Search Results (21)

The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton. Full article

The transition to 100% renewable energy systems is critical for achieving global sustainability and reducing dependence on fossil fuels. Island power systems, due to their geographical isolation, limited interconnectivity, and reliance on imported fuels, face unique challenges in this transition. These systems’ vulnerability to supply–demand imbalances, voltage instability, and frequency deviations necessitates tailored strategies for achieving grid stability. This study conducts a systematic review of the technical and operational challenges associated with transitioning island energy systems to fully renewable generation, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. Out of 991 identified studies, 81 high-quality articles were selected, focusing on key aspects such as grid stability, energy storage technologies, and advanced control strategies. The review highlights the importance of energy storage solutions like battery energy storage systems, hydrogen storage, pumped hydro storage, and flywheels in enhancing grid resilience and supporting frequency and voltage regulation. Advanced control strategies, including grid-forming and grid-following inverters, as well as digital twins and predictive analytics, emerged as effective in maintaining grid efficiency. Real-world case studies from islands such as El Hierro, Hawai’i, and Nusa Penida illustrate successful strategies and best practices, emphasizing the role of supportive policies and community engagement. While the findings demonstrate that fully renewable island systems are technically and economically feasible, challenges remain, including regulatory, financial, and policy barriers. Full article

The Voronoi diagram on the Earth’s surface is a significant data model, characterized by natural proximity and dynamic stability, which has emerged as one of the most promising solutions for global spatial dynamic management and analysis. However, traditional algorithms for generating spherical raster Voronoi diagrams find it challenging to dynamically adjust the Voronoi diagram while maintaining precision and efficiency. The efficient and accurate construction of the spherical Voronoi diagram has become one of the bottleneck issues limiting its further large-scale application. To this end, this paper proposes a dynamic construction scheme for the spherical Voronoi diagram based on the QTM (Quaternary Triangular Mesh) system, with the aim of enabling efficient generation, local updates, and multi-scale visualization of the spherical Voronoi diagrams. In this paper, canonical ordering is introduced. Tailored for the properties of the spherical triangular grid, it constructs a unified and standardized sorting strategy for the dilation of the spherical grids. The construction and updating of the spherical Voronoi diagram are achieved through the ordered dilation of sites. Furthermore, the multi-scale visualization of the spherical Voronoi diagram is realized through the hierarchical structure of the QTM. The paper presents our algorithm intuitively through pseudocode, conducts comparative experiments on the feasibility and efficiency, and designs an experiment for the dynamic navigation and management of ocean-going vessels based on the global multi-resolution Voronoi diagram. The experimental results demonstrate that our algorithm effectively controls the error of the generation of the raster Voronoi diagram and has a significant efficiency advantage when processing dynamic environments. Full article

The construction of digital twin cities is a current research hotspot; GIS technology and BIM technology are widely used in the field of digital twin cities. However, BIM is still subject to major limitations in its applications, mainly due to huge amounts of model data, low query efficiency and accuracy, non-uniform marking systems, etc. The reason is that the BIM model itself focuses more on the expression of visual effects and lacks spatial calculation ability and the utilization of spatial location information. Secondly, the current lightweight processing methods for BIM models are mostly based on geometric transformation and rendering optimization, focusing more on the data compression and visual quality of the model, which essentially does not change the data structure of the BIM model, and it is difficult to establish the mapping relationship between spatial location and spatial data, information, and resources. In addition, current coding methods proposed for BIM models are mostly based on the line classification method, which realizes the identification of components based on the classification of their attributes, and the location information is stored according to the attributes or natural language descriptions, which need to be parsed and translated when they are used, and this procedure ignores the importance of spatial location in daily management and emergency management. The importance of spatial location in daily management and emergency management is also ignored. Based on this kind of identification code, it is impossible to directly analyze and apply spatial location data. Therefore, this paper takes the combination of GIS technology and BIM technology as the starting point and proposes a BIM data modeling method based on the BeiDou grid code, based on the efficiency of its underlying data organization and the accuracy of its real geographic location expression on the one hand and the completeness of the information expression by BIM and fine three-dimensional visualization on the other hand. Finally, a series of experiments are carried out based on the method. Through visualization modeling and efficiency experiments, different feature models are meshed to verify the feasibility and efficiency of the model. Through coding and information query experiments, the model′s data organization capability, data dynamic carrying capability, and efficient spatial computation capability and practical application capability are verified. Full article

