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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.

In order to control the chemical composition of atmospheric air in cities located in the zone of influence of petrochemical enterprises, automatic atmospheric air monitoring stations (ASKAV) are installed. For the effective use of experimental data, the authors of the article developed a data collection system with ASKAV, in which the air control in residential quarters and the gas mixture at the source of organized emissions are synchronized. The analysis of data on the concentration of pollutants in the atmospheric air of the Sterlitamak obtained from ASKAV, on the basis of which a list of marker substances for enterprises of the city was compiled. Priority sources of air pollution with marker substances during a different wind regime were identified. The authors developed models for changing the concentration of marker substances in the air using the method of factor regression. Based on the results of the cross-correlation function, the time of movement of the contaminated gas cloud from the source of emission to the residential area of the city is obtained. Conclusions are made about the effectiveness of the developed air monitoring system and the field of application of the acquired models.

The well-known limitations of modal system identification methods have led to a broad exploration of alternative solutions for operational monitoring and damage diagnosis of structures. This study presents a time-domain Bayesian finite element model updating approach to jointly identify the vehicular loads and finite element modeling parameters of bridges using the vibration data and the location of vehicles traversing the bridge as input. A Bayesian model updating is devised and verified through a series of case studies based on numerically simulated data from a prestressed reinforced concrete box-girder bridge model. Damage states are defined for concrete degradation and delamination, steel corrosion, and loss of prestressing force. Ten different damage scenarios, encompassing the range from minor localized to major distributed damage, are examined. The responses of the damaged bridge are simulated under random traffic scenarios. The acceleration responses, along with the location of the vehicles on the bridge, are used for jointly estimating the model parameters and vehicular loads. The estimated model parameters are then used to infer the location and extent of damage within the bridge. The results show the successful performance of the proposed approach in a numerically simulated environment. [ABSTRACT FROM AUTHOR]

The increasing demand for cloud-based services, such as big data analytics and online e-commerce, leads to rapid growth of large-scale internet data centers. In order to provide highly reliable, cost effective, and high quality cloud services, data centers are equipped with sensors to monitor the operational states of infrastructure hardware, such as servers, storage arrays, networking devices, and computer room air conditioning systems. However, such coarse grained monitoring cannot provide fine grained real time information for resource multiplexing and job scheduling. Moreover, the monitoring of node level power consumption plays an important role in the optimization of workload placement and energy efficiency in data centers. In this paper, we propose an edge computing platform for intelligent operational monitoring in data centers. The platform integrates wireless sensors and on-board built-in sensors to collect data during the operation and maintenance of data centers. Using logical functions, we divide the data center clusters into grids, and then deploy wireless sensors and edge servers in each grid. As such, data processing on edge servers can reduce the latency in data transmission to central clouds and thereby enhance the real time resource mapping decisions in data centers. In addition, the proposed platform also provides predictions of resource utilization, workload characteristics, and hardware health trends in data centers.

This manuscript describes methods for evaluating the efficacy of five satellite-based Chlorophyll-a algorithms in Chesapeake Bay, spanning three separate sensors: Ocean Land Color Imager (OLCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and MODerate Resolution Imaging Spectroradiometer (MODIS). The algorithms were compared using in situ Chlorophyll-a measurements from 38 separate stations, provided through the Chesapeake Bay Program (CBP). These stations span nearly the entire 300 km length of the optically complex Chesapeake Bay, the largest estuary in the United States. Overall accuracy was examined for the entire dataset, in addition to assessing the differences related to the distance from the turbidity maximum to the north by grouping the results into the upper bay, middle bay, or lower bay. The mean bias and the Mean Absolute Error (MAE) as well as the median bias and Median Absolute Error (MedAE) were conducted for comparison. A two-band algorithm, that is based on the red-edge portion of the electromagnetic spectrum (RE10), when applied to OLCI imagery, exhibited the lowest overall MedAE of 36% at all stations. As a result, it is recommended that the RE10 algorithm be applied to OLCI and provided as an operational product through NOAA’s CoastWatch program. The paper will conclude with results from a brief climatological analysis using the OLCI RE10 algorithm.

