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This article studies the monitoring of oak dieback in forests of the Centre-Val de Loire region (France), where drought-induced dieback has become a major concern due to climate change. The main objective of the study is to evaluate the applicability of multispectral satellite time series for operational monitoring of forest dieback. Using in situ data collected from 2017 to 2022 on approximately 2700 oak plots, a multiyear mapping of the analyzed region was performed using the random forest algorithm and Sentinel-2 images. Our results show that it is possible to detect oak dieback accurately (average overall accuracy = 80% and average balanced accuracy = 79%). A spatial cross-validation analysis also evaluates the performance of the model on regions that were never encountered during training, across all years, resulting in a slight decrease in accuracy ($\sim$5%). The study also highlights the importance of measuring the stability and performance of the classification model over time, in addition to standard cross-validation metrics. A feature analysis shows that the shortwave infrared part of the spectrum is the most important for mapping forest dieback, while the red-edge portion of the spectrum can increase the stability of the model over time. Overall, both in situ data and model predictions showed evidence of forest decline in many areas of the study region. Our results suggest that large areas of forest can decline over short periods of time, highlighting the interest of satellite data to provide timely and accurate information on forest status.

This research examines the potential of digital twin (DT) technology for reformation within China’s traditional solid-wood-panel processing industry, which currently suffers from production inefficiencies and the slow adoption of digital technology. The research centers around developing a digital twin system, elucidating improvements in manufacturing efficiency, waste management, process simulation, and real-time monitoring. These capabilities facilitate immediate problem solving and offer transparency in the process. The digital twin system is comprised of physical, transport, virtual, and application layers, employing a MySQL database and using the Open Platform Communications Unified Architecture (OPC UA) protocol for communication. The application of this system has led to heightened production efficiency and better material use in the solid-wood-panel manufacturing line. Integrating the dynamic selection adaptive genetic algorithm (DSAGA) into the virtual layer drives the system’s efficiency forward. This evolved approach has allowed for an enhancement of 8.93% in the scheduling efficiency of DSAGA compared to traditional genetic algorithms (GAs), thereby contributing to increased system productivity. Real-time mapping and an advanced simulation interface have strengthened the system’s monitoring aspect. These additions enrich data visualization, leading to better comprehension and a holistic process view. This research has ignited improvements in solid-wood-panel production, illustrating the tangible benefits and representing progress in incorporating digital technology into traditional industries. This research sets a path for transforming these industries into smart manufacturing by effectively bridging the gap between physical production and digital monitoring. Furthermore, the adjustability of this approach extends beyond solid-wood-panel production, indicating the capability to expedite movement towards intelligent production in various other manufacturing sectors.

The Online Monitoring System (OMS) at the Compact Muon Solenoid experiment (CMS) at CERN aggregates and integrates different sources of information into a central place and allows users to view, compare and correlate information. It displays real-time and historical information. The tool is heavily used by run coordinators, trigger experts and shift crews, to ensure the quality and efficiency of data taking. It provides aggregated information for many use cases including data certification. OMS is the successor of Web Based Monitoring (WBM), which was in use during Run 1 and Run 2 of the LHC. WBM started as a small tool and grew substantially over the years so that maintenance became challenging. OMS was developed from scratch following several design ideas: to strictly separate the presentation layer from the data aggregation layer, to use a well-defined standard for the communication between presentation layer and aggregation layer, and to employ widely used frameworks from outside the HEP community. A report on the experience from the operation of OMS for the first year of data taking of Run 3 in 2022 is presented.

The introduction of Synthetic Aperture Radar (SAR) through the Oceans and Coasts Monitoring Information system (OCIMS) has therefore become a critical tool for operational coastal and ocean monitoring in South Africa. Natural/man-made ocean phenomena can be observed using satellite Synthetic Aperture Radar (SAR) techniques, making it an integral part of large ocean area monitoring for maritime domain awareness. SAR is an active instrument on-board a satellite platform that transmits electromagnetic waves and measures the returning signal to infer what is happening on the ocean surface. The objective of this paper was to provide information and present options to the decision-makers around sustainability of commercial SAR data acquisition in support of OCIMS IVT. The OCIMS IVT tool identifies ships from multiple types of SAR data, which can provide locations of vessels that have switched off their AIS and oil slicks caused by vessels. Although open-data offerings, such as Sentinel-1, can be considered alternatives to commercial sensors, they present severe limitations for an operational service. To meet OCIMS requirements, and address the imperative to reduce costs, a hybrid of commercial acquisition being used in conjunction with an open data source such as Sentinel-1 is recommended. This would ensure that an operational service with the desired latency as well as increasing the revisit time over the EEZ.