Renewable, decentralised, and citizen-centred energy paradigms have emerged as feasible and reliable alternatives to the traditional centralised fossil-based infrastructure. In this scenario, energy storage systems (ESSs) are enabling technologies to boost the stability and flexibility of the power grid in the short-to-medium term, allowing local communities to envision energy autonomy in the medium term. Traditionally, ESSs have been installed in individual households for their own benefit. However, new storage paradigms focusing on building clusters and district scale have illustrated the need to revise the role of ESSs and to pay close attention to the social factors, while devising implementation strategies for scaling up these new energy infrastructural models. This study reviews recent research trends (2021–2023), proposing three integrated social pillars for the implementation of ESSs: (i) multi-dimensional geographical and institutional scales of ESSs; (ii) social components of spatial and temporal flexibility of ESSs; and (iii) co-creation approaches to devising ESS implementation strategies. These pillars point out the necessary social factors for the implementation of ESSs at scale, highlighting future research perspectives to operationalise such factors, with a particular focus on the importance of citizens’ perception, participation, and collaboration, which are critical for maximising the benefit of sharing and exchanging renewable energy locally. Development of flexible and agile digital platforms that facilitate the co-creation of adaptable socio-technical solutions to adopting ESSs is proposed. The need to tailor these solutions to suit the stakeholders’ capabilities is emphasized. Full article

Considering how difficult it is for a pin in the ocean to be found, painstaking searches among historical documents and eyewitness accounts often end up with more unknowns and questions. We developed a three-tier geo-spatial tech-based approach to discover and unfold the lost WWII heritage features in the countryside of Hong Kong that can be applied in other contexts. It started with an analysis of historical texts, old maps, aerial photos, and military plans in the historical geographic information system (HGIS) Project ‘The Battle of Hong Kong 1941: a Spatial History Project’ by Hong Kong Baptist University to define regions/points of interest. Then, 3D point clouds extracted from the government’s airborne LiDAR were migrated to form a digital terrain model (DTM) for geo-registration in GIS. All point clouds were geo-referenced in HK1980 Grid via accurate positioning using the global navigation satellite system—real-time kinematics (GNSS-RTK). A red relief image map (RRIM) was then used to image the tunnels, trenches, and pillboxes in great detail by calculating the topographical openness. The last tier of the tech work was field work involving ground validation of the findings from the previous two tiers and on-site imaging using terrestrial LiDAR. The ground 3D LiDAR model of the heritage feature was then built and integrated into the DTM. The three-tier tech-based approach developed in this paper is standardised and adopted to streamline the workflow of historical and archaeological studies not only in Hong Kong but also elsewhere. Full article

