Ground displacement monitoring is a key aspect of assessing the impacts of underground gas storage (UGS). Conventional approaches are based on geodetic methods that, while providing high accuracy, are limited in spatial coverage and temporal resolution. This study assesses the suitability of synthetic aperture radar interferometry (InSAR) as a complement to standard ground displacement monitoring and identifies a method with sufficient accuracy to assess ground displacement conditions and facility safety. A comparative analysis was conducted using European Ground Motion Service (EGMS) data and independently derived Sentinel-1-based time series generated with the small baseline subset (SBAS) and persistent scatterer InSAR (PSI) methods. The analysis of a cavern UGS facility located in northern Poland spanned a five-year period from 2019 to 2023 and included error analysis and significance testing of differences between the methods. Observed displacement rates across the study area ranged from −4.3 mm/year for the SBAS method to −0.4 mm/year for PSI. Although the absolute values of the estimated velocities differed among the methods, the differences between the modeled deformation rates were statistically insignificant. The results confirm that InSAR can supplement geodetic monitoring and help investigate seasonal ground deformations associated with gas injection and withdrawal cycles as well as environmental processes, capturing patterns that discrete geodetic measurements may miss.
The aim of the study was to assess the sedimentation of the Klimk & oacute;wka reservoir using topographic differencing (TD) analysis of digital elevation models (DEMs) created for two periods: before the reservoir was filled and after 30 years of operation. The archival model was developed from a scanned and calibrated analogue large-scale (1:5,000) topographic map with contour lines and elevation points. The current model was obtained by integrating unmanned aerial vehicle (UAV) photogrammetry and bathymetric measurements with a GNSS-positioned dual-frequency echo sounder. The accuracy of both models was analysed in detail, taking into account cartographic errors, map shrinkage, the scanning and calibration process, and the heterogeneous accuracy of the archival elevation data. Based on the DEM difference, the estimated sedimentation volume was considered unreliable. A detailed analysis of selected cross-sections revealed local accumulation processes in the backwater zone and slope erosion, but did not permit a reliable assessment of the siltation of the entire reservoir. The authors conclude that the reliable application of the TD method requires comparing two models produced with similarly high accuracy (e.g. UAV + bathymetry), while analogue cartographic materials from the 1970s do not meet current accuracy requirements for this type of analysis.
Recent progress in LiDAR, UAV, and photogrammetric systems has made spatial data collection faster and more accessible. These tools enable the acquisition of detailed point clouds that form the foundation for many smart city applications. Efficient processing of these datasets is now a practical necessity, especially for everyday tasks such as monitoring roads and bridges, managing traffic, or building 3D city models used in digital twins. This paper reviews both classical and deep learning-based processing methods, data acquisition techniques, and multi-sensor integration strategies. Furthermore, the paper highlights applications beyond infrastructure, such as environmental monitoring of green areas and the analysis of pedestrian and bicycle networks. Despite the significant progress achieved in recent years, several open challenges remain. Among the most important are the need for standardized data formats, improved computational efficiency, and robust fusion of heterogeneous sensor data. Overcoming these difficulties is key to ensuring that digital twins and AI-based analysis become useful tools in practical urban management. Ultimately, continued progress in this field can make a meaningful contribution to the development of smarter and more sustainable cities.
Housing valuation is a concrete reflection of socio-economic inequalities in urban space. Particularly in densely populated, spatially fragmented large cities like Istanbul, the current official mass appraisal system used for property taxation fails to reflect market reality. This situation results in revenue losses in property taxes and spatial injustices. This study develops a Deep Neural Network (DNN)-based model that integrates spatially derived variables from Geographic Information Systems (GIS) and incorporates 24 objective variables related to location, structure, and access in Istanbul. The model, trained on 3,757 samples created using open-source big data, estimated housing values with high accuracy (R-2 = 0.979). The findings show that spatial differences in housing values are strongly related to urban variables such as accessibility and proximity to infrastructure. This approach not only produces housing value estimates but also provides a theoretical and methodological framework for spatial analyses of how value is produced in urban space. The study has the potential to support the development of a fair, transparent, and updatable mass appraisal system, especially for developing cities.