An innovative approach for the automation of operational monitoring activities in agricultural and forestry tasks is described and discussed in this article. This approach can be considered as a solution for Precision Agriculture and Precision Forestry applications and can be used as an information and communication technology (ICT) tool for the management aims by a variety of agricultural and forestry companies. The aim of the proposed concept is to develop a system, composed of both hardware and software units, with the ability to collect and manage operative raw data and then to translate them into operational information that will be used in decision-making processes. All the procedures will be carried out automatically, in order to ensure an objective compilation of the field activity register. Thus, the entrepreneur will have all the operative information automatically updated in a dedicated database system. All the obtained documents can then be used for certification and traceability processes, if required by the procedural guideline, as well as to satisfy any other management tasks, including the estimation of the actual operative costs of the farm.

We analyze the efficacy of using a Do-It-Yourself (DIY) modular instrument to estimate the diffuse attenuation coefficient (Kd) of photosynthetically active radiation (PAR), which can be used for operational oceanography in turbid shallow waters. This parameter evaluates the water transparency, as it summarizes several water components providing an indicator for water quality. Historically, water transparency has been measured with a simple and inexpensive tool: the Secchi disk. Although it contributes a valuable index of visual water clarity, the quality of its measurements is user-dependent, and it does not enable the automatic monitoring of the water quality. For this reason, we need electronic devices to get accurate measures and facilitate long-term evaluations for water quality monitoring. This paper has two main objectives: First, to present the KduPRO, a low-cost and DIY moored instrument. The KduPRO is an evolution of the KdUINO buoy, that provides an estimation of the water transparency in coastal areas and continental waters, with an automatic quality control parameter that makes this sensor suitable for operational observing systems; and second, to provide a replicability analysis associated to the uncertainty of its Kd estimations. This instrument is based on a modular system of light sensors, independent of each other, measuring the irradiance at different depths. This study analyses the performance of the KduPRO with other reference commercial instruments, the performance between different modules of the same system and finally, a case study of measuring the water quality in Loch Leven (a lake in Scotland). The affordable cost, ease of use and measurement repeatability make this instrument a potentially valuable tool for anyone interested in monitoring water quality.

A dataset of street light images is presented. Our dataset consists of ∼350 k images, taken from 140 UMBRELLA nodes installed in the South Gloucestershire region in the UK. Each UMBRELLA node is installed on the pole of a lamppost and is equipped with a Raspberry Pi Camera Module v1 facing upwards towards the sky and lamppost light bulb. Each node collects an image at hourly intervals for 24 h every day. The data collection spans for a period of six months.Each image taken is logged as a single entry in the dataset along with the Global Positioning System (GPS) coordinates of the lamppost. All entries in the dataset have been post-processed and labelled based on the operation of the lamppost, i.e., whether the lamppost is switched ON or OFF. The dataset can be used to train deep neural networks and generate pre-trained models providing feature representations for smart city CCTV applications, smart weather detection algorithms, or street infrastructure monitoring. The dataset can be found at 10.5281/zenodo.6046758.

The issues of image fusion in a two-position small-size radar on-board operational monitoring system are considered. The aim of present research is to develop a method for fusion of images of the land surface based on data obtained from a multi-sensor spatially distributed on-board location system implemented on the basis of a UAV. The method of combining different-angle location images is implemented iteratively. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are highlighted using two image segmentation methods. The final result is a proposed method for information fusion from a two-position on-board small-sized radar system and an optical location system. The implemented method of fusion makes it possible to increase the information content, quality and reliability of information about the observed underlying surfaces and the physical objects detected on them. The practical significance of the results obtained lies in the formation of integral information in real time in the interests of environmental reconnaissance, monitoring in hard-to-reach and dangerous places for human life, as well as in order to promptly prevent natural and man-made emergencies.

