International Journal of Geoinformatics

GIS Application in Urban Road Network Analysis: The Case of Panchkula City, Haryana, India

A. Taran — 2026-04-30

Urban road networks are crucial for the efficient movement of people and goods. A region's ability to develop has always been directly correlated with the calibre of its transportation infrastructure. Effective and unobstructed road networks are essential in today's world to reduce problems like traffic jams, delays, pollution, higher vehicle operating expenses, and collisions. Panchkula, a rapidly growing city in Haryana, India, faces challenges related to traffic congestion and road network efficiency. Using Geographic Information Systems (GIS), this study examines Panchkula City, Haryana, India's urban road network. The objective of this research is to assess the current condition of Panchkula's road densities, such as major, minor, and lane road density, and short path analysis for the emergency services between the two city points. GIS technique is used to find the shortest path between important city places, providing workable answers to traffic related issues. By employing various connectivity and density indices, the current state of the road network is analysed, and proposals for improvements for better traffic management and urban planning are made. The results show that the major roads density varies from 0.150537 to 3.201378, the minor road density varies from 0.013290 to 1.565590, and the lane density varies from 0.56540 to 3.068603.

Time-Series Deformation Monitoring over the Kalush-Golynsk Potassium Salt Deposit (Ukraine), Using InSAR Method

D. Kukhtar — 2026-04-30

Improper liquidation of underground mine cavities can lead to the development of hazardous geodynamic processes, including progressive surface subsidence and ground instability in mining regions. This study demonstrates a workflow for satellite radar monitoring of deformation processes in mining regions using Sentinel-1 SAR data. Time-series analysis was performed using the NSBAS (New Small Baseline Subset) method implemented in the MintPy software package. Interferogram generation was automated through the HyP3 cloud processing platform of the Alaska Satellite Facility, while subsequent analysis was conducted in the Jupyter Notebook environment provided by the OpenSARlab platform. A total of 295 interferometric pairs covering the period from April 2021, to March 2024 were analyzed. Tropospheric delay corrections were introduced using ERA5 atmospheric reanalysis data distributed via the Climate Data Store service. The resulting mean velocity map reveals several zones of intensive subsidence within the Novo-Holyn mining field, including the area of a large sinkhole currently flooded with water, the roof of a reclaimed tailings storage facility, and parts of the nearby residential area. In some locations, deformation rates approach 50 cm/year, indicating rapid surface destabilization related to underground mining processes. To validate the InSAR results, four annual campaigns of high-precision second-order levelling were conducted. A comparison of deformation rates along four levelling profiles shows good agreement between satellite radar monitoring and ground-based geodetic measurements. The presented approach demonstrates that time-series InSAR analysis with short temporal baselines can provide reliable deformation estimates even under conditions of rapid subsidence and strong temporal decorrelation typical of mining areas. The proposed processing workflow can therefore be applied to other regions affected by underground mining or similar geodynamic processes, expanding the practical capabilities of satellite radar monitoring for early detection and localization of hazardous surface deformation.

Evaluation and Local Calibration of Sentinel-2 Chlorophyll-a Algorithms in Kendal Coastal Waters, Indonesia

L. Maslukah — 2026-04-30

Monitoring chlorophyll-a (Chl-a) concentrations in coastal waters is crucial due to its role as a biogeochemical indicator sensitive to environmental changes. Remote sensing techniques have been widely utilized for Chl-a estimation; however, the precision and relevance of algorithms developed in other regions require comprehensive evaluation, validation, and calibration against in situ Chl-a data. This study evaluated a Sentinel-2A Chl-a algorithm using 10m blue, green, red, and near-infrared (NIR) resolution bands (hereafter, MR4B) and an algorithm based on the green-red (GR) ratio band from another region. The performance of both was compared against an algorithm generated through in situ Chl-a calibration. Calibration was performed on the green-blue, green-red ratio bands, and a single band, using in situ Chl-a data collected on April 24, 2025, coinciding with the S2A satellite's passing time. The results showed that the performance of MR4B and GR was outperformed by the algorithms generated through the calibration process, where the SB algorithm showed superior performance, followed by the green-blue ratio and the green-red ratio, with root mean square error (RMSE) of 0.74 µg/L, 0.89 µg/L, and 0.93 µg/L, respectively. This study showed that the single band algorithm, demonstrated in the use of the green band (SB) provides a more practical and robust approach for Chl-a monitoring in this coastal system, with its simpler structure compared to other algorithms. However, further research is needed to examine the algorithm in the different season.