This paper aimed at assessing the technical and economic potential of using rooftop solar photovoltaic (PV) systems in Lithuanian urban areas to support energy and climate policy formation and its implementation in the country. A bottom-up approach was applied. A number of apartment (AP), commercial (COM) and public (PUB) buildings, electric vehicle (EV) charging infrastructures and solar PV systems in the largest Lithuanian cities were analyzed, and the results were tailored to national-wide estimates. LiDAR and photogrammetry data of building topologies, geographical information systems (GIS), digital maps for rooftop measurements, factual solar photovoltaic (PV) performance modeling, average profiles of solar PV systems, statistical data of energy consumption in buildings, levelized cost of energy (LCOE) models and the literature review method were used. The results of the assessment of selected buildings and their categories reveal that the rooftop area per installed PV unit was 14.1–18.3 m²/kW in AP buildings, followed by 18.0–18.6 m²/kW in COM and 6.1–18.3 m²/kW in PUB buildings; the factor of rooftop suitability for solar PV system installation varied in ranges of 31.5–85.4%, 48.9–99.4% and 50.1–88.2%, respectively. Corrected by expert judgement, these estimates served as inputs for the assessment of technical and economic potentials. Furthermore, energy generated by solar PV could be consumed locally with little supplied to the distribution grid, resulting in no or a limited need for expensive investments in infrastructure. In addition, locating an EV charger on the premises of COM or PUB buildings could improve the localized consumption of solar energy. Thus, rooftop solar PV systems in urban Lithuanian areas are preferable. The results of the technical potential analysis show that, on the rooftops of AP buildings, 272 MW could be installed, followed by 78 MW on roofs of PUB and 67 MW on COM buildings. If technical potential is extracted, EUR 332.7 million can be invested. The cost of energy produced by solar PV systems was estimated at 2.34–5.25 EURct/kWh, which is significantly lower than the prices of market and retail electricity, standing at 23 EURct/kWh and 24 EURct/kWh (with support from the government) in 2022, respectively. The research’s limitations are defined by the fact that average values were applied to estimate the technical and economic potential of rooftop solar PV systems; a limited number of building categories were analyzed; the category of private houses, which is minor in cities, was missed, and rural areas with a great number of private houses were not covered by this study. Full article

The application of the automated analysis of remote sensing data processed into high-resolution digital terrain models (DTMs) using geographic information systems (GIS) tools provides a geomorphometric characterization of the diversity of the relief of loess patches over large areas. Herein, a quantitative classification of 79 loess patches with a total area of 3361 km², distributed within the eastern part of the Polish Uplands belt, is carried out. A high-resolution 1 × 1 m DTM was generated from airborne laser scanning (ALS) data with densities ranging from 4 pts/m² to 12 pts/m², which was resampled to a resolution of 5 × 5 m for the study. This model was used to classify landform surfaces using the r.geomorphon (geomorphon algorithm) function in GRASS GIS software. By comparing the values in the neighborhood of each cell, a map of geomorphometric features (geomorphon) was obtained. The classification and typology of the relief of the studied loess patches was performed using GeoPAT2 (Geospatial Pattern Analysis Toolbox) software. Pattern signatures with a resolution of 100 × 100 m were extracted from the source data grid, and the similarity of geomorphological maps within the signatures was calculated and saved as a signature file and segment map using the spatial coincidence method. The distance matrix between each pair of segments was calculated, and the heterogeneity and isolation of the maps were generated. R system was used to classify the segments, which generated a dendrogram and a heat map based on the distance matrix. This made it possible to distinguish three main types and eight subtypes of relief. The morphometric approach used will contribute to a better understanding of the spatial variation in the relief of loess patches. Full article

Owing to geographical and political influences, Korea has an independent electric power system and the highest density of electric power facilities in the world. Large-scale base power generation complexes are located in non-metropolitan coastal areas, while the most expensive combined cycle power plants are operating or under construction in the metropolitan areas, which have the largest electricity demand. It has become difficult to secure a site for power plants, and the existing power transmission network is insufficient because of the additional construction of generators in existing power generation complexes, the increase in capacity of facilities, and the rapid increase in new and renewable energy. In particular, the East Coast region has a problem of transient stability for this reason, which is being addressed in advance through power generation restrictions. In addition, TCSC (Thyristor Controlled Series Capacitor) is installed and operated to expand the capacity of existing power transmission lines and improve stability in the failures of nearby high-voltage lines until new transmission lines that take more than 10 years are installed to resolve power generation restrictions. However, after the construction of a new transmission line, the efficiency of the existing TCSC is degraded, and for efficient use, it is necessary to rearrange the installation location to utilize the optimal TCSC according to changes in the configuration of the nearby power system. Moreover, a detailed analysis is needed on whether the TCSC designed according to the existing grid configuration exhibits accurate control performance even after the relocation and whether it interferes with nearby generators. In addition to the dynamic performance based on real-time simulations, it is necessary to study the control performance and interaction for this power electronic equipment. This study verified the need to change controller parameters, the interaction effect with nearby generators for stable operation, and the system effect of TCSC relocation using the same replica controller as the actual field controller, as well as RTDS (Real Time Digital Simulation) simulating the entire power transmission system. Full article

