Exact Location Research Articles

A koronavírus-járvány társadalmi és gazdasági hatásai a határ menti térségek fejlődésére a magyar–szlovák határrégiók esetében

The Social and Economic Impact of the Coronavirus Pandemic on the Development of Border Regions in the Case of the Hungarian–Slovak Border Regions Borders in a given social context seem to be eternal and unchangeable, but their exact location, their permeability, their functions and the regulations assigned to them are in fact constantly changing according to the ideals of the respective societal eras. The SARS-CoV-2 neo-coronavirus infection, identified in December 2019 as the cause of Covid-19 disease, has become a global pandemic, causing a major health, economic and social crisis. The pandemic had an impact on the single market mechanisms established by the European Union, affecting freedoms—the free movement of services, goods, capital and people—and on almost every aspect of everyday life as it had been until then. In border regions, these effects were magnified. The active socio-economic interactions of the Hungarian–Slovak border regions were interrupted overnight as a result of the border traffic measures, causing significant negative changes and restructuring the functioning of cross-border processes.

Dentigerous cysts in horses: A retrospective study

SummaryBackgroundDentigerous cysts are well‐known congenital defects in horses, and the literature on this condition is extensive. Recently, a third type of dentigerous cyst, associated with an exostosis arising from the temporal bone, has been described. Today, computed tomography (CT) is often available and provides detailed information about the exact location and extent of dentigerous cysts, allowing for the description of new findings.Study designThis is a case series outlining the clinical, radiographic and computed tomographic features of dentigerous cysts in seven horses presented to the Equine Clinic at the Vetsuisse Faculty, University of Zurich, Switzerland.ObjectivesThe aim of this case series was to describe the radiographic and computed tomographic findings, and the occurrence of exostoses arising from the temporal bone, in horses with dentigerous cysts.MethodsThe medical records of the Equine Hospital at the University of Zurich were reviewed for horses admitted with dentigerous cysts between 2007 and 2023. Data collected and analysed included patient signalment, clinical history, clinical signs and their duration prior to referral, diagnostic imaging results, treatment and outcomes.ResultsThe most common location for the cysts was the base of the ear, although in one case, the maxillary sinus was involved. Of the seven cases, four were Icelandic horses. CT proved superior to standard radiography for determining the extent and precise location of the cysts, as well as for identifying dental material and exostoses in the temporal region. CT also facilitated detailed surgical planning. All cysts were surgically removed with good long‐term outcomes.Main limitationsOverall, the incidence of dentigerous cysts in horses was low. The relatively high occurrence of dentigerous cysts in Icelandic horses in this study is an interesting finding, though the small sample size calls for further research.ConclusionsTwo of the cases in this series were similar to a recently described variant of a ‘temporal dentigerous cyst associated with an exostosis in the temporal region’ and were closely connected to the bony structure and draining tract.

A Two-Stage Damage Localization Method for Structural Sealants Based on Boundary Modal Curvature

To facilitate the maintenance of hidden frame glass curtain walls, it is necessary to identify the damage location of the structural sealant accurately. However, detecting concealed sealant damage directly is challenging. This paper proposes a two-stage damage localization method to pinpoint the center of damaged structural sealants. In the first stage, an assurance criterion of boundary modal curvature (ACBMC) is developed to identify damaged edges. In the second stage, the exact damage location is determined by comparing the differences in boundary modal curvature between the undamaged and damaged edges. Two-panel units are analyzed by numerical and model experiments. The results show that the proposed method can effectively identify the damage location of the structural sealant when the damage severity is higher than 4%.

A semi-probabilistic Bayesian method to identify the number and location of potential sources in 3D unconfined aquifer using limited observed concentration