In this work, we aim to evaluate the feasibility and operational limitations of using Sentinel-1 synthetic aperture radar (SAR) data to monitor water levels in the Poço da Cruz reservoir from September 2016–September 2020, in the semi-arid region of northeast Brazil. To segment water/non-water features, SAR backscattering thresholding was carried out via the graphical interpretation of backscatter coefficient histograms. In addition, surrounding environmental effects on SAR polarization thresholds were investigated by applying wavelet analysis, and the Landsat-8 and Sentinel-2 normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) were used to compare and discuss the SAR results. The assessment of the observed and estimated water levels showed that (i) SAR accuracy was equivalent to that of NDWI/Landsat-8; (ii) optical image accuracy outperformed SAR image accuracy in inlet branches, where the complexity of water features is higher; and (iii) VV polarization outperformed VH polarization. The results confirm that SAR images can be suitable for operational reservoir monitoring, offering a similar accuracy to that of multispectral indices. SAR threshold variations were strongly correlated to the normalized difference vegetation index (NDVI), the soil moisture variations in the reservoir depletion zone, and the prior precipitation quantities, which can be used as a proxy to predict cross-polarization (VH) and co-polarization (VV) thresholds. Our findings may improve the accuracy of the algorithms designed to automate the extraction of water levels using SAR data, either in isolation or combined with multispectral images.

This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are isolated with the use of two image segmentation methods. Experiments on the formation of fused images are presented. The result of the research is the proposed method for integrating information obtained from a two-position airborne small-sized radar system and an optical location system. The implemented method can improve the information content, quality, and reliability of the finally established fused image of the land surface.

This study justifies the prospect of using aerial imagery from unmanned aerial vehicles (UAVs) for technological monitoring and operational control of municipal solid waste landfills. It presents the results of surveys (aerial imagery) of a number of Russian landfills, which were carried out using low-cost drones equipped with standard RGB cameras. In the processing of aerial photographs, both photogrammetric data processing algorithms (for constructing orthophotoplans of objects and 3D modeling) and procedures for thematic interpretation of photo images were used. Thematic interpretation was carried out based on lists of requirements for the operating landfills (the lists were compiled on the basis of current legislative acts). Thus, this article proposes framework guidelines for the complex technological monitoring of landfills using relatively simple means of remote control. It shows that compliance with most of the basic requirements for landfill operations, which are listed in both Russian and foreign regulation, can be controlled by unmanned aerial imagery. Thus, all of the main technological operations involving waste at landfills (placement, compaction, intermediate isolation) are able to be controlled remotely; as well as compliance with most of the design and planning requirements associated with the presence and serviceability of certain engineering systems and structures (collection systems for leachate and surface wastewater, etc.); and the state of the landfill body. Cases where the compliance with operating standards cannot be monitored remotely are also considered. It discusses the advantages of air imagery in comparison with space imagery (detail of images, operational efficiency), as well as in comparison with ground inspections (speed, personnel safety). It is shown that in many cases, interpreting the obtained aerial photographs for technological monitoring tasks does not require special image processing and can be performed visually. Based on the analysis of the available world experience, as well as the results of the study, it was concluded that unmanned aerial imagery has great potential for solving problems of waste landfill management.

The regeneration of urban brownfields is a relevant strategy to limit the sprawling of post-industrial European cities. However, the integration of sustainability issues in urban brownfield regeneration projects is not a spontaneous process, remaining in most cases partial or superficial. Achieving the goals of sustainable development requires high global quality objectives, integrated into the project dynamics, and a continuous monitoring of environmental, social, and economic indicators, adapted to the specificities of brownfields. Following these considerations, an operational monitoring tool facilitating the transformation of urban brownfields into sustainable neighborhoods was created. This paper presents and discusses the verification stage of the tool, divided into two complementary tests: 1) test-applications on three case studies in Belgium, France and Switzerland and 2) interactions with the involved stakeholders. First, we describe the functioning of the tool. Then, we present the test-applications results, supported by the Val Benoit project in Liège (Belgium) as an example. They are followed by the interactions with the stakeholders that took the form of roundtable discussions. Finally, we discuss the performance of the tool looking at its potential added-value. It reveals that an adapted operational monitoring tool can facilitate sustainability assessment, reporting, decision-making and multidisciplinary collaboration. Globally speaking, the integration of monitoring practices appears not only feasible, but also realistic and desired. [ABSTRACT FROM AUTHOR]