The study assesses the evolution of soil organic carbon (SOC) research in Ecuador between 2003 and 2023 using a bibliometric approach. The search was conducted in the Scopus, Web of Science, and SciELO databases, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. This protocol enabled the systematic identification and evaluation of the literature, resulting in a final selection of 60 peer-reviewed documents focused on SOC in Ecuador. These documents were analysed using RStudio (Bibliometrix), VOSviewer, and QGIS to map research output, collaboration networks, and thematic evolution. The main findings highlight that research on SOC in Ecuador has increased since 2003, largely due to international collaboration, primarily with institutions in Germany, Spain, and the United States. Early studies primarily focused on land-use change (deforestation and agriculture), whereas recent research emphasises remote-sensing applications, carbon stabilisation mechanisms, and nature-based solutions. Most research focuses on high-altitude provinces and protected areas, such as Loja and Chimborazo. The results also indicate that in volcanic ash soils (Andosols), altitude and land-use intensity are the main drivers of variation in SOC stocks. Key gaps include a lack of studies in the Amazon basin and coastal lowlands, as well as the absence of long-term SOC monitoring, both of which limit the development of national SOC inventories. While research output has increased steadily over time, addressing the identified gaps is necessary to establish a comprehensive scientific basis for climate change adaptation and sustainable soil management across the country's diverse ecosystems.
Droughts occurring in open-pit mining areas are becoming increasingly significant, primarily due to decreased water availability. This poses a danger because it threatens the stable development of society and agricultural production and contributes to increased dust emissions that may interfere with mining operations. Climate change further intensifies these threats. Therefore, research into water availability, continuous monitoring, and environmental health indicators is vital, as the water cycle greatly impacts these factors. The paper aims to investigate drought severity in two large open-pit lignite mines in Tur & oacute;w and Be & lstrok;chat & oacute;w, and Legnica-G & lstrok;og & oacute;w Copper District (LGOM). The Combined Climatological Drought Index (CCDI) was used, alongside the water budget (WB), to characterise drought at the study sites. High consistency between the indices was observed throughout most of the studied period until 2018. Notably, significant reductions in water availability were recorded from 2018 onwards in the areas of the three studied mines.
This study analyzes the environmental impact of the EU car fleet and investi-gates the relationship between its age structure, motorization rate, the share of hybrid and electric vehicles, and carbon emission levels, considering both overall trends and regional variations based on population income. The meth-odology employed descriptive, comparative, and correlational analyses using recent data from European statistical and institutional sources. The findings indicate that despite the growing adoption of alternative-fuel vehicles (AFVs), the EU car fleet continues to age, and emissions show only a negligible reduc-tion. Significant regional disparities were identified, with wealthier nations maintaining younger, more environmentally sustainable vehicle fleets. Fur-thermore, air pollution measured by carbon dioxide emissions was positively correlated with vehicle age and negatively correlated with both the share of AFVs and the proportion of newly registered vehicles. The research highlights the tension between the objectives of the circular economy and the crucial need for accelerated fleet modernization, proposing directions for advancing sus-tainable mobility and environmentally responsible consumer behavior.
Ground displacement monitoring is a key aspect of assessing the impacts of underground gas storage (UGS). Conventional approaches are based on geo-detic methods that, while providing high accuracy, are limited in spatial cov-erage and temporal resolution. This study assesses the suitability of synthetic aperture radar interferometry (InSAR) as a complement to standard ground displacement monitoring and identifies a method with sufficient accuracy to assess ground displacement conditions and facility safety. A comparative anal-ysis was conducted using European Ground Motion Service (EGMS) data and independently derived Sentinel-1-based time series generated with the small baseline subset (SBAS) and persistent scatterer InSAR (PSI) methods. The anal-ysis of a cavern UGS facility located in northern Poland spanned a five-year period from 2019 to 2023 and included error analysis and significance testing of differences between the methods. Observed displacement rates across the study area ranged from-4.3 mm/year for the SBAS method to-0.4 mm/year for PSI. Although the absolute values of the estimated velocities differed among the methods, the differences between the modeled deformation rates were sta-tistically insignificant. The results confirm that InSAR can supplement geodetic monitoring and help investigate seasonal ground deformations associated with gas injection and withdrawal cycles as well as environmental processes, captur-ing patterns that discrete geodetic measurements may miss.