With the increase in the penetration rate of renewable energy sources, a machine-learning-based forecasting system has been introduced to the grid sector to improve the participation rate in the electricity market and reduce energy losses. In these studies, correlation analysis of mechanical and environmental variables, including geographical figures, is considered a crucial point to increase the prediction’s accuracy. Various models have been applied in terms of accuracy, speed calculation, and amount of data based on a mathematical model that can calculate the wake; however, it can be difficult to derive variables such as air density, roughness length, and the effect of turbulence on the structural characteristics of wind turbines. Furthermore, wake accuracy could decrease due to the excessive variables that come from the wake effect parameters. In this paper, we intend to conduct research to improve prediction accuracy by considering the wake effect of wind turbines using supervisory control and data acquisition (SCADA) data from the Dongbok wind farm. The wake divides the wind direction into four parts and then recognizes and predicts the affected wind turbine. The predicted result is the wake wind speed and its conversion to power generation by applying a power curve. We try to show the efficiency of machine learning by comparing the wake wind speed and the power generation in the wake model. This result shows the error rate using evaluation metrics of regression, such as mean squared error (MSE), root mean squared error (RMSE), and weighted absolute percentage error (WAPE), and attempts to verify power system impact and efficiency through a real-time digital simulator (RTDS). Full article

Machine Learning (ML) algorithms have been used as an alternative to conventional and geostatistical methods in digital mapping of soil attributes. An advantage of ML algorithms is their flexibility to use various layers of information as covariates. However, ML algorithms come in many variations that can make their application by end users difficult. To fill this gap, a Smart-Map plugin, which complements Geographic Information System QGIS Version 3, was developed using modern artificial intelligence (AI) tools. To generate interpolated maps, Ordinary Kriging (OK) and the Support Vector Machine (SVM) algorithm were implemented. The SVM model can use vector and raster layers available in QGIS as covariates at the time of interpolation. Covariates in the SVM model were selected based on spatial correlation measured by Moran’s Index (I’Moran). To evaluate the performance of the Smart-Map plugin, a case study was conducted with data of soil attributes collected in an area of 75 ha, located in the central region of the state of Goiás, Brazil. Performance comparisons between OK and SVM were performed for sampling grids with 38, 75, and 112 sampled points. R² and RMSE were used to evaluate the performance of the methods. SVM was found superior to OK in the prediction of soil chemical attributes at the three sample densities tested and was therefore recommended for prediction of soil attributes. In this case study, soil attributes with R² values ranging from 0.05 to 0.83 and RMSE ranging from 0.07 to 12.01 were predicted by the methods tested. Full article

The digital age has brought about an explosion in the growth of data, of which data with a geographical component stands out. Proper use of geographical data comes with the need for correct coordinate reference systems (CRSs). They are considered the ultimate binder for interoperability between geospatial data actors and stakeholders. Moreover, CRSs are crucial for the visual and analytical integration of geospatial data from disparate data sources. However, CRSs might be—for numerous reasons—incorrectly assigned or even missing. The result is a time-consuming study of the map, literature, and available resources to ultimately find the alleged right CRS. This study provides a summary of prevailing resources from national mapping agencies of some European countries to address the above problem. Secondly, and most importantly, is the development of an open-source Python-based software package. This software package aims to accurately estimate the best candidate CRS, given a tuple of coordinates at a priori an approximately known location. It is controlled by geocoding the known location and intersecting the resulting coordinate with the bounding box of all CRSs in the EPSG-database. An in-depth review of CRS tools by mapping authorities reveals, in particular, limitations concerning the countries’ spatial areas, in combination with often required know-how of local CRSs. To address these shortcomings, our tool is developed to enable a more generic extraction of CRSs for any given location worldwide. Testing proved successful for 30 different maps, with a grid present on the map and the CRS of the map being included in the EPSG-database. Full article