Advancing Topographic Mapping with UAVs: Technologies, Challenges, and Prospects

A.L. Tuan — 2026-04-30

UAV technology is currently routinely utilized to generate topographic maps. This paper summarizes recent publications on the use of unmanned aerial vehicle (UAV) technology for terrain mapping based on the results of research published from 2010 to January 2025. The quantitative synthesis of the literature reveals that photogrammetry remains the most prevalent method, accounting for 64% of the studies, followed by LiDAR at 26%, while other emerging technologies comprise 9.8% of the research landscape. The obtained results showed that the UAV method with photogrammetry, lidar, and GNSS technologies are useful techniques for producing topographical maps. Also included in this study are some of the technology's drawbacks and advantages. Finally, the prospects of development of methods such as RTK/PPK integration for centimeter-level accuracy, AI-driven autonomous flight planning, multi-sensor fusion (combining thermal, multispectral, and LiDAR data), swarm UAVs, the integration of UAVs with IoT, smart sensors were also presented. This study provides a technical reference for advancing knowledge and comprehension of UAV applications in creating the topographic maps.

Event-Based Analysis of Rainfall Variability from Integrated GNSS-Derived Precipitable Water Vapor and Weather Radar Observations in Bangkok

C. Trakolkul — 2026-04-30

Short-term rainfall monitoring in tropical megacities remains challenging due to highly variable atmospheric conditions. This study evaluates the relationship between GNSS-derived precipitable water vapor (PWV) and rainfall occurrence, intensity, and duration in the Bangkok Metropolitan Region, Thailand. High-resolution GNSS data from five Continuously Operating Reference Stations (CORS) collected during 2019–2022 were processed using precise point positioning to estimate PWV from tropospheric delays. These PWV estimates were integrated with rainfall, relative humidity, and weather radar reflectivity data. Statistical analysis during the 2019–2022 rainy seasons indicates a weak linear relationship between PWV and 24-hour accumulated rainfall (Pearson r = 0.15, p < 0.001), but a stronger monotonic association (Spearman ρ = 0.39, p < 0.001), suggesting that rainfall response to atmospheric moisture is not strictly linear. Lag-correlation analysis shows that PWV typically precedes rainfall by approximately 8–9 hours. Detailed lag-based analysis was conducted at a representative GNSS station (DPT9) with minimal GNSS–gauge separation (≈4 km), reducing spatial representativeness uncertainty under convective rainfall conditions. Radar observations show spatial and temporal consistency between elevated PWV and organized precipitation systems during heavy and prolonged rainfall events. The integration of GNSS-derived PWV with meteorological and radar observations provides an observational basis for event-based rainfall assessment in urban environments.

Efficient Integration of Water Demand using Fuzzy Analytical Hierarchical Process Model with Geographic Information System in Vientiane Capital, Lao PDR

M. Singharaj — 2026-04-30

Water demand management remains a critical challenge in rapidly urbanizing cities, particularly in Southeast Asia where infrastructure expansion often lags behind demographic growth. This study develops an integrated framework that combines the Fuzzy Analytic Hierarchy Process (FAHP) with Geographic Information System (GIS) to analyze domestic water demand in Vientiane Capital, Lao PDR. Five criteria consist of population density, household size, access to piped water, distance from water sources, and elevation were evaluated through expert-based pairwise comparisons using FAHP, which effectively addresses uncertainty and subjectivity in decision-making. The derived weights were incorporated into spatial datasets and analyzed using the Weighted Overlay technique in ArcGIS Pro to generate a Water Demand Suitability Map. Quantitative results classified water demand into five levels: very high (4.40%), high (60.11%), medium (7.34%), low (8.79%), and very low (19.36%). High and very high demand zones were concentrated in the urban core districts Chanthabouly, Xaisettha, Sikhotabong, Sisattanak, and Hatxayfong. These areas host most water treatment plants, piped distribution systems, and economic activities. In contrast, medium- to very low-demand areas were found in peripheral districts such as Sangthong, Naxaythong, Xaithani, and Pakngum, where approximately 20% of villages lack piped water access and seasonal droughts exacerbate water scarcity. Access to piped water (weight = 0.503) and population density (weight = 0.231) emerged as the most influential determinants, underscoring the importance of infrastructure and demographic pressures in shaping demand. The novelty of this study lies in extending FAHP-GIS applications from hazard and drought assessments to domestic water demand analysis, thereby providing a replicable tool for prioritizing infrastructure investment and resource allocation. Limitations include reliance on expert judgment for weighting and the exclusion of climate change projections, which should be addressed in future research. Overall, the integrated FAHP-GIS approach offers a practical and innovative decision-support framework for policymakers and water authorities to design resilient strategies for sustainable water resource management under conditions of rapid urbanization and climate variability.