Prolonged and recurrent droughts are a problem of the 21st century. Agricul-ture, grazing, fires, logging, and mining make soil susceptible to permanent degradation. However, well-managed land can recover from long drought cy-cles. Because drought is increasingly affecting larger areas, continuous moni-toring and risk assessment are essential. Satellite-based models provide global observations of the Earth and enable their assessment using indices, thereby supporting the classification of the examined areas. In this study, the Com-bined Climatological Deviation Index (CCDI) and the Water Storage Deficit In-dex (WSDI) were calculated to evaluate drought sensitivity in Europe, within its climatic zones according to the K & ouml;ppen-Geiger classification. Based on the research, it was concluded that almost all areas show a tendency towards dry-ing, and the predictions indicate that the current drought conditions and their pace will continue. The CCDI and WSDI are very useful in studies of drought in Europe.
Accurate detection of built-up areas in semi-arid regions is vital for urban planning and environmental monitoring. However, built-up surfaces and bare soils often produce very similar spectral responses. As a result, this similarity causes confusion in satellite image classification. Additionally, spectral overlap among urban materials, bare soil, and sparse vegetation further complicates detection. This study evaluates several spectral indices, including DBSI, NDTI, NDVI, BRBA, and BSI, combined with Principal Component Analysis (PCA) to enhance built-up area extraction from Sentinel-2A imagery. Images captured during the driest season were selected to maximize spectral contrast. Three classification schemes based on Support Vector Machine (SVM) were tested. The first scheme used DBSI, NDTI, and NDVI. The second used BRBA, NDTI, and NDVI. The third relied on PCA-derived components. The results indicate that the PCA-based approach achieved the highest classification accuracy at 95%. In comparison, the DBSI/NDTI/NDVI combination reached 93%, while the BRBA/NDTI/NDVI scheme achieved 92%. Therefore, PCA helps reduce spectral confusion and enhances the identification of built-up areas in semi- arid environments. Overall, combining multiple spectral indices with dimensionality reduction offers a reliable method for urban analysis using Sentinel-2 imagery.
Urbanization is rapidly transforming the spatial and socioeconomic landscape of many emerging cities in Nepal, yet relatively little research has explored these dynamics outside the Kathmandu Valley. This study applies a cellular automata-Markov (CA-Markov) model to simulate and predict land use and land cover (LULC) changes in Surkhet Valley, the core of Birendranagar Municipality, one of Nepal's fastest-growing urban centers. Using Landsat imagery from 1999, 2009, and 2019, alongside spatial and socioeconomic factors, the model captures historical LULC transitions and projects future changes for the years 2029, 2039, and 2049. Model validation was conducted against the 2019 classified LULC map, yielding an overall agreement of 80.65% and a standard kappa statistic of 70.31%, confirming the model's predictive reliability. Results indicate a clear trajectory of urban expansion at the expense of agricultural land. Built-up surfaces is projected to more than double - from 12.43 km(2) in 2019 to 31.38 km(2) in 2049, while cultivated land is expected to decline by over 20 km(2 )in the same period. Spatial analysis shows urban growth intensifying around existing centers, highways, and transitional ecotones between forest and cultivation zones. Compared to similar studies in Kathmandu and Biratnagar, Surkhet exhibits a higher normalized rate of urban expansion, highlighting its emerging role in regional development. This research underscores the value of remote sensing and spatial modeling in urban planning and land management. The findings provide essential insights for policymakers to guide sustainable development in Surkhet and other rapidly urbanizing areas across Nepal.
The objective of this article is to determine the extent to which the inclusion of the benefit principle affects the amount of compensation in expropriation procedures for road investments. An additional area of research is the identification of discrepancies in the practical application of this principle in the context of administrative court case law. The analysis examines the practical consequences of adopting different interpretations for specific properties subject to expropriation. It demonstrates how the chosen interpretation of the benefit principle affects the calculated compensation. The findings indicate the need to revise the regulations regarding the methodology for estimating real estate value, which forms the basis for determining compensation for expropriation.
