NDVI) in grave detection. nDSM successfully located grave mounds at heights as low as 1 cm. A noticeable difference in NDVI values was observed between the graves and their surroundings, regardless of the extreme weather conditions. The results support the potential of using RGB and multispectral sensors mounted on UAVs for detecting burial sites in an arid environment.

Earth Observation and Geoinformatics to Monitoring the Environmental Status of Urban Streams Inextricably Linked to People's Mental Health.

The general condition of urban watercourses is inextricably linked to ecological balance and public health (physical and mental well-being). Given the vulnerability of these systems to climate change and human pollution, this work aims to demonstrate the effective use of environmental indicators to: a) rapidly assess soil and water conditions near urban streams and b) highlight the importance of geoinformatics and earth observation supported by ground-based techniques. There is a great need for new technology and methods for spatial and temporal monitoring and further quantification of environmental quality in urban streams and the land surrounding them to sensitize policymakers and the public to the environmental degradation of these exceptional habitats and to further protect people's mental health.

High-resolution temporal NDVI data reveal contrasting intratidal, spring-neap and seasonal biomass dynamics in euglenoid- and diatom-dominated biofilms

Intertidal microphytobenthos (MPB) are a major contributor to primary production in estuarine ecosystems. While their biomass is highly variable at multiple spatial and temporal scales, the underlying drivers are as yet little understood. Both in situ sampling and remote-sensing techniques often lack the temporal resolution or coverage to simultaneously capture short-term (intratidal to daily) and longer-term (weekly to annual) biomass changes. Our field setup with in-situ NDVI sensors allowed us to study MPB surface biomass variability at high temporal resolution (10 mins) for up to two years in a freshwater euglenoid dominated mudflat, and a brackish and a marine diatom dominated mudflat. MPB biomass showed marked periodicities at multiple temporal scales: seasonal, spring-neap and intratidal. The diatom-dominated MPB community showed a seasonal biomass peak in winter, while the euglenoid-dominated community showed biomass peaks during spring and summer, probably caused by underlying divergent responses to mainly irradiance, temperature and wind-induced resuspension, and macrobenthos grazing. Spring-neap periodicity likely resulted from differential migratory responses of the MPB communities to variation in timing and duration of daylight exposure. In the freshwater community, upward migration only occurred when exposure duration was sufficiently long (≥4 h). In the diatom-dominated community, morning daylight exposure resulted in highest NDVI values. This study highlights the differences in MPB biomass dynamics between MPB communities within estuarine ecosystems, and underscores the great potential of high-resolution temporal NDVI monitoring for more accurate estimates of MPB biomass and primary production.

Elevated airborne radioactivity downwind of a Colorado oil refinery

ABSTRACT Airborne radioactivity from fossil fuel production systems is poorly characterized, but a recent study showed elevated ambient levels with proximity to oil and gas production wells. Here, we report year-long, high temporal resolution monitoring results of airborne alpha radioactivity from both radon gas and radon progeny attached to particulates immediately northeast of an oil refinery in Commerce City, Colorado, USA, in an environmental justice community of concern. Gas and particle-associated radioactivity contributed nearly evenly to the total alpha radioactivity. Total radioactivity levels of 30–40 Bq m−3 were 2–3 times higher than background levels (~10–15 Bq m−3) when winds were light and southwesterly, suggesting the refinery as the geographic origin. Furthermore, elevated airborne radioactivity tracked most closely with the light hydrocarbon and natural gas tracer ethane. Thus, the data imply natural gas as the radon emission carrier. Our findings are unique and suggest a need for further investigations of radon emissions from oil and gas infrastructure such as natural gas processing plants, compressor stations, petrochemical plants, and oil refineries that process oil and natural gas from unconventional production. Implications: Regulatory agencies currently do not mandate or conduct monitoring of radioactivity releases and public exposure from petroleum industry air emissions. This study reports elevated radioactivity from radon gas and nonvolatile radon decay products attached to particulate matter, at about 2–3 times above background levels in proximity to Colorado’s largest oil refinery. Observations were within an environmental justice community of concern that experiences well above-average exposure to many other harmful atmospheric pollutants, suggesting potential adverse health effects from this cumulative exposure. Our findings offer actionable insights for policymakers, industry stakeholders, and affected communities alike.

Using Dynamic Laser Speckle Imaging for Plant Breeding: A Case Study of Water Stress in Sunflowers.

This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress in plants can involve physical and biochemical changes. Some of these changes may alter the optical scattering properties of leaves. The present study therefore proposes to test the potential of a biospeckle measurement to observe the temporal evolution in different varieties of sunflower plants under water stress. An experiment applying controlled water stress with osmotic shock using polyethylene glycol 6000 (PEG) was conducted on two sunflower varieties: one sensitive, and the other more tolerant to water stress. Temporal monitoring of biospeckle activity in these plants was performed using the average value of difference (AVD) indicator. Results indicate that AVD highlights the difference in biospeckle activity between day and night, with lower activity at night for both varieties. The addition of PEG entailed a gradual decrease in values throughout the experiment, particularly for the sensitive variety. The results obtained are consistent with the behaviour of the varieties submitted to water stress. Indeed, a few days after the introduction of PEG, a stronger decrease in AVD indicator values was observed for the sensitive variety than for the resistant variety. This study highlights the dynamics of biospeckle activity for different sunflower varieties undergoing water stress and can be considered as a promising phenotyping tool.