Impacts of Climate Change and Land-Use Change on Hydrological Processes and Sediment Load in the Upper Cau River Basin, Northern Vietnam

N. Ngoc Anh — 2026-04-30

Climate change and land-use change strongly influence hydrological processes and soil erosion in tropical mountainous watersheds. This study uses the SWAT model to assess their individual and combined impacts on streamflow, sediment, and soil erosion in the Upper Cau River Basin, northern Vietnam. The model was calibrated and validated using observed hydro-meteorological data for 1997–2020 and applied to scenario-based projections of future climate (2021–2050) and land-use planning to 2050. The SWAT model demonstrated high predictive accuracy for both streamflow and sediment (NSE > 0.76, R² > 0.77). Results indicate that, relative to the baseline, the combined climate and land-use change scenario produces the largest impacts, with mean streamflow increasing by 44.95%, sediment discharge at the gauge station rising by 53.3%, and average basin-wide soil erosion reaching 12.57 t/ha/year more than a 380% increase. By comparison, climate change alone increases mean streamflow and soil erosion by 44.8% and 73.7%, respectively, while land-use change alone causes only minor changes in streamflow (+0.82%) but substantially increases soil erosion (+265%). These findings demonstrate a pronounced amplification of hydrological extremes and erosion risk when both drivers act together. Beyond scenario comparison, this study contributes a spatially explicit erosion hotspot attribution framework that links scenario forcing to hydrological response unit and sub-basin prioritisation of erosion risk. Spatial analysis reveals a basin-wide shift from predominantly low erosion under baseline conditions to widespread moderate and high erosion under the combined scenario. While the results highlight the dominant and interacting roles of climate and land-use change, they reflect SSP2-4.5 projections from a single climate model (UK Earth System Model, UKESM1-0-LL), and uncertainty across multiple climate models was not assessed. The findings provide a robust scientific basis for targeted watershed management and erosion-control planning in northern Vietnam’s mountainous regions.

A Review of Research in Estimation and Dispersion of Particulate Matter Using Remote Sensing Data in Southeast Asia

N.A.F. Kamarul Zaman — 2026-04-30

Air pollution is a major challenge for the global population nowadays, while the most deleterious effects in terms of human health (cardiovascular diseases, asthma) are detected in the densely populated and highly polluted regions of south and east Asia. This study provides a comprehensive review of scientific articles dealing with particulate matter (PM) estimations from space in the climate sensitive and polluted southeast Asian (SEA) region. PM monitoring from space over this region is a challenging task due to high aerosol burden, mostly attributed to biomass-burning smoke from extensive forest, agricultural and peat fires, outside cooking and increasing urban/industrial emissions due to growing population, urbanization and industrialization. Several satellite sensors onboard polar-orbiting and geostationary satellites are reviewed, as well as influencing factors (meteorological variables, gaseous pollutants, topographic characteristics), algorithms and statistical techniques that are synergistically implemented for assessment of surface pollution (i.e. PM concentrations) from space-borne remote-sensing applications. Furthermore, current review highlights the potential of implementing Machine Learning (ML) and Deep Learning (DL) models and advanced computational techniques for developing air pollution prediction in hotspot regions of Asia (mainly), opening a new era in PM simulations and providing support to policymakers and stakeholders to design new effective pollution control strategies for attaining sustainable development goals under the challenge of climate change. International collaboration in the fields of remote sensing applications, maintenance of ground-based pollution networks and development of new ML models between researchers from various countries is especially important for future perspectives and innovations in PM estimations from space.