Source identification of a contaminant has always been challenging for accurately modeling groundwater transport. Source identification problems are classified into several parts, such as identifying the location of contamination, the strength of contamination, the time the contaminant is introduced into the groundwater, and the duration of its activity. Identifying the sources considering all the parts as variables increases the computational complexity. Reducing the number of variables in source identification problems is necessary for a swift solution through optimization approaches. The most challenging variable in source identification modeling is the location of contamination, as it is a discrete variable for almost all the numerical solutions of groundwater models. In this research study, we have created a methodology to narrow the location of contamination from a random distribution throughout the aquifer to a reasonable number of probable locations. Although methods to identify the location of contamination were devised earlier, we have attempted an approach of combining a particle tracking approach with Bayesian method of updating the probabilities as a novel approach, where the observation data is limited. We have considered the aquifer parameters and observation well data and devised a method with a Lagrangian approach to particle movement to identify the potential source locations. We have refined the source locations to a narrower probability distribution using the Bayesian method of updating the probability through new information of refined grid space. We have tested the models to identify the potential sources with different hypothetical problems and identified the sources in advective dominant transport with an average probability of 0.53, diffusion dominant transport with an average probability of 0.62, heterogenous soils with an average probability of 0.99, anisotropic aquifer with an average probability of 0.91, and aquifer with irregular boundary with an average probability of 0.96 to identify the location nearest to the actual contaminant source. The results are satisfactory in identifying the number of potential sources with an accuracy of 88 % (15 identified out of 17 sources with a probability greater than 0.4) and their locations in the aquifer with a probability of 0.223 for exact location identification. The probability of finding a source nearest to the actual location is 0.745 at an average distance of 11.6 m from the actual source location.

Geomasking to Safeguard Geoprivacy in Geospatial Health Data

Geomasking is a set of techniques that introduces noise or intentional errors into geospatial data to minimize the risk of identifying exact location information related to individuals while preserving the utility of the data to a controlled extent. It protects the geoprivacy of the data contributor and mitigates potential harm from data breaches while promoting safer data sharing. The development of digital health technologies and the extensive use of individual geospatial data in health studies have raised concerns about geoprivacy. The individual tracking data and health information, if accessed by unauthorized parties, may lead to privacy invasions, criminal activities, and discrimination. These risks underscore the importance of robust protective measures in the collection, management, and sharing of sensitive data. Geomasking techniques have been developed to safeguard geoprivacy in geospatial health data, addressing the risks and challenges associated with data sharing. This entry paper discusses the importance of geoprivacy in geospatial health data and introduces various kinds of geomasking methods and their applications in balancing the protection of individual privacy with the need for data sharing to ensure scientific reproducibility, highlighting the urgent need for more effective geomasking techniques and their applications.

Synonymic notes in the Southeast Asian genus Lutera Westwood, 1875 (Coleoptera: Scarabaeidae: Rutelinae)

To resolve the taxonomic confusion surrounding two Asian Rutelinae species, Lutera nigromaculata Ohaus, 1900 and L. nigrita Ohaus, 1930, all primary types of relevant taxa have been revisited. A redescription of L. nigromaculata is given, and diagnosis and color illustrations are provided for both L. nigromaculata and L. nigrita. Parastasia ohausi Chûjô, 1966 (new synonym), an unnecessary replacement name for and junior objective synonym of L. nigromaculata, was found in a neglected publication by the late Michio Chûjô. A lectotype is designated for L. nigromaculata guttata (Frey, 1970) (new synonym) which is recognized as another junior synonym of L. nigromaculata. All known distribution records of the two species are mapped, and L. nigromaculata is newly recorded from India and Vietnam. The exact location of “Lakhon” in Indo-China, which is currently Nakhon Phanom of Thailand, is also discussed.

Discovering Electric Vehicle Charging Locations Based on Clustering Techniques Applied to Vehicular Mobility Datasets