Effects of temporal hydrologic shifts on the population biology of an endangered freshwater fish in a dryland river ecosystem

AbstractSpecies occupying dryland river ecosystems often experience “boom‐and‐bust” demographic cycles that coincide with shifts in habitat availability. Knowing whether declines are within natural thresholds versus those caused by acute human disturbance is critical for managing protected species. We investigated temporal shifts in abundance and habitat use of an endangered population of the threespine stickleback Gasterosteus aculeatus in southern California, where a Mediterranean climate leads to ephemeral habitat in one of the regions' least hydrologically modified rivers, the Santa Clara River. We conducted population surveys over a period of below‐average rainfall in the upper watershed in Soledad Canyon, with predefined reaches surveyed multiple times per year to capture different hydrologic conditions. Abundances were stable across years but varied significantly depending on location, with some reaches remaining dry and others drying seasonally to varying degrees. Occupancy models showed that the presence of stable perennial reaches, drying regime, and other site‐specific factors were important predictors of habitat use, and that certain reaches may be key to ensuring source‐sink dynamics as flow dissipates over the dry season. Low occupancy in two sections was driven by different predominant mechanisms, one by diel cycles of evapotranspiration and the other by cattails (Typha spp.), with both having greater effects during the hotter, drier parts of the year. As dryland river ecosystems are vulnerable to the effects of anthropogenic‐induced climate change, this study demonstrates how temporal monitoring can delimit dry‐state benchmarks for improving management interventions (i.e., translocation and habitat restoration) for protected species under conditions that are predicted to worsen in the coming years.

Spatio-temporal metapopulation trends: The coconut crabs of Zanzibar.

Species experience a variety of environmental and anthropogenic conditions across their ranges leading to spatial variation in population dynamics. Understanding population dynamics under different conditions is important but it is challenging to allocate limited effort to spatial and temporal subpopulation monitoring. Using GLMMs, we analyze survey data of a metapopulation of coconut crabs spanning 7 years and 15 sites in and near the Pemba archipelago, Zanzibar, to estimate trends in population size (based on catch per unit effort), weight and sex ratio at the meta- and subpopulation level and investigate anthropogenic drivers of these trends. We found that the overall metapopulation has remained stable in terms of size and composition over the survey period, but observed diverging trends in population size and sex ratio at some subpopulations. Formal protection of sites was associated with positive population trends. Of nine sites for which we could estimate site-specific trends, three showed increasing and two decreasing trends, whereas four sites had stable subpopulations. Although anthropogenic factors affected the average weight, and the incidence of small and large individuals, we found no temporal trends in any weight-related measures. Furthermore, there were no apparent patterns between weight-related measures and subpopulation trends. The metapopulation was biased toward males, and exploitation appeared to be associated with declining trends in the proportion of females, likely an artifact of a strong decline in the proportion of females in one of only two exploited sites in the dataset. Educational campaigns implemented in 2020 at six sites were not related to higher population sizes in later surveys. The variable trends in subpopulation sizes and composition highlight the need for spatially replicated monitoring in metapopulations. The analyses further provide a detailed baseline for future subpopulation studies of this vulnerable species in one of its last remaining metapopulations in the Western Indian Ocean.

Altered temporal awareness during Covid-19 pandemic.

Social isolation during the COVID-19 pandemic had profound effects on human well-being. A handful of studies have focused on how time perception was altered during the COVID-19 pandemic, while no study has tested whether temporal metacognition is also affected by the lockdown. We examined the impact of long-term social isolation during the COVID-19 pandemic on the ability to monitor errors in timing performance. We recruited 1232 participants from 12 countries during lockdown, 211 of which were retested "post-pandemic" for within-group comparisons. We also tested a new group of 331 participants during the "post-pandemic" period and compared their data to those of 1232 participants tested during the lockdown (between-group comparison). Participants produced a 3600 ms target interval and assessed the magnitude and direction of their time production error. Both within and between-group comparisons showed reduced metric error monitoring performance during the lockdown, even after controlling for government-imposed stringency indices. A higher level of reported social isolation also predicted reduced temporal error monitoring ability. Participants produced longer duration during lockdown compared to post-lockdown (again controlling for government stringency indices). We reason that these effects may be underlain by altered biological and behavioral rhythms during social isolation experienced during the COVID-19 pandemic. Understanding these effects is crucial for a more complete characterization of the cognitive consequences of long-term social isolation.