Acoustic sensor networks (ASN) are widely used to monitor water leaks in the power generating systems. Since the ASN are used in harsh climatic conditions the failures of microphone elements of ASN are inevitable. That's why the failure detection of ASN elements is a problem of current interest. Two techniques of operational monitoring ASN are developed. Both of them are based on the placement of the test sound source within a network. The signal processing for ASN sensors had to detect the failed element. Techniques are based time difference of arrival (TDOA) estimating at the each pair of ASN elements. TDOA estimates as argmaximum of cross-correlation function (CCF) for signals on each microphone sensors pair. The M-sequence phase-shift keyed signal is applied as a test acoustic signal to ensure high accuracy of the CCF maximum estimation at low signal/noise ratio (SNR). The first technique is based on the isolation principle for TDOA sum at three points. It require to locate the test sound source in the far field. This is not always possible due to technological reasons. For the second proposed technique test sound source can be located near the ASN. It is based on a system of hyperbolic equations solving for each of the four elements of the ASN. Both techniques has been tested in the computer imitation experiment. It was found that for the SNR to –5 dB both techniques show unmistakable indicators of control quality. The second method requires significantly more time control.

The Information Service (IS) is an integral part of the Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The IS allows online publication of operational monitoring data, and it is used by all sub-systems and sub-detectors of the experiment to constantly monitor their hardware and software components including more than 25000 applications running on more than 3000 computers. The Persistent Back-End for the ATLAS Information System (PBEAST) service stores all raw operational monitoring data for the lifetime of the experiment and provides programming and graphical interfaces to access them including Grafana dashboards and notebooks based on the CERN SWAN platform. During the ATLAS data taking sessions (for the full LHC Run 2 period) PBEAST acquired data at an average information update rate of 200 kHz and stored 20 TB of highly compacted and compressed data per year. This paper reports how over six years PBEAST became an essential piece of the experiment operations including details of the challenging requirements, the failures and successes of the various attempted implementations, the new types of monitoring data and the results of the time-series database technology evaluations for the improvements towards LHC Run 3.

Desert locust swarms intermittently damage crops and pastures in sixty countries from Africa to western Asia, threatening the food security of 10% of the world’s population. During the 20th century, desert locust control operations began organizing, and nowadays, they are coordinated by the Food and Agriculture Organization (FAO), which promotes a preventative strategy based on early warning and rapid response. This strategy implies a constant monitoring of the populations and of the ecological conditions favorable to their development. Satellite remote sensing can provide a near real-time monitoring of these conditions at the continental scale. Thus, the desert locust control community needs a reliable detection of green vegetation in arid and semi-arid areas as an indicator of potential desert locust habitat. To meet this need, a colorimetric transformation has been developed on both SPOT-VEGETATION and MODIS data to produce dynamic greenness maps. After their integration in the daily locust control activities, this research aimed at assessing those dynamic greenness maps from the producers’ and the users’ points of view. Eight confusion matrices and Pareto boundaries were derived from high resolution reference maps representative of the temporal and spatial diversity of Mauritanian habitats. The dynamic greenness maps were found to be accurate in summer breeding areas (F-score = 0.64–0.87), but accuracy dropped in winter breeding areas (F-score = 0.28–0.40). Accuracy is related to landscape fragmentation (R2 = 0.9): the current spatial resolution remains too coarse to resolve complex fragmented patterns and accounts for a substantial (60%) part of the error. The exploitation of PROBA-V 100-m images at the finest resolution (100-m) would enhance by 20% the vegetation detection in fragmented habitat. A survey revealed that end-users are satisfied with the product and find it fit for monitoring, thanks to an intuitive interpretation, leading to more efficiency.