With the proliferation of vehicular mobility traces because of inexpensive on-board sensors and smartphones, utilizing them to further understand road movements have become easily accessible. These huge numbers of vehicular traces can be utilized to determine where to enhance road infrastructures such as the deployment of electric vehicle (EV) charging stations. As more EVs are plying today’s roads, the driving anxiety is minimized with the presence of sufficient charging stations. By correctly extracting the various transportation parameters from a given dataset, one can design an adequate and adaptive EV charging network that can provide comfort and convenience for the movement of people and goods from one point to another. In this study, we determined the possible EV charging station locations based on an urban city’s vehicular capacity distribution obtained from taxi and ride-hailing mobility GPS traces. To achieve this, we first transformed the dynamic vehicular environment based on vehicular capacity into its equivalent urban single snapshot. We then obtained the various traffic zone distributions by initially utilizing k-means clustering to allow flexibility in the total number of wanted traffic zones in each dataset. In each traffic zone, iterative clustering techniques employing Density-based Spatial Clustering of Applications with Noise (DBSCAN) or clustering by fast search and find of density peaks (CFS) revealed various area separation where EV chargers were needed. Finally, to find the exact location of the EV charging station, we last ran k-means to locate centroids, depending on the constraint on how many EV chargers were needed. Extensive simulations revealed the strengths and weaknesses of the clustering methods when applied to our datasets. We utilized the silhouette and Calinski–Harabasz indices to measure the validity of cluster formations. We also measured the inter-station distances to understand the closeness of the locations of EV chargers. Our study shows how CFS + k-means clustering techniques are able to pinpoint EV charger locations. However, when utilizing DBSCAN initially, the results did not present any notable outcome.

A Scoping Review on the Utility of Ultrasound to Visualize Bursae in Anatomical Dissection Courses

Bursitis is a common condition in clinical practice, often causing pain in the shoulder and buttock areas due to inflamed bursae. Proper diagnosis and treatment depend on knowing the presence and exact location of these bursae. Anatomy classes typically provide limited instruction on bursae because they are difficult to demonstrate during dissection courses. High-resolution ultrasound is an essential and versatile technique for detecting bursitis, and it could also serve as a valuable tool for students to better understand bursae. Relevant studies were screened in the following databases: CENTRAL, MEDLINE, BIOSIS Previews, EMBASE, and Web of Science Core Collection. Grey literature was also considered. Literature was screened on January 3, 2023. Only ultrasound investigations in human cadaver bursae were included, specifically using B-Mode ultrasound. The general characteristics of the included studies and the ultrasound-guided approaches for labeling the bursae were analyzed and examined. T The search found 8,899 matches, but only 15 met the criteria. Fifteen different bursae were studied, and 12 studies were included in the analysis. Both the marking substrate and the injected volume varied. Despite a high overall accuracy of 99% achieved using ultrasound-guided labeling approaches in the included studies, caution is advised due to the small sample size (1 to 24). The current study serves as a review to examine ultrasound studies on bursae in human cadavers. Ultrasound-guided labeling techniques achieve high accuracy and could be a valuable teaching tool in dissection courses. These techniques help visualize difficult-to-dissect structures and provide students with an understanding of sonoanatomy.

The Imageology Study and Clinical Application of Perforating Branch of Posterior Tibial Artery.

It is necessary to precisely locate the branches for better result of flap transplantation for the high variability of perforating branches of posterior tibial artery (PTA). In the research, digital subtraction angiography (DSA) and high-frequency ultrasound are jointly used to study the distribution and exact location of PTA perforating branches to guide clinical practice. From January 2020 to December 2022, 16 patients underwent DSA examination and 5 of them accepted further high-frequency ultrasound examination. The authors analyzed the distribution, number, location, direction, and lengths of PTA branches and used PTA perforator flaps to repair middle and inferior lower leg defects according to the above imaging findings. The donor site was repaired with skin grafts or a sequential PTA perforator flap. A total of 81 branches of PTA were identified in 16 patients. Most of the perforating branches were in the middle and distal thirds of tibia. There was a significant correlation between the length of perforating branches and their perforating sites. Cluster analysis showed that larger branches appeared most frequently in the range of 15 to 20cm from the tip of the inner ankle. 5 cases of PTA perforator flaps survived well. DSA can clearly show the distribution of PTA and its branches, especially the dominant ones. The imaging findings can effectively guide PTA perforator flap design and harvesting, and thus improve the result of flap transplantation.

