International Journal of Geoinformatics

Mapping of Urban Land Surface Temperature Trends in Makassar (2000–2020) Using a Harmonic Model for Continuous Change Detection and Classification

A. Izzaty — 2026-07-25

Rapid urban expansion modifies land cover, alters vegetation dynamics, and intensifies urban surface warming. This study examines long-term vegetation change, urban expansion, and land surface temperature (LST) dynamics in Makassar City, Indonesia, from 2000 to 2020 using multi-temporal Landsat imagery. Vegetation conditions were quantified using the Normalized Difference Vegetation Index (NDVI) derived from annual cloud-free composites. Temporal vegetation trajectories were analyzed using continuous change detection and classification (CCDC) based on a harmonic regression model, while urban expansion was identified through persistent vegetation loss. LST was retrieved using a consistent, sensor-specific workflow to enable interannual comparison. The results reveal a pronounced decline in vegetation between 2010 and 2015, with the strongest reductions occurring in areas where NDVI values decreased below 0.1, indicating conversion from vegetated surfaces to impervious urban land cover. During this period, urbanized areas expanded by approximately 1,500–2,000 hectares. Concurrently, mean LST increased from 22.21 °C in 2000 to 28.16 °C in 2020, representing an increase of 5.95 °C in mean urban land surface temperature (LST), primarily driven by vegetation loss and the expansion of impervious surfaces. Spatial analysis shows that the most substantial LST increases coincide with zones of sustained vegetation loss and urban expansion. These findings demonstrate the effectiveness of long-term satellite time-series analysis for detecting gradual and abrupt urban-induced environmental changes. The proposed framework provides robust evidence for monitoring vegetation degradation and urban surface warming over extended periods, offering valuable insights for urban planning and climate adaptation strategies in rapidly developing cities.

Voxel Based Web Visualisation for 3D Cadastre in Resource Constrained Urban Environments

N. Qamilah — 2026-07-25

Rapid urban verticalization has made conventional two-dimensional cadastral systems inadequate for representing overlapping property rights, vertical restrictions, and building-volume relationships in dense urban environments. This study aims to develop and evaluate a lightweight web-based 3D cadastral visualisation framework that can support accessible land administration in resource-constrained settings. Using Sumur Bandung District, Bandung, Indonesia, as a case study, the research processed 6,762 building footprints covering 3.39 km² through a hybrid Python-JavaScript voxelisation pipeline. The proposed method converts 2D cadastral vector footprints into semantic volumetric units at a 0.5-metre spatial resolution using a server-side open-source stack based on Flask, GeoPandas, and Shapely, while client-side visualisation is implemented through Three.js and WebGL. System performance was assessed by comparing the voxel-based output with a baseline high-polygon surface mesh generated from the same vector data in QGIS 3.36 and exported as an uncompressed Wavefront OBJ file containing approximately 1.24 million triangular faces. The results show that the proposed framework reduced the data payload from 45.5 MB to 18.2 MB, equivalent to a 60% reduction, while maintaining interactive rendering at approximately 55 FPS on a low-specification device equipped with an Intel i5 12th-generation processor and Intel UHD GPU. The system achieved a Time-to-Interactive of 3.2 seconds, improving web accessibility compared with previously reported web-based 3D cadastral solutions exceeding 10 seconds of initial loading time. Geometric accuracy assessment indicated a mean boundary aliasing error of 0.21 m and a maximum error of 0.35 m, remaining within the 0.50 m positional tolerance for Class 3 cadastral data under Indonesian BPN technical guidelines. In addition, the framework integrates static rule-based height-compliance validation against RDTR and KKOP regulations. Validation using 522 manually verified buildings produced 100% precision and 91.7% recall. These findings demonstrate that voxelisation offers a practical, scalable, and open-source approach for modernizing 3D land administration in developing countries.

Morphostructural Analysis and Active Tectonics of the Northeastern Constantine Basin, Algeria: Insights from Morphometric Indices and Structural Lineaments