Now spanning five simultaneous open-pit operations with exploration carried out through open pit benching, Carajás complex encompasses the world´s largest iron reserves. Open pit mining operations in the area can lead to slope instabilities with risks to personnel, equipment and production due to intense excavations in rock products of low geomechanical quality, blasting practices and heavy precipitation. Thus, an effective prediction and management of surface deformations should be a key concern for the mining operations. The ground displacement monitoring techniques in Carajás include surface measurement techniques at discrete points (total station/reflective prisms) and over area using SSR (Slope Stability Radar, a ground based radar). On the other hand, DInSAR techniques are receiving relevance in the mining industry for reasons such a synoptic and continuous coverage without the need for ground instrumentation and a point-to-point good accuracy of measuring displacements (millimeter to centimeter scale) over a dense grid. Using a stack of 33 StripMap TerraSAR-X images acquired over Carajás covering the time span from March 2012 to April 2013, a monitoring approach is discussed based on the complementary use of information provided by DInSAR (DInSAR Time-Series and Persistent Scatterer Interferometry) and surface measuring techniques (total station/prisms, ground-based radar).

The structure of the wireless sensor network (WSN) for operational monitoring of soil moisture and temperature on the agricultural field with high accuracy is considered in the article. The evaluation of wireless sensor networks' technical capabilities for agricultural applications is given; the advantages and disadvantages of their use in the field conditions are noted, taking into account the influence of different factors on the quality of communication. To solve the problems of radio communication in the soil and deploying a sensor device completely below the ground surface, the prototype model of a sensor node with a retractable antenna has been developed. Sensor nodes were communicated via the ZigBee protocol and the integrated spatial attribute information was transmitted into the database.

In flood-prone areas, the delineation of the spatial pattern of historical flood extents, damage assessment, and flood durations allow planners to anticipate potential threats from floods and to formulate strategies to mitigate or abate these events. The Chenab plain in the Punjab region of Pakistan is particularly prone to flooding but is understudied. It experienced its worst riverine flood in recorded history in September 2014. The present study applies Remote Sensing (RS) and Geographical Information System (GIS) techniques to estimate the riverine flood extent and duration and assess the resulting damage using Landsat-8 data. The Landsat-8 images were acquired for the pre-flooding, co-flooding, and post-flooding periods for the comprehensive analysis and delineation of flood extent, damage assessment, and duration. We used supervised classification to determine land use/cover changes, and the satellite-derived modified normalized difference water index (MNDWI) to detect flooded areas and duration. The analysis permitted us to calculate flood inundation, damages to built-up areas, and agriculture, as well as the flood duration and recession. The results also reveal that the floodwaters remained in the study area for almost two months, which further affected cultivation and increased the financial cost. Our study provides an empirical basis for flood response assessment and rehabilitation efforts in future events. Thus, the integrated RS and GIS techniques with supporting datasets make substantial contributions to flood monitoring and damage assessment in Pakistan.

This study proposes an enhanced damping estimation procedure for flexible cable-supported bridges based on operational modal analysis (OMA). The OMA approach combines the natural excitation technique (NExT) with an eigensystem realization algorithm (ERA). An amplitude modulating (AM) function is introduced to stationarize the vehicle-induced responses. This study proposes guidelines for the selection of parameters for the NExT-ERA algorithm and AM-based stationarization. The proposed damping estimation procedure and parameter selection guidelines were applied to the data gathered from measuring an in-service cable-stayed bridge over a three-day period. The correlations between the identified damping ratios and environmental factors such as vibration level, temperature and wind velocity were investigated. The effect of aerodynamic damping during OMA is also discussed. The examined data identifies the amplitude-dependency of the damping ratios. [ABSTRACT FROM AUTHOR]

Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue “Advances in Remote Sensing of Agriculture”. To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations.

The annual risk assessment of Scots pine (Pinus sylvestris L.) stands in the federal state of Brandenburg and subsequent decisions on the application of insecticides are based on operational surveillance data on L. monacha. The objective of the study was to rationalize the operational pheromone capture method. Data from operational monitoring with pheromone-baited traps in Brandenburg were analyzed. The records included data from a total of 11,761 traps and covered the development of populations of this insect from normal to outbreak densities and back during the years 2000-2009. Statistical analysis of the data suggested that a capture period of 15 June to 10 August, which is shorter than the period used hitherto, is sufficient to determine whether more precise monitoring by a census of adult females is required at a given site in the following year owing to increasing population densities. If this suggested shorter capture period had been implemented in 2000-2009, approximately 33% fewer trap inspections would have been required.

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. [ABSTRACT FROM AUTHOR]