Investigation of some issues of automation of elements of terminal complexes for transshipment of large-capacity containers

The topic of the work is the study of the reasons for increasing the efficiency of the use of marine container transportation in the entire supply chain. It is noted that the increase in their efficiency is explained by the possibility of transporting various types of cargo, the speed of handling vehicles, as well as the compactness of storage and safety of cargo. The problem of increasing ship capacity on the terms of shipping companies, which currently exists and is being solved by container terminal operators, has been formulated, which allows minimizing the time of ships parking in the port for processing, which in turn can be achieved by increasing the capacity of ships. Attention is drawn to the fact that reducing costs by increasing the efficiency of lifting and transport equipment stimulates investments in automation systems for almost all terminal operations, and in particular in lifting and transport equipment systems. It is emphasized that the automation process of container terminals differs depending on the task at hand (a new project is being considered or work is being carried out to automate an existing terminal). The implementation of automation at existing terminals is a more difficult task due to the problems of compatibility with the types of activities performed and resistance to ongoing changes, which primarily involve systematic and repetitive technological processes that can be programmed. Attention is drawn to the fact that container terminals use the latest operating systems to control and optimize the movement of containers inside the warehouse area (in particular, they are used at terminals where gantry cranes on a pneumatic (RTG) and on a rail (RMG) are used), and it is also emphasized that when working with RTG technology gantry cranes on a pneumatic vehicle are equipped with sensors connected to satellite positioning systems, which are located on the terminal area and on wheeled carts and are brought to the place of cargo operation by signals from the control room, at the same time, the dock operator controls only when lowering or lifting the container. Such information systems are also used on terminals operating using RMG technology. It is noted that automation of berthing container cranes is currently under development, since the complexity of its implementation lies in the fact that during pitching the vessel has a roll and a deflector, therefore, when moving it, it is difficult to determine the exact location of the container and the alignment of the spreader horizon with the container horizon, as a result, the guidance process? It requires additional time, which leads to a decrease in work productivity.

Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks.

New approach for accurate discrimination and location of power transformers with different internal winding faults.

Power transformers are essential elements in power systems and thus their protection schemes have critical importance. In this paper, a scheme is proposed for accurate discrimination and location of internal faults in power transformers using conventional measuring devices attached to the transformer. Different types of internal winding faults are intensely considered: partial discharge, inter-disk faults, series and shunt short circuit faults and axial displacement. Depending on the transformer measured output voltage, input voltage and the input current, the construction of a locus diagram (ΔV-Iin) serves as an indicator for any physical modification to the winding. Using five suggested features extracted from the developed locus, an artificial neural network (ANN) technique is applied to accurately distinguish any deviation from the transformer healthy condition. The exact location of each fault inside the windings of power transformer is then determined. The obtained results validate the usefulness of the proposed scheme for different internal faults. The superiority of the proposed scheme is extensively examined by comparing its results with some published schemes.

Clinico-Radiological Manifestations of Cochlear Schwannomas

Background and Objectives: Cochlear schwannomas, which are categorized into intracochlear and intravestibulocochlear schwannomas (ICs and IVCs, respectively) are rare and may cause hearing loss (HL). The affected region is invariably correlated with tumor location, which can be detected on magnetic resonance imaging (MRI). We describe the cochleovestibular manifestations of ICs and IVCs. Subjects and Methods: The study included 31 patients with ICs or IVCs. Tumor extent and exact locations were delineated using MRI. Types of HL were subcategorized into the low-to-mid frequency (250 Hz to 1 kHz), mid-to-high frequency (>1 kHz), and all-frequency (universal) HL groups. Results: The tumors involved the entire cochlear turn (two ICs) or extended beyond the cochleae (nine IVCs) in 11 patients, and 20 ICs were located in specific locations as follows: 14 in the basal, 3 in the middle, and 3 in the middle and apical turns. No patient showed tumor invasion of the internal auditory canal or middle ear. The pattern of HL usually reflects the location or extent of a tumor. We observed HL at all frequencies, at low-to-mid frequencies, and at mid-to-high frequencies in 13, 4, and 14 patients, respectively. Dizziness or tinnitus was observed in >50% of patients. Surgical tumor removal was performed in 10 patients, and the remaining patients are undergoing annual monitoring. Conclusions: Cochlear schwannomas may be associated with HL, which may worsen over time and reflect tumor location. Therefore, these lesions should be considered in the differential diagnosis in patients who present with idiopathic, fluctuating, progressive or sudden HL.