S. Zerdoudi — 2026-07-25

Since their formation, the Maghrebids have been influenced by major tectonic movements in geological history. These events have resulted in morpho-structural arrangements that are still poorly understood, particularly in the northeastern of the Constantine Basin. This study aims to fill this gap with an integrated morpho-structural analysis, based on: (i) detailed field mapping, (ii) detailed lineament analysis based on aerial and satellite imagery, and (iii) the calculation of quantitative geomorphological indices grouped in the IRAT (Indice de Tectonique Active Relative). Seven morphometric indices were extracted from a 10 m resolution DTM and applied to the Kef Hahouner (KHB) and Guendoula Messigla (GMB) watersheds. These are the hypsometric integral (H_i), mountain front sinuosity (S_mf), valley floor width/height ratio (V_f), asymmetry factor (A_f), basin shape index (B_s), transverse topographic symmetry factor (T) and stream length gradient (S_L). The ENE-WSW trending Kef Hahouner-Aïn Berda (KHAB) sinister strike-slip fault crosses both basins. It constitutes the main structure, identified through more than 500 lineaments, accompanied by more recent faults oriented N-S and NW-SE. The average IRAT S/N score is 2.0, classifying the zone as class 3 (moderate activity). However, several indicators suggest that this moderate classification is underestimated: S_L values exceed 600, V_fratios are below 0.5, the S_mf of the KHB mountain front is 1.11, and A_f values indicate asymmetrical basin tilting. In addition, the low B_s values (1.43-1.58) seem to testify to tectonic control. This suggests that the IRAT probably underestimates the actual level of activity. By combining morphometric data with GIS and field measurements of offset geomorphic markers, the area was reclassified as class 2 (high activity). Our results show that an active fault corridor strongly modifies drainage, relief generation and sediment dynamics. They underline the importance of combining geomorphological indicators with structural analysis to improve neotectonic assessments in structurally complex regions.

Comparison of Kernel Support Vector Machine Method Using Aerial Photographs to Identify Slum Settlements in Squatter Area of South Magelang

T.A. Putri — 2026-07-25

Detection of slums in urban informal settlements plays an important role in making the right decisions and ensuring equitable urban growth in highly developing cities. This paper presents a comparative study of four types of SVM kernel functions, namely, Linear, RBF, Polynomial, and Sigmoid for classifying slum areas based on texture feature using Gray Level Co-occurrence Matrix (GLCM) from very high resolution aerial images. The study is performed with a sample squatter area in South Magelang, Magelang City, Indonesia, and compares the performance of three essential GLCM features (mean, variance, and dissimilarity) on different window sizes. As can be seen, there is a considerable impact of kernel type selection in classification performance. The best performance in terms of overall accuracy was obtained by using RBF kernel with a relatively low accuracy of 62.26%, then comes Polynomial (57.78%), Linear (55.33%), and Sigmoid (49.72%). The above hierarchy of performance shows the fundamental non-linearity that exists between the morphology of slums and the textural representation of slums in the RGB image. Majority voting aggregation of the output of texture feature extraction for each kernel resulted in more coherent settlement maps. In summary, this study offers empirical evidence to guide the optimal use of SVM-based texture features in slum mapping, providing a screening procedure for the automated analysis of urban morphologies via remote sensing.

A Scale-Aware Web GIS Architecture for Village-Level Exploratory Spatial Interaction: Design, Implementation and Scenario Evaluation

S. Afnarius — 2026-07-25

Following the COVID-19 pandemic, independent travel has become a tourism trend, and the use of digital tools to support exploratory spatial interaction in rural tourism has increased. While Web-Based Geographic Information Systems (Web GIS) have been widely applied in tourism research, most of these applications focus on visualization or promotion, with few examining the application of village-based exploratory spatial interaction tools. This research introduces DTExplorer, a scale-aware Web GIS application powered by curated spatial data. DTExplorer enables category- and radius-based exploration of curated Points of Interest (POIs), reflecting how independent travelers make decisions in rural destinations, where proximity, thematic relevance, and clustering of visits are the main spatial choice factors. The system was developed using a research and development (R&D) approach and implemented as a Web GIS application using Node.js, JavaScript, MySQL, Google Maps and an interactive mapping interface. DTExplorer has been tested and passed functional and scenario testing. These findings indicate that at the village level, the main focus of effective exploratory spatial interaction has not been based on analytical complexity and optimization-based models. Rather, the efficacy of exploratory interaction arises through the spatial scale congruence, user decision reasoning, and controlled POI data governance. Combining curated POI data with simple spatial operations such as proximity and category-based filtering, DTExplorer demonstrates the potential of simple GIS functions to be applied as meaningful exploratory spatial interaction in rural tourism situations. This study contributes to applied geoinformatics by redefining exploratory spatial interaction in rural tourism as a scale-aware, design-oriented process rather than an analytical one. This work advances theoretical knowledge on adapting Web GIS for small-scale destinations and demonstrates the strategic importance of curated spatial data in community-based tourism decision-making.

Evaluation of CHM Morphological Processing and U-