Monitoring Datasets Research Articles

Hydrogeochemical data from carbonate springs as an aid for delimiting catchment areas in the Rovte region, Central Slovenia

Study regionsThe Rovte region, Central Slovenia, is a rural region where karst spring waters are often used for drinking water consumption and irrigation. Study focusIn karst regions, water quickly reaches the subsurface through features ranging from thin fractures to large conduits; as such, this rapid flow leaves karst areas particularly vulnerable to pollutants. This range of permeability and frequently mixed lithologies means that catchment areas in karst regions are often difficult to determine. Here, we present a year-long dataset of hydrogeochemical monitoring of six karst springs and one river, alongside detailed geological data. The aim was to test if water geochemistry can be used to determine the geological units through which the waters flowed, and if this knowledge can help locate plausible catchment areas for springs in karst regions. New hydrological insights for the regionWe show that subtle differences in water geochemistry (including major ions, electrical conductivity, pH) and discharge rates permitted the identification of the geological units (including limestones, dolostones, marly limestones, and siliciclastics) through which the waters flowed. This led to significant modifications to the topographically-modeled catchment areas. We, therefore, advocate using water geochemistry to delineate realistic catchment areas in karst regions. Furthermore, our investigations identified trace metals, in particular Hg, which were above drinking water guideline values, under certain precursor meteorological conditions.

Automated seismic event detection considering faulty data interference using deep learning and Bayesian fusion

AbstractStructural health monitoring (SHM) aims to assess civil infrastructures' performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from extensive continuous monitoring data, is important to ensure the timeliness of subsequent data analysis. To overcome the poor timeliness of manual identification and the inconsistency of sensors, this paper proposes an automated seismic event detection procedure with interpretability and robustness. The sensor‐wise raw time series is transformed into image data, enhancing the separability of classification while endowing with visual understandability. Vision Transformers (ViTs) and Residual Networks (ResNets) aided by a heat map–based visual interpretation technique are used for image classification. Multitype faulty data that could disturb the seismic event detection are considered in the classification. Then, divergent results from multiple sensors are fused by Bayesian fusion, outputting a consistent seismic detection result. A real‐world monitoring data set of four seismic responses of a pair of long‐span bridges is used for method validation. At the classification stage, ResNet 34 achieved the best accuracy of over 90% with minimal training cost. After Bayesian fusion, globally consistent and accurate seismic detection results can be obtained using a ResNet or ViT. The proposed approach effectively localizes seismic events within multisource, multifault monitoring data, achieving automated and consistent seismic event detection.

How, why, where and when people feed birds?—Spatio‐temporal changes in bird‐feeding in Finland

Abstract Providing food to animals, especially birds, during winter is a common activity in many countries. While bird‐feeding can increase connections between people and nature, there are increasing calls from researchers and the general public to limit this activity due to emerging knowledge of potential negative ecological impacts (e.g. biased competition and spread of pathogens). However, what motivates changes in bird‐feeding habits remains largely unknown, despite the ‘provisioners’ perspective’ being critical for designing and implementing policy that benefits both animals and people. Here, we investigate changes in how and why people feed birds in urban and rural areas of Finland as a case study. We made use of two long‐term annual bird monitoring data sets (the Winter bird census and Finnish bird feeder monitoring scheme) to investigate how the number of bird‐feeding sites and the amount of food provisioned have changed since the 1980s. Additionally, we conducted an online questionnaire in 2021 (over 14,000 respondents) to examine reasons for the changes that we detected. We find that, over 40 years, the annual amount of food provided has increased significantly in rural areas, while the number of bird‐feeding sites has decreased and especially so in urban areas. Questionnaire answers indicated that this decline was likely due to changing regulations of local governments and housing organisations, with increased concerns of attracting pests leading to restrictions on providing food for birds. In rural areas, people who reduced feeding more often identified concerns over avian diseases and the effort required to access, clean and refill bird‐feeding sites. Policy implications: Our results highlight that provisioning food to wild animals involves complex decision‐making depending on habitat, geography and economic factors. Therefore, policies designed to curb (or promote) this activity should take into account its multifaceted nature. Read the free Plain Language Summary for this article on the Journal blog.

Daily and Peak Monitor Independent Movement Summary (MIMS) Values Associated With Metabolic Syndrome: NHANES 2011-12 and 2013-14.