Gaussian process-based online sensor selection for source localization

This paper addresses the sensor selection problem for source localization within cyber–physical systems (CPSs). While recent machine learning and reinforcement learning approaches aim to optimize sensor selection and placement within the Area of Interest (AoI), their need for intensive data collection and training precludes online operation. Furthermore, these methods often require prior knowledge of the unknown source’s characteristics and lack adaptability to the dynamic nature of CPSs, leading to inefficiencies in unseen environments. This paper addresses these shortcomings using Gaussian process Optimization coupled with an active sensor selection mechanism to locate the unknown source within the AoI. The proposed approach first builds a probabilistic model of the environment, which is discretized into a grid, without prior training using a Gaussian Process surrogate model. Next, the model iteratively and systematically learns the underlying spatial phenomenon using Gaussian Process optimization. Concurrently, the approach selects a subset of sensors by optimizing a fitness function that advocates selecting informative and energy-efficient sensors. Next, the probabilistic model, having accurately learned the environment, directs the algorithm to the unknown source by identifying the cell with the highest likelihood of containing it. Finally, a peak refinement step is performed, which computes the exact location of the source within the designated cell. The proposed method’s efficacy is validated through experiments in radioactive source localization, validation studies, and adaptability assessments across various environments. In terms of quality of localization (QoL), it outperforms recent localization benchmarks, such as a reinforcement learning-based approach and DANS, by around 18% and 100%, respectively.

Lipoma: neoplasm beyond boundaries

Lipoma is one of the commonest mesenchymal tumours arising from fat cells of the adult type. It is benign in nature in majority of cases. However, conversion to liposarcoma is described especially in lipomas situated in the retroperitoneum and thigh. Lipomas may be associated with variety of familial syndromes. In such situations they are mostly multiple. Typical physical signs of lobulated nature, slipping sign and pseudo-fluctuation are diagnostic of benign lipomas. MRI is essential to confirm the exact location, extent and infiltration of the surrounding tissues especially when malignant change is suspected. FNAC helps in establishing the diagnosis. Meticulous complete excision is the mainstay of treatment taking utmost care to avoid breach of capsule. Recurrence rate is extremely low.

Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences.

Dysphagia is the inability or difficulty to swallow normally. Standard procedures for diagnosing the exact disease are, among others, X-ray videofluoroscopy, manometry and impedance examinations, usually performed consecutively. In order to gain more insights, ongoing research is aiming to collect these different modalities at the same time, with the goal to present them in a joint visualization. One idea to create a combined view is the projection of the manometry and impedance values onto the right location in the X-ray images. This requires to identify the exact sensor locations in the images. This work gives an overview of the challenges associated with the sensor detection task and proposes a robust approach to detect the sensors in X-ray image sequences, ultimately allowing to project the manometry and impedance values onto the right location in the images. The developed sensor detection approach is evaluated on a total of 14 sequences from different patients, achieving a F1-score of 86.36%. To demonstrate the robustness of the approach, another study is performed by adding different levels of noise to the images, with the performance of our sensor detection method only slightly decreasing in these scenarios. This robust sensor detection provides the basis to accurately project manometry and impedance values onto the images, allowing to create a multimodal visualization of the swallow process. The resulting visualizations are evaluated qualitatively by domain experts, indicating a great benefit of this proposed fused visualization approach. Using our preprocessing and sensor detection method, we show that the sensor detection task can be successfully approached with high accuracy. This allows to create a novel, multimodal visualization of esophageal motility, helping to provide more insights into swallow disorders of patients.

A hybrid niching memetic algorithm for multi-modal optimization of double row layout problem

Double row layout problem (DRLP) involves identifying the exact locations of machines participating in a production task on two rows. There are typically multiple layouts with approximately optimal material handling cost for a DRLP. These layouts often exhibit significantly different layout configurations. Identifying multiple global or local optimal layouts can provide layout designers with a wide range of options, which is of great significance for enhancing the maintainability, scalability, and customisability of the facility. However, most existing studies on DRLPs typically focus on designing a single optimal layout. In this paper, we study a multi-modal optimization of double row layout problem (MDRLP). A hybrid approach combing a fast niching memetic algorithm and linear programming (FNMA-LP) is proposed for MDRLP to locate multiple global or local optimal layouts with a similar quality. First, a fast niching memetic algorithm is developed to find a set of approximate optimal machine sequences. Then, LP is employed to optimise the exact locations of machines for each machine sequence. To evaluate the performance of the proposed algorithm, FNMA-LP is compared against three popular multi-modal algorithms and a state-of-the-art single-modal algorithm developed for DRLP. Experiments show that our approach outperforms competing approaches on almost all problem instances.