In 2017–2019, a seismic swarm was triggered in the Maurienne valley (French Alps), with more than 5000 events detected by the regional SISmalp network. The population, who asked SISmalp to provide information on the processes and the associated risk, felt many earthquakes. In a post-L’Aquila trial context, we conducted a reflection on the scientific and social operational management of the crisis. The geological and tectonic analysis, the deployment of a temporary seismic network, an automatic double-difference relocation procedure (HypoDD) after clustering earthquakes, as well as the interactions with the population and the risk managers, have been carried out jointly. The length and unpredictability of the sequence complicated crisis management and the relations between local authorities and civil protection. The involvement of SISmalp, beyond its main scientific and observation prerogatives, has contributed to moderate the fears of the population by providing scientific explanations.

Florida has a distinct wet season, which serves the annual water needs of the State and beyond. Our earlier studies have indicated that in addition to the seasonal rainfall anomalies, the variations of the length of the season also contribute significantly to variability of the wet season over Florida. Furthermore, the variations of the onset date of the rainy season relate significantly to the seasonal anomalies of length of the season and rainfall. In this study, we have used the National Aeronautics and Space Administration's (NASA's) Integrated Multi-Satellite Retrievals for Global Precipitation Mission version 6 (IMERG) rainfall dataset to monitor the rainy season over the five Water Management Districts (WMDs) of Florida for 2021. This effort was complimented by analyzing and verifying the variations of the rainy season over the preceding 20 wet seasons from the IMERG datasets. IMERG produces rainfall datasets at various latencies, with the final product having a 3.5-month latency since the satellite measurements of radiance are made. However, in this study, we find that an intermediate 12-h latency product of rainfall analysis from IMERG is reasonable to use for near real-time monitoring of the wet season over Florida. The operational monitoring of the 2021 wet season using the 12-h latency dataset from IMERG was also supplemented with the extended weather 6- to 10-day and 8- to 14-day forecasts of precipitation probability issued by the NOAA's Climate Prediction Center. Our study suggests that the current methodology of monitoring the onset date variations of the rainy season provides a viable alternative to assess and anticipate the seasonal variations amidst the moderate to insignificant weather and climate prediction skill of the numerical forecast models of the wet season of Florida.

Hydrogeological survey of wider Majdanpek mining area in the Carpathian mountain arch of eastern Serbia, including open mine pits, tailings sites, and major karstic springs and caves has been undertaken in order to estimate environmental conditions in groundwater body (GWB) “Krš–sever”, groundwater quality, and to investigate causes of their earlier indicated poor chemical status. Bearing in mind that Majdanpek copper mine field is directly bordering the karst aquifer and delineated GWB “Krš–sever”, the two karst springs namely Valja Fundata and Kaludjerica were in situmeasured and sampled in high and low-water periods (spring 2019, late autumn 2019 and spring 2020). Sampling and analysis of groundwater were carried out under the frame of project “Operational Monitoring of Groundwater of the Republic of Serbia”, established by the Ministry of Environmental Protection of Serbia. The field measurements of unstable chemical components and physical properties as well as laboratory analyses confirmed very poor and even hazardous water quality of both surveyed springs Valja Fundata and Kaludjerica. Registered concentrations of some ions, such as Fe2+, Mn2+, Ca2+, SO4 2–are high above maximal permitted level for potable water in Serbia. High turbidity rate also confirms impact of colloidal suspensions from the tailing which is located in karstic blind valley. Leakage of mine water passes through joints, open cavities and even large cave system Valja Fundata. Results of undertaken survey confirm that low-water period results with worse water quality and much higher concentration of hazardous substances than that characterized high-water season when infiltrated rainy water and/or melted snow dilute tailing’s wastewater. Strict application of environmental protection measures and de - sign/construction of the smaller water treatment facility at both surveyed springs should possibly mitigate the impacts of mining activities to karst groundwater and dependant ecosystem.