Evaluating Quantum Machine Learning for GIS-Based Land Suitability Analysis: A Case Study of Reforestation

N. More — 2026-04-30

Quantum Machine Learning (QML) has emerged as an active area of research, yet its practical readiness for real-world geospatial applications remains uncertain due to constraints related to data availability, preprocessing requirements, and current quantum hardware limitations. This study evaluates the feasibility and behaviour of quantum kernel-based learning models within a realistic GIS workflow, using land suitability analysis for reforestation in the Mumbai metropolitan region as a representative case study although demonstrated using the Mumbai metropolitan region as a case study, the proposed GIS-driven preprocessing pipeline and quantum kernel evaluation framework are transferable to other regions with comparable remote-sensing data availability and environmental indicators. A geospatial dataset is constructed from satellite-derived environmental variables, including Normalized Difference Vegetation Index (NDVI), annual rainfall, terrain slope, soil pH, and soil organic carbon, with suitability labels assigned using ecologically motivated rule-based thresholds that serve as heuristic ground truth. The dataset is processed through a standardized pipeline involving feature scaling, Principal Component Analysis (PCA), stratified train–test splitting, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Quantum Support Vector Machines (QSVMs) employing Pauli and ZZ feature maps are implemented using Qiskit and evaluated under noiseless simulation conditions, with performance compared against a classical Support Vector Machine baseline using identical preprocessing and evaluation metrics. While classical models achieve superior computational efficiency and higher predictive performance, QSVMs exhibit non-trivial classification behaviour on small, structured datasets when appropriate preprocessing and feature-to-qubit alignment are applied. Rather than claiming quantum advantage, this work provides an empirical assessment of the current capabilities and limitations of QML in applied geospatial analysis, positioning quantum kernel methods as complementary tools for exploratory environmental modelling under present technological constraints.

Land Value Changes Analysis in Sub-Urban Area of Surabaya-Gresik using Spatial Modelling of Anselin Local Moran’s I

F. Bioresita — 2026-04-30

Land value changes in suburban areas are a complex phenomenon influenced by land use dynamics, population growth, and infrastructure development. In metropolitan regions like Surabaya, urbanization pressure has led to land conversion, especially from agricultural land to residential and industrial zones. This suburbanization process triggers uneven changes in land value, depending on external factors such as accessibility and regional connectivity. This study aims to analyze the relationship between land use change from non-urban to urban and the changes in land value in the Surabaya–Gresik area in 2019 and 2023. The analysis utilizes GIS-based and spatial modelling with Sentinel-1 imagery, Land Value Zone (ZNT) maps, and the Anselin Local Moran’s I method to identify spatial clustering patterns of land value changes. The results indicate that most areas undergoing urbanization also experienced an increase in land value, classified as High-High (HH) clusters, particularly in the districts of Sambikerep, Lakarsantri, and Menganti. However, exceptions were found in areas such as the southwestern parts of Lakarsantri and Pakal, which, despite urbanization, remained in the Low-Low (LL) cluster, reflecting persistently low land values. These findings highlight that urbanization does not automatically raise land value without supporting infrastructure and adequate accessibility. This spatial-temporal approach provides crucial insights for regional planning that is responsive to the dynamic growth of suburban areas.

Relative Importance of Environmental and Spatial Predictors for Slipper Orchid (Paphiopedilum spp.) Distribution in Thailand Using Random Forest

P. Pholgerddee — 2026-04-30

Slipper orchids (Paphiopedilum spp.) represent some of Southeast Asia's most ecologically specialized and conservation-sensitive orchid taxa. Their distribution in Thailand is shaped by intricate environmental gradients, encompassing climate, topography, vegetation structure, and geographic location. Nonetheless, comprehensive national assessments of habitat appropriateness for Paphiopedilum species are scarce. This research utilized a species distribution modelling methodology to ascertain critical environmental factors and forecast appropriate habitats for Paphiopedilum species throughout Thailand. Seventy occurrence reports were obtained from biodiversity databases, field surveys, and herbarium collections. A Random Forest modelling framework was employed utilizing twelve environmental predictors that encompass climatic, non-climatic, and spatial factors. A balanced presence-absence dataset was created, and model performance was assessed using various statistical criteria, including the Brier score. The model attained a Brier score of 0.05, signifying elevated predictive accuracy. Climatic factors accounted for 38.30% of overall variable importance, followed by non-climatic variables at 37.40% and spatial predictors at 24.30%. The model exhibited robust predictive efficacy, with an AUC of 0.94, sensitivity of 0.80, specificity of 0.76, and an overall classification accuracy of 0.78. Principal predictors encompassed latitude, longitude, elevation, vegetation structure (NDVI), and temperature-associated factors. Optimal habitats were predominantly situated in the mountainous areas of northern and western Thailand, with supplementary fragmented patches observed in certain locations of southern Thailand. The results underscore the significant influence of topographic gradients, temperature, and vegetation structure on the ecological niche of Paphiopedilum species. These findings establish a scientific foundation for pinpointing priority conservation zones and facilitating long-term management strategies for slipper orchid ecosystems in Thailand.