Aeroacoustic analysis of flow induced resonance in a gas pipeline

Flow induced noise and vibration in the pipework of gas production platforms can lead to fatigue failures of attached small-bore pipes, and this can constrain the operating conditions of the platform. Typically such problems comprise a source of turbulence in the flow, an aeroacoustic feedback mechanism due to acoustic resonances inside the pipework, and mechanical resonances which amplify the resulting vibration. The problem described here occurred in the gas metering system of the platform, with the source located in the header which comprised a bifurcation into two metering lines and a number of sharp bends. Because of the cost of rectifying the problem an in-depth study was carried out to analyse the flow and induct acoustic conditions. The predictions will support recommendations for an efficient redesign of the entire system to control the problem at source. Various levels of CFD modelling (RANS and URANS) were carried out using the Openfoam code to predict the exact location and mechanism of the vortex shedding. The acoustic response of the complex ductwork was then modelled using the ACTRAN code to understand the feedback mechanism which led to resonance and high levels of response.

Robust unsupervised 5D seismic data reconstruction on regular and irregular grids

Seismic data reconstruction has become a central focus in seismic data processing, addressing challenges posed by sparse sampling due to physical and budgetary constraints. The advent of 5D acquisition methodologies marks a significant advancement in the quality and completeness of seismic data sets. Most traditional 5D reconstruction methods commonly use the fast Fourier transform (FFT), requiring regular grids and preliminary 4D binning before 5D interpolation. Discrete Fourier transform and nonequidistant FFT can honor the original irregular coordinates. However, when using exact locations, these methods become computationally expensive. We introduce an unsupervised deep-learning methodology to learn a continuous function across the sampling points in seismic data, facilitating reconstruction on regular and irregular grids. The network comprises a multilayer perceptron with linear layers and element-wise periodic activation functions. It excels at mapping the input coordinates to the corresponding seismic data amplitudes without relying on external training sets. The network’s intrinsic low-frequency bias is crucial in prioritizing acquiring self-similar features over high-frequency and incoherent ones during training. This characteristic mitigates incoherent noise in seismic data, such as random and erratic components. To assess the robustness of the unsupervised reconstruction technique, we conduct comprehensive evaluations using synthetic data examples sampled regularly and irregularly, as well as field-data examples with and without binning. The findings demonstrate the efficacy of our deep-learning framework in achieving resilient and accurate seismic data reconstruction across diverse sampling scenarios.

Measuring Temporal Change in Scrub Vegetation Cover Using UAV-Derived Height Maps: A Case Study at Two UK Nature Reserves.

Measuring the outcome of practical interventions and actions helps to inform conservation management objectives and assess progress towards objectives and targets. Measuring success also informs future management by identifying actions that are effective and those that are not. Scrub vegetation is an important habitat type in terrestrial ecosystems, providing important shelter and food resources for biodiversity and livestock. Much of practical land management in the UK involves the monitoring and management of scrub, and current drone-based methods of scrub collection requires expensive equipment or complex methods. A 2021 paper determined a cheap and simple way to determine scrub levels, and this could potentially be used to map temporal changes, as well as identify directional change in scrub. This study looks at whether the method outlined in the 2021 study could be used to measure temporal and directional changes in scrub cover on two nature reserves in the UK: Daneway Banks in Gloucestershire and Flat Holm Island in the Severn Estuary. Scrub levels at Daneway Banks increased from 14.63% in 2015 to 16.52% in 2017, before decreasing to 14.89% in 2021 due to managed cutting and clearing. Scrub cover at Flatholm Island decreased from 10.18% in 2019 to 8.71% in 2021. The exact locations of scrub growth and loss for each site was also calculated and mapped. This approach was found to be a viable way of measuring temporal and directional change in scrub levels. The data can also be used to reframe changes in scrub levels as a shift towards vegetation succession or reduction, to better visualise how changes in scrub levels affect overall site management goals, and is a cheaper, more accessible alternative to current methods of measuring temporal vegetation changes.