This research aims to analyze elevation change as an adaptation strategy for flooding, identify the dynamics and patterns of adaptation and collaboration between actors, and evaluate the impact of elevation change on the urban environment of Mamminasata, Indonesia. This research uses qualitative methods with interpretative phenomenological analysis (IPA). Data collection involved observations, in-depth interviews, and documentation. The results show that elevation changes in buildings, land, and road infrastructure trigger an increase in elevation adaptation strategies by actors while expanding the flood-affected areas as a consequence of such measures. The nature of actors' strategies varies; communities tend to be spontaneous, independent, and informal, while developers and the government employ business strategies and policy support tactics. Consequently, these strategies are fragmented among actors because of the lack of uniformity in elevation regulations. This reflects a pattern of long-term urban adaptation, even in the absence of formal coordination or regulation. Elevation changes negatively impact ecosystems, land stability, and social and physical connectivity. This study recommends the application of elevation changes in planning regulations for zoning and land-use management, as well as collaborative governance, to support adaptation strategies of actors and promote sustainable urban resilience.
Population growth and urban development in Indonesia have led to an in-creased demand for efficient road infrastructure to support economic activities and human mobility. The street network plays a crucial role in shaping the spatial structure and morphological characteristics of a city. This study aims to identify street network orientation, entropy, and circuity patterns and to clas-sify their typologies across eight metropolitan cities in Indonesia. The analysis utilized secondary data from OpenStreetMap (OSM), processed through the OpenStreetMap NetworkX (OSMnx) software. The three main variables ana-lyzed were orientation, entropy, and circuity. The results reveal that metropol-itan cities in Indonesia exhibit varying street network patterns, which include both grid-like and dispersed forms. Based on the clustering analysis, the cities were grouped into four clusters: Cluster 1 comprises only Medan; Cluster 2 includes Surabaya; Cluster 3 comprises Bandung, Jakarta, Makassar, and Se-marang; and Cluster 4 contains Bandar Lampung and Palembang. The results show that cities in different clusters require distinct planning approaches due to the varying characteristics of their street networks. These findings provide valuable insights into the organization and structure of urban street networks, offering a foundation for more efficient and sustainable transportation infra-structure planning at the national level.
Urban expansion has become the most significant threat to rice fields, particularly in medium-sized cities. Surakarta is a medium-sized city experiencing rapid growth, surrounded by regencies that are also undergoing urban expansion. This study aims to investigate the presence of rice fields under the pressures of urban expansion dynamics over a 32-year multitemporal period covering 1990, 1995, 2000, 2005, 2010, 2015, 2020, and 2022. This research utilized remote sensing data processed through Geographic Information Systems (GIS). Supervised classification was applied to identify land cover over the 32-year period, including the extent of rice fields. The average annual urban expansion rate (AUER) and urban expansion intensity index (UEII) analyses were used to determine urban expansion's magnitude, speed, and direction. This study found that the most significant and fastest rate of urban expansion in Surakarta City occurred between 2020 and 2022. The results also showed that areas adjacent to the urban core (Surakarta City) experienced greater expansion speed than areas located farther away. Urban expansion pressure also resulted in a 40% loss of rice fields, with Klaten District experiencing the greatest loss. Maintaining rice fields in peri-urban areas can provide dual benefits by supplying rice for the city while preserving the local ecosystem. The dynamics of urban expansion in the Surakarta urban agglomeration, as revealed by this research, are essential for sustainable spatial planning in the region.
Bantul and Yogyakarta are regions with earthquake-hazard risks in Indonesia. The earthquake that occurred in 2006 produced deaths, high economic losses, and significant damages to the housing and infrastructure. This research aimed to assess the urban growth in the earthquake-hazard zone in Bantul and Yogyakarta. The study used the remote sensing method of nighttime light (NTL), zonal statistics, and ClockBoard zone analysis. The combination of these analysis techniques for linking urban growth and earthquake hazards has not been widely discussed by previous studies. The earthquake-hazard data was retrieved from the United States Geological Survey website; meanwhile, the NTL data was based on the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite. The results indicated that those zone segments at very high earthquake-hazard levels were also areas with night-light intensities of more than ten units (meaning increasing urban growth). Based on these facts, local governments should evaluate spatial planning to limit the density of built-up areas in earthquake-hazard areas and ensure the effective implementation of urban sustainability and resilience.
The use of emissions-intensive motorized transport for school commuting, particularly in urban areas, is highly concerning. Restricting the use of motorized transport and encouraging independent school mobility provides an opening for emissions reduction. Previous research has demonstrated that independent mobility is a function of various sociodemographics. The present study aims to examine the potential for reducing carbon emissions from children's school commute through the utilization of smart mobility tracking, with travel distance and sociodemographics as determinants for primary school children in Semarang City, Indonesia. The children's mobility patterns for school commutes were recorded with portable GPS tracking devices. The data were processed using GIS to analyze routes and distances. Sociodemographic characteristics related to independent mobility were examined using logistic regression. The study estimated the actual and potential carbon emissions resulting from school commute. Travel distance, along with some of the sociodemographic traits, was analyzed to identify children's potential for independent mobility and the resulting emissions reduction. The findings indicate that increasing the chance of children's independent mobility could considerably contribute to lowering carbon emissions related to school commutes.
This study explores the relationship between population growth and urban expansion as well as their impacts on climate and environmental parame-ters in Berau Regency, Indonesia. Using night-light data and land use/land cover (LULC) analysis from 2019 through 2023, the research identified signif-icant urban growth, with night-lit areas doubling and a population increase from 232,290 to 280,990. Urban expansion led to notable land conversion, re-ducing vegetated areas by 18,202.38 ha, while built-up and open land grew by 11,768.6 ha and 5,989.74 ha, respectively. These changes impacted environ-mental conditions, with non-vegetated areas experiencing higher land-surface temperatures (31-34 degrees C) and lower rainfall (5,000-6,000 mm/year) compared to the cooler and wetter vegetated areas (20-21 degrees C; 7,000-8,000 mm/year). The findings emphasized vegetation's role in regulating temperature and rainfall, highlighting the environmental risks of urbanization and the need for sustain-able land management to mitigate climate impacts in growing cities.
Population expansion and climate change have significantly affected the coastal environment in Lampung, Indonesia, mainly through the conversion of man-groves into shrimp-farming ponds. This transformation requires effective mon-itoring to evaluate its impacts on coastal ecosystems and local livelihoods, as shrimp farming is a major income source in East Lampung. This research im-proves aquaculture detection and monitoring along the eastern coast of Lam-pung by integrating several water indices such as the normalized difference water index (NDWI), modified NDWI (MNDWI), water ratio index (WRI), and a newly developed water index (WI), within the cloud-based Google Earth En-gine (GEE) platform to capture spatial and temporal variations. Reference data were derived from the 2019 Regional Medium-Term Development Planning Doc-ument (RPJMD) and high-resolution Google Earth imagery for accuracy assess-ment. Results showed that WRI combined with the Otsu's thresholding method achieved the highest performance, with an overall accuracy (OA) of 93.3% and a kappa coefficient (kappa) of 86.7%. Analysis from 2018 to 2022 showed a decline in aquaculture area from 8,407.35 ha to 3,415.50 ha, aligned with statistical data on shrimp production, which decreased from 24,202 t to 8,041 t. These results indicate that the method provides a rapid and effective tool for detecting aqua-culture changes, enabling local authorities to strengthen coastal management for sustainable development, ecosystem protection, and livelihood support.
The interconnected porosity of soil provides conduit channels for the down-ward infiltration of water into the subsurface; this occurs in soil layers and within soil-less areas or geologic formations. The lithology and geological structure significantly influence the infiltration capacity of soils and are crucial in determining whether the infiltration water continuously reaches an aquifer or becomes stagnant in the saturated soil. An artificial neural network (ANN) algorithm was employed to model the actual infiltration rate, incorporating soil texture and soil moisture along with geological scores as inputs and ac-tual infiltration rates as outputs. This study aimed to quantify qualitative geo-logical data and incorporate it into ANN model parameters. The development of the ANN infiltration model involved two serial trial-and-error experiments to determine the optimal number of nodes in the hidden layer, ranging from nodes c(4,2) to c(12,2), one serial experiment with geological input, and the other without geological input. Throughout the model testing, metrics such as MAE, RMSE, and MSE were recorded, and the first and second optimum models were identified when employing c(9,2) nodes of hidden layers. The re-sulting model can be used to predict actual infiltration and will be beneficial for hydrometeorological-disaster mitigation and city-development planning.