Unmanned Aerial Systems, or drones, are very helpful tools for managing open-pit mining operations and developing ecological restoration activities. This article presents a method for identifying water erosion processes in active quarries by means of drone imagery remote sensing, in the absence of pre-existing imagery or mapping for comparison. A Digital Elevation Model (DEM) with a spatial resolution (SR) >10 cm and an orthophoto with an SR >2.5 cm were generated from images captured with a drone and their subsequent photogrammetric processing. By using Geographical Information Systems tools to process the DEM, a detailed drainage network was obtained, the areas of detected water erosion were separated, and the watersheds in the gullies identified. Subsequently, an estimated DEM before the erosive processes was reconstructed by interpolating the gully ridges; this DEM serves as a reference for the relief before the erosion. To calculate the volume of eroded material, the DEM of Differences was calculated, which estimates the volume difference between the previously estimated DEM and the current DEM. Additionally, we calculated the material necessary for the geomorphological adaptation of the quarry and the slope map, which are two valuable factors closely related to the monitoring of erosive processes. The results obtained allowed us to identify the erosion factors quickly and accurately in this type of mining. In the case of water-filled quarries, it would be important to characterize the subsurface relief. Essentially, the presented method can be applied with affordable and non-invasive materials to create digital grid maps at 10 cm resolution, obtaining data ready for 3D metrics, being a very practical landscape modelling tool for characterizing the restoration evolution of open-pit mining spaces. Full article

Although ground-based synthetic aperture radar (GB-SAR) interferometry has a very high precision with respect to deformation monitoring, it is difficult to match the fan-shaped grid coordinates with the local topography in the geographical space because of the slant range projection imaging mode of the radar. To accurately identify the deformation target and its position, high-accuracy geocoding of the GB-SAR images must be performed to transform them from the two-dimensional plane coordinate system to the three-dimensional (3D) local coordinate system. To overcome difficulties of traditional methods with respect to the selection of control points in GB-SAR images in a complex scattering environment, a high-resolution digital surface model obtained by unmanned aerial vehicle (UAV) aerial photogrammetry was used to establish a high-accuracy GB-SAR coordinate transformation model. An accurate GB-SAR image geocoding method based on solution space search was proposed. Based on this method, three modules are used for geocoding: framework for the unification of coordinate elements, transformation model, and solution space search of the minimum Euclidean distance. By applying this method to the Laoguanjingtai landslide monitoring experiment on Hailuogou Glacier, a subpixel geocoding accuracy was realized. The effectiveness and accuracy of the proposed method were verified by contrastive analysis and error assessment. The method proposed in this study can be applied for accurate 3D interpretation and analysis of the spatiotemporal characteristic in GB-SAR deformation monitoring and should be popularized. Full article

It is necessary to periodically obtain topographic maps of the geographical and environmental characteristics of tidal flats to systemically manage and monitor them. Accurate digital elevation models (DEMs) of the tidal flats are produced while using ground control points (GCPs); however, it is both complicated and difficult to conduct GPS surveys and readings of image coordinates that correspond to these because tidal flat areas are not easy to access. The position and distribution of GCPs affect DEMs, because the entire working area cannot be covered during a survey. In this study, a least-squares height-difference (LHD) DEM matching method with a polynomial model is proposed to increase the number of DEM grids while using a presecured precise DEM to rectify the distortion and bowl effect produced by unmanned aerial vehicle (UAV) images. The most appropriate result was obtained when the translation parameters were quadratic curve polynomials with an increasing number of grids and the rotation parameters were constant. The experimental results indicated that the proposed method reduced the distortion and eliminated the error caused by the bowl effect while only using a reference DEM. Full article