Geomatics and Environmental Engineering

Satellite-Based Urban Heat Island Study: A Prisma-Based Systematic Literature Review

2025-07-21T18:43:15+02:00

Over the years, urban heat island (UHI) has emerged as a significant contributor to global warming, thereby necessitating considerable attention. Currently, satellite technology is a basic tool for the future – particularly, for its effective and efficient urban analysis. Thus, this study aims to assess the progress of existing satellite-based UHI studies by reviewing scientific publications that were released between 1972 and early 2024. Moreover, we observed that 1991 was a pivotal year, marking the integration of satellite technologies into the development of UHI monitoring and identification systems based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review methodology examines the UHI phenomenon by focusing on its characteristics based on sensors, algorithms, and accuracy. The results of the systematic review revealed that Landsat and MODIS were the most-deployed sensors for UHI identification and monitoring, while the land surface temperature (LST) indicator and normalized difference vegetation index (NDVI) were the most-deployed algorithms. Regarding accuracy, the integration of satellite sensors and algorithms into UHI studies provides a promising range of accuracies. The review found that the future of satellite-based UHI monitoring is promising, with technological advancements driving the development of effective techniques such as data fusion, gap filling, machine learning (ML), and deep learning. Additionally, Google Earth Engine (GEE) is a cloud-based platform for performing large-scale geospatial analyses, which facilitates the assessments of local, regional, and global-scale UHIs. Finally, the other review findings for future directions indicated that future satellite-based UHI studies will prioritize six crucial points: enhancing data resolution, integrating satellite data with ground-based sensors, artificial intelligence, and ML, climate change modeling, and a global study of UHIs and their impacts.

Evaluating Spatial-Plan Consistency Through Probabilistic Machine-Learning Land-Use/Land-Cover Suitability: Insights from Bogor Regency, Indonesia

2025-07-31T11:42:45+02:00

Sustainable development is contingent upon the efficient management of land resources for resolving spatial challenges such as land-use conflicts and fragmentation. A land-suitability model offers a potential instrument for assessing land-use/land-cover (LULC) consistency with spatial plans. This study employed a data-driven probabilistic approach using a support vector machine (SVM) algorithm and error-correcting output codes (ECOCs) for incorporating 11 physical parameters to generate spatial grids that reflected land-suitability levels. The probabilistic outputs were derived by calibrating SVM decision values using Platt scaling within the ECOC framework, enabling a reliable estimation of class-wise landsuitability probabilities. The model achieved the highest probability value of 0.9952, with an average of 0.8251; this demonstrated its potential for assessing the consistency of land use/land cover with spatial plans. The model exhibited robust performance and substantial agreement between the predictions and actual data, with an overall accuracy of 88.56% and a kappa index of 0.873. Additionally, the study utilized a land-suitability model and non-weighted overlay relevance matrix to identify discrepancies in Bogor Regency’s spatial plan, quantifying the compliant and noncompliant land areas for each LULC class within specified spatial-plan zones. The evaluation revealed a significant misalignment, with 25–45% of agricultural land uses that included wetland and dryland agriculture, plantations, and inland fish farms being allocated within settlement zones; this indicated a mismatch between spatial plans and land suitability. These findings underscored the importance of evaluating and revising the spatial plan to enhance its alignment with land suitability.

Performance Assessment of Second-Generation SBAS Prototype in Thailand

2025-08-20T12:29:07+02:00

This study evaluates the preliminary performance of the dual-frequency multiconstellation satellite-based augmentation system (DFMC SBAS) prototype that was deployed in Thailand, focusing on key performance indicators such as positional accuracy and continuity. To this end, real data that was collected from 4, 8, and 12 ground tracking stations in Thailand was used to calculate SBAS corrections for the periods of January 1–7, April 1–7, August 1–7, and December 1–7, 2023. The accuracy of these corrections for single-point positioning was then tested using data from 20 continuously operating reference stations (CORS) in the region. The results showed that the correction data that was derived from the data from the 8 and 12 ground tracking stations significantly improved the efficiency of the single-point positioning, thus meeting the required standards for Category I (CAT-I) aviation operations. This initial assessment provides a solid foundation for the continued development of a fully operational DFMC SBAS that is tailored to Thailand’s specific requirements.

Statistical Analysis of Soil Contamination in Vicinity of Coal-Processing Plant: Implications for Ecosystem Stability

2025-07-04T14:13:16+02:00

The extensive generation of waste and intensified geological processes that result from hard coal mining and active operations within mining regions have led to increases in the pollution levels of ecosystems. Most coal-mining wastes contain significant amounts of heavy metals and are, therefore, particularly hazardous to the environment. The soils around waste heaps can be contaminated with various pollutants. This article presents the results of a study of soils that were sampled in the impact zone of the waste heap of the Chervonohradska CPP of the Chervonohrad Mining District. Using statistical methods (including variogram modeling and spatial interpolation), we analyzed the spatial distributions of heavy metals in the affected soil zones. This approach allowed for an enhanced understanding of contamination-dispersion patterns and potential risk areas. The authors collected soil samples from the depth of the biotically active humus-accumulative horizon from the lower tier of the slope of the waste heap at distances of 20 m, 40 m, and 100 m from the spoil tip. We measured the contents of the studied elements in the soil using X-ray fluorescence analysis and assessed the quality of the soil by phytotesting using the Triticum aestivum L. and Lepidium sativum L. test species. It was found that the average concentrations of certain heavy metals in multiple samples exceeded the background values for the region and affected the inhibition of the development and growth of the test objects.

Demographic Determinants as Sources of Development of Private Senior Care Homes in Poland

2025-07-21T18:29:45+02:00

This research concerned the directions of changes in the real estate market in the area of private nursing homes for the elderly. The progressive decline in Poland’s population, the decline in the fertility rate, and demographic projections for the population structure have given rise to considerations about areas of the real estate market that will have to meet the expectations of elderly real estate participants. The growing number of privately owned nursing homes represents a commercial real estate sector that can be viewed as a profitable investment venture. The study examined investment performance indicators for several private nursing homes in Poland; the results indicated relatively high levels of EBIDTA margins for each company.