The U.S. National Health and Nutrition Examination Survey (NHANES) physical activity monitor datasets for 2011-12 and 2013-14 were released in late 2020. To date, there has been limited interpretation of these nationally representative wrist-worn accelerometer data (summarized and reported in Monitor Independent Movement Summary [MIMS] units) and their relationships with health-related outcomes. This study examined the associations between free-living DailyMIMS (volume), Peak 1MIMS and Peak 30MIMS (intensity), and risk of Metabolic Syndrome (MetS). Data from adults (N=3787; 18-80+ years) in the 2011-12 and 2013-14 NHANES cycles with health examination and accelerometry data were included. Accelerometer data were processed into Peak 1MIMS and Peak 30MIMS (MIMS/min), and DailyMIMS (MIMS/day). Design-based generalized linear and logistic regressions, and a sample-weighted decision tree, were used to examine associations between MIMS variables and MetS risk factors. Lower Peak 1MIMS, Peak 30MIMS, and DailyMIMS were observed for every one-unit increase in the number of risk factors -3.9 [95% CI: -4.3, -3.4] and -2.3 [-2.6, -2.1] MIMS/min, (-672.1 [-772.7, -571.5] MIMS/day, respectively, all p< 0.001). The Decision Tree classified individuals ≥ 46.5 years with a DailyMIMS ≥ 12 245 MIMS/day and a Peak 30MIMS < 45.1 MIMS/min as having MetS (≥ 3 risk factors). Individuals < 46.5 years with a Peak 1MIMS ≥ 62.9 MIMS/min were classified with 0 risk factors. Higher DailyMIMS and PeakMIMS were associated with an absence of MetS risk factors, with a progressive decline as the number of risk factors increased. These findings may be considered as preliminary benchmarks for DailyMIMS and PeakMIMS associated with cardiometabolic risk.

A Groundwater Quality Index Proposed for Assessment of Groundwater Quality: A Case Study of Quang Tri Province, Central Vietnam

Background: Water quality assessment is a crucial task to manage water sources and control water pollution. The purpose of this study is to develop a Groundwater Quality Index (GWQI) to assess groundwater quality. Methods: The GWQI was developed and functioned by weight (wi) and sub-index (qi) of ten selected water quality parameters. Principal Component Analysis (PCA) was applied to a water quality monitoring data set (year 2016 to 2019, 163 samples) at 27 locations in Quang Tri province, aiming to identify the weight of each parameter. Sub-index linear function was established based on the allowable limits for each parameter defined in the National Technical Regulation on drinking water quality. Multiplicate (GWQIM), additive (GWQIA), and reference (GWQIRef) formulas were used to compute the final GWQI indices. The sensitivity and suitability of the proposed GWQI in water quality evaluation were also tested. Results: Among the three GWQI calculation methods, GWQIM was found to be the most suitable in reflecting groundwater quality in the study area. From 2019 to 2022 (152 samples), in the province, 63.2% of GWQIM values were classified as EXCELLENT or GOOD, 17.1% as POOR, and 19.7% as VERY POOR, or UNFIT FOR DRINKING. Conclusion: This improved GWQI can be applied to assess groundwater quality at local or regional scales.

Gross Alpha/Beta Radioactivity of Drinking Water and Relationships with Quality Parameters of Water from Alba County, Romania

Drinking-water is considered one of the existing exposure situations to radiation, which involves a level of risk for human health. Thus, its radioactivity is evaluated to keep exposure “as low as reasonably achievable”. Based on monitoring data sets (2017–2023) for six physical-chemical indicators of drinking water quality and total radioactivity expressed by gross alpha activity, gross beta activity and Radon222 content, we evaluated the correlations, relationships, predictors (water source, locality, year), regional variations and potential baseline patterns using statistical analysis and models. The obtained model shows that drinking water sources and localities are factors with significant effect on nitrates concentration and total hardness, ranking sources in the order: well &gt; spring &gt; municipal distribution system; the rank order of drinking water sources based on the concentration of Radon-222 is spring &gt; well &gt; municipal distribution system; locality was found the best predictor for radioactivity; significant correlations were found between gross alpha and gross beta, between gross beta and Radon-222, and between ammonia, nitrates, nitrites and total hardness. The establishment of a baseline pattern for gross alpha activity is a public health goal that can be achieved through performance monitoring of natural radioactivity in water, useful for preparedness in case of a nuclear event

Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring

Soil Moisture-Derived SWDI at 30 m Based on Multiple Satellite Datasets for Agricultural Drought Monitoring

Feature extraction and pattern recognition in time-lapse pressure transient responses

Monitoring of modern wells equipped with permanent downhole gauges and flowmeters provides large datasets of pressure, temperature and flowrate measurements. On a limited scale, selected events from these datasets are traditionally used in well performance analysis and monitoring, reservoir simulation, and many other tasks employing physics-based models. New methods that combine model- and data-driven approaches for full-scale analysis of these large datasets have recently attracted interest, suggesting new metrics for knowledge extraction. Most recent studies have used a combination of Pressure Transient Analysis (PTA), which is a key reservoir engineering tool, with hybrid methods, such as PTA-metrics and AI-powered models. The emergence of hybrid methodologies combining model- and data-driven approaches has opened new perspectives for comprehensive analysis and knowledge extraction from big well monitoring data sets, accumulated in the industry. Despite their potential, these methodologies often face challenges in reliability, effective data processing, interpretation and feature engineering.This paper introduces a novel PTA-feature extraction and pattern recognition methods for analysis of time-lapse pressure transient responses and their Bourdet derivatives. The pattern recognition method builds on an unsupervised classification method to extract PTA-features, associated with different flow regimes commonly used in PTA. Each pressure transient in a time-lapse series is first processed by an autonomously fine-tuned algorithm that measures the signal's distance to an ensemble of physically meaningful responses defined by PTA-feature library, thus breaking the transient into a series of likely PTA-features. A set of hyperparameters is used in the unsupervised classification, where an optimization procedure is employed for automated tuning of the hyperparameters to a particular transient. Subsequently, recognition of underlying patterns governed by sequences of these PTA-features in the time-lapse transient responses is performed. Testing of the combination of these new methods through synthetic and field cases is further carried out with verification via comparison with expert’ interpretation results. Added value to the previously introduced PTA metrics, which provide on-the-fly well and reservoir performance analysis, is finally demonstrated. The proposed pattern recognition method improves reliability and automates the calculation of the PTA metrics.The developed methodology serves as a new tool for knowledge extraction from big well monitoring datasets, available in the companies operating in the oil and gas industry, as well as the emerging industries such as carbon capture and storage and geothermal energy production. The article concludes with a discussion of the main advantages and limitations of the suggested feature extraction and pattern recognition methods. Besides the combined use of the methods described in this article, these methods may also be integrated with conventional physics-based approaches widely used in the industry for well data interpretation and reservoir simulations, improving their performance and efficiency for big data sets.

3DGEN: a framework for generating custom-made synthetic 3D datasets for civil structure health monitoring

The availability of high-quality datasets is increasingly critical in the field of computer vision-based civil structural health monitoring, where deep learning approaches have gained prominence. However, the lack of specialized datasets for such tasks poses a significant challenge for training a reliable model. To address this challenge, a framework, 3DGEN, is proposed to swiftly generate realistic synthetic 3D datasets which can be targeted for specific tasks. The framework is based on diverse 3D civil structural models, rendering them from various angles and providing depth information and camera parameters for training neural networks. By employing mathematical methods, such as analytical solutions and/or numerical simulations, deformation of civil engineering structures can be generated, ensuring a reliable representation of their real-world shapes and characteristics in the 3D datasets. For texture generation, a generative 3D texturing method enables users to specify desired textures using plain English sentences. Two successful experiments are conducted to (1) assess the efficiency of generating the 3D datasets using two distinct structures, (2) train a monocular depth estimation network to perform 3D surface reconstruction with the generated dataset. Notably, 3DGEN is not limited to 3D surface reconstruction; it can also be used for training neural networks for various other tasks. The code and dataset are available at: https://github.com/YANDA-SHAO/Beam-Dataset-SE

Trends in plant cover derived from vegetation plot data using ordinal zero‐augmented beta regression

AbstractQuestionsPlant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non‐equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.LocationThe Netherlands, Western Europe.MethodsWe used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero‐augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four‐year period. Thereafter we assessed trends in cover and in occurrence across all periods.ResultsWe found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.ConclusionsOur model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets.

IMD-MP: Imputation of Missing Data in IoT Based on Matrix Profile and Spatio-temporal Correlations

Data in the Internet of Things (IoT) domain may be missing due to connectivity errors, environmental extremes, sensor malfunctions, and human errors. Despite the many approaches for imputing missing values, the most significant difficulty in terms of imputation precision or compute complexity for larger missing sub-sequences in uni-variate series is still being explored. This work introduced IMD-MP (Imputation of Missing Data using Matrix Profile), a new technique that improves imputation accuracy for big data analysis in IoT applications based on spatial-temporal correlations using a novel distance metric Matrix Profile Distance (MPD). Our method preserves spatial correlation by grouping the sensors present in the network (using grouping algorithm-GA) to impute the missing data of the failed sensor node. After grouping, similar sensor nodes to the failed sensor node are identified using the Node Similarity Algorithm (NSF). From its similar sensor data, a certain number of sub-sequences that are most similar to the one preceding the failed node&amp;rsquo;s missing values are gathered. These sub-sequences heights are optimized to ensure temporal correlation in the imputed data. To find the optimal imputation sequence, the current research uses MPD and similarity scores. Numerical findings using sensor data from real-time environmental mon-itoring and Intel data sets demonstrate the algorithm&amp;rsquo;s effectiveness compared to other benchmarks.

Boosting field data using synthetic SCADA datasets for wind turbine condition monitoring

State-of-the-art Deep Learning (DL) methods based on Supervisory Control and Data Acquisition (SCADA) system data for the detection and prognosis of wind turbine faults require large amounts of failure data for successful training and generalisation, which are generally not available. This limitation prevents benefiting from the superior performance of these methods, especially in SCADA-based failure prognosis. Data augmentation approaches have been proposed in the literature for generating failure data instances within a SCADA sequence to reduce the imbalance between healthy and faulty state data points, which is relevant to fault detection tasks. However, the successful implementation of DL-based failure prognosis methods requires the availability of multiple run-to-failure SCADA sequences. This paper proposes a data-driven method for generating synthetic run-to-failure SCADA sequences with custom operational and environmental conditions and progression of degradation. An Artificial Neural Network (ANN) is trained with signals that represent these factors to reconstruct the SCADA signals. Then, it is used to generate synthetic SCADA datasets based on data available from a wind turbine that experienced a gearbox failure. Synthetic data sets generated are evaluated on the basis of the similarity of their signal distributions, the temporal dynamics within each signal, and the temporal dynamics among different SCADA signals with those in similar field datasets. The results show that the generated synthetic datasets are consistent with their field counterparts, with a comparatively lower diversity in their dynamic behaviour in time.

Assessing Time-Lapse Full-Waveform Inversion Strategies in a Brazilian Pre-Salt Setting

We conducted a comparative study of time-lapse full-waveform inversion (time-lapse FWI) strategies, considering a typical deep-water Brazilian pre-salt setting. This study employed a realistic P-wave model, ocean bottom nodes (OBN) acquisition, noisy data, and a Gaussian anomaly to represent time-lapse model changes. We evaluated the four most commonly used time-lapse FWI schemes. In the first, known as parallel time-lapse FWI, two independent FWI processes are performed from the same initial model, utilizing baseline and monitor datasets. In the second strategy, namely sequential time-lapse FWI, the retrieved baseline model serves as the starting model for inverting monitor data. In both cases, the time-lapse model is derived by subtracting the retrieved baseline model from the retrieved monitor model. The remaining two methods were double-difference and central-difference time-lapse FWI. Our findings demonstrate that all these schemes can detect model variations of 3%. Remarkably, the central-difference time-lapse FWI method demonstrated superior accuracy in producing time-lapse models and, as such, presents itself as a promising strategy for implementation in time-lapse studies within Brazilian pre-salt regions.

A Discussion of Building a Smart SHM Platform for Long-Span Bridge Monitoring.

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure.

Transfer learning-based quality monitoring of laser powder bed fusion across materials

Laser powder bed fusion (LPBF) is a promising additive manufacturing (AM) technique that has gained rapid growth. Despite the unique advantages such as producing complex-shaped metal parts, LPBF has been still slowed by the poor process repeatability and part consistency. In-situ monitoring methods combining sensing sensors and machine leaning (ML) tools have attracted much attention for defect detection and quality identification. However, the current ML algorithms usually rely on the availability of massive labeled datasets, and the trained model for monitoring a certain powder material cannot be directly applied to monitoring another material with different properties. Moreover, for a new material, it is time-consuming and expensive to collect the sufficient labeled data for building the quality monitoring model from scratch. To this end, motivated by transfer learning (TL), we propose a TL-based quality monitoring approach across two different materials in the LPBF process. Three different sensing datasets of layer-wise images of the solidified layer, photodiode signals, and acoustic emission signals are separately transferred from the 316L stainless steel to the TC4 titanium alloy. A convolutional neural network (CNN)-based quality monitoring model is first built by the sufficient source datasets from 316L stainless steel material. Then, the pre-trained CNN model is fine-tuned with a small amount of the target datasets for quality monitoring on TC4 titanium alloy material. Experimental results demonstrate the effectiveness of the proposed method for quality monitoring from one material to another with limited target datasets, with a classification accuracy above 92 % just with 5 % of target datasets on all three transferred datasets.

Deseasonalized trend of ground-level ozone and its precursors in an industrial city Kaohsiung, Taiwan

Mitigating ground-level ozone (GLO) remains challenging due to its highly nonlinear formation process. Thus, understanding GLO pollution trends is crucial for developing effective control strategies, especially Kaohsiung industrial city, Taiwan. Based on the long-term monitoring data set of 2011–2022, temporal analysis reveals that monthly mean GLO peaks in autumn (40.66 ± 5.10 ppb), carbon monoxide (CO) and major precursors such as nitrogen oxides (NOx), nonmethane hydrocarbons (NMHC) reach their highest levels in winter. The distinct seasonal variation of air pollutants in Kaohsiung is primarily influenced by the unique blocking effect of the mountainous area under the northeasterly wind, as the city is situated downwind, causing high GLO levels during autumn due to the accumulation of stagnant air hindering the dispersion of pollutants. Over the 12 years (2011–2022), the deseasonalized trend analysis was conducted with p < 0.001, revealing a stabilization trend of GLO (+0.04 ppb/yr) from a previous sharp increase. The observed improvement is credited to a drastic decrease in total oxidants (Ox) at −0.63 ppb/yr due to significantly reducing their precursors. Furthermore, the effectiveness of precursor reduction is also supported by GLO daily maximum profile changes. While high GLO events (>120 ppb) decrease, days within midrange (60–80 ppb) rise from 24.4% to 33.3%. A notable difference emerges when comparing daytime and nighttime GLO. While daytime GLO decreased at −0.22 ppb/yr, nighttime GLO increased at +0.34 ppb/yr. Weakened nocturnal titration effects accounted for the nighttime increase. The distinct spatial variations in GLO trends on a citywide scale underscore that areas with complicated industrial activities may not benefit from a continuing reduction of precursors compared to less-polluted areas. The findings of this study hold significant implications for improving GLO control strategies in heavily industrialized city and provide valuable information to the general public about the current state of GLO pollution.

Identifying prehospital trauma patients from ambulance patient care records; comparing two methods using linked data in New South Wales, Australia

BackgroundLinked datasets for trauma system monitoring should ideally follow patients from the prehospital scene to hospital admission and post-discharge. Having a well-defined cohort when using administrative datasets is essential because they must capture the representative population. Unlike hospital electronic health records (EHR), ambulance patient-care records lack access to sources beyond immediate clinical notes. Relying on a limited set of variables to define a study population might result in missed patient inclusion. We aimed to compare two methods of identifying prehospital trauma patients: one using only those documented under a trauma protocol and another incorporating additional data elements from ambulance patient care records. MethodsWe analyzed data from six routinely collected administrative datasets from 2015 to 2018, including ambulance patient-care records, aeromedical data, emergency department visits, hospitalizations, rehabilitation outcomes, and death records. Three prehospital trauma cohorts were created: an Extended-T-protocol cohort (patients transported under a trauma protocol and/or patients with prespecified criteria from structured data fields), T-protocol cohort (only patients documented as transported under a trauma protocol) and non-T-protocol (extended-T-protocol population not in the T-protocol cohort). Patient-encounter characteristics, mortality, clinical and post-hospital discharge outcomes were compared. A conservative p-value of 0.01 was considered significant ResultsOf 1 038 263 patient-encounters included in the extended-T-population 814 729 (78.5 %) were transported, with 438 893 (53.9 %) documented as a T-protocol patient. Half (49.6 %) of the non-T-protocol sub-cohort had an International Classification of Disease 10th edition injury or external cause code, indicating 79644 missed patients when a T-protocol-only definition was used. The non-T-protocol sub-cohort also identified additional patients with intubation, prehospital blood transfusion and positive eFAST. A higher proportion of non-T protocol patients than T-protocol patients were admitted to the ICU (4.6% vs 3.6 %), ventilated (1.8% vs 1.3 %), received in-hospital transfusion (7.9 vs 6.8 %) or died (1.8% vs 1.3 %). Urgent trauma surgery was similar between groups (1.3% vs 1.4 %). ConclusionThe extended-T-population definition identified 50 % more admitted patients with an ICD-10-AM code consistent with an injury, including patients with severe trauma. Developing an EHR phenotype incorporating multiple data fields of ambulance-transported trauma patients for use with linked data may avoid missing these patients.

Host density and anthropogenic stress are drivers of variability in dark spot disease in Siderastrea siderea across the Florida Reef Tract

Dark spot disease (DSD) was first reported within Florida's coral reefs in the 1990s but factors affecting its spatial distribution have not been well studied. We used a 14-yr (2005–2019) coral monitoring data set, utilizing 2242 surveys collected along Florida's coral reefs (about 530 linear km) to explore the spatial and temporal patterns of DSD occurrence. We built predictive statistical models to test for correlations between a suite of environmental and human impact factors and the occurrence of DSD in the reef coral, Siderastrea siderea. DSD in S. siderea is a chronic disease which occurred in all 14 yrs of the study. Annual DSD prevalence ranged from 0.45% to 4.4% and the proportion of survey sites that had DSD ranged from 4.8% to 30.9%. During the study period, DSD became more widespread across Florida's coral reefs and affected a higher proportion of S. siderea populations. Spatial variations in DSD correlated with environmental and human factors which together explained 64.4% of the underlying variability. The most influential factors were concentration of silica in the surface waters (a proxy for freshwater input), the total number of coral hosts, and distance to septic areas. DSD occurred in all regions, but the highest cumulative prevalence occurred in the upper Keys on reefs around major urban centers with links to coastal water discharges. Our results support the hypothesis that coastal water quality is a key component of DSD disease dynamics in Florida and provides motivation for addressing land–sea connections to ameliorate disease occurrence in the region.

2011–2015年洞庭湖洲滩群落植物生态型、生活型和生长型组成数据集

Analyzing plant life-forms and ecotypes can provide deep insights into the relationship between plant community structure and environmental conditions, and plant life-forms play an important role in understanding the emergence, development, and succession laws of plant community. An investigation was made on the species life-forms and ecotypes of wetland plants in Dongting lake wetland. In accordance with the unified monitoring specifications of the Chinese Ecosystem Research Network (CERN), the Dongting Lake Wetland Ecosystem Observation and Research Station conducts long-term positioning monitoring on the species composition, species life-forms, growth forms and ecotypes of typical beach vegetation in the wetland ecosystem in response to the changing hydrological conditions of Dongting Lake. The dataset of long-term monitoring conducted on three typical plant communities (Carex sp., Miscanthus lutarioriparius and Polygonum hydropiper) in Dongting Lake wetland has been verified by experts to ensure the accuracy of the data. We then classified and processed the data to obtain the dataset of community composition of plant life-forms, growth forms and ecotypes on Dongting Lake beach (2011–2015). The dataset covers a total of 8 indicators, namely species name, Latin name, life-form, growth form, ecotype according to light intensity, ecotype according to water requirement, ecotype based on soil pH and ecotype according to responses sediment burial. This dataset comes with detailed background information to ensure its consistency across both spatial and temporal dimensions. This dataset comes with detailed background information about site information and site management records to ensure its consistency across both spatial and temporal dimensions. This dataset can provide support for the analysis of the classification of plant life forms and the composition and structure of ecotypes, and serve as valuable reference for further study of vegetation succession in Dongting Lake wetland.

Comparison and Validation of Multiple Medium- and High-Resolution Land Cover Products in Southwest China

The rapid advancement of remote sensing technology has given rise to numerous global- and regional-scale medium- to high-resolution land cover (LC) datasets, making significant contributions to the exploration of worldwide environmental shifts and the sustainable governance of natural resources. Nonetheless, owing to the inherent uncertainties embedded within remote sensing imagery, LC datasets inevitably exhibit inaccuracies. In this study, a local accuracy assessment of LC datasets in Southwest China was conducted. The datasets utilized in our analysis include ESA WorldCover, CLCD, Esri Land Cover, CRLC, FROM-GLC10, GLC_FCS30, GlobeLand30, and SinoLC-1. This study employed a sampling approach that combines proportional allocation and stratified random sampling (SRS) to gather sample points and compute confusion matrices to validate eight LC products. The local accuracy of the eight LC maps differs significantly from the overall accuracy provided by the original authors in Southwest China. ESA WorldCover and CLCD demonstrate higher local accuracy than other products in Southwest China, with their overall accuracy (OA) values being 87.1% and 85.48%, respectively. Simultaneously, we computed the area for each LC map based on categories, quantifying uncertainty through the reporting of confidence intervals for both accuracy and area parameters. This study aims to validate and compare eight LC datasets and assess precision and area of diverse spatial resolution datasets for mapping and monitoring across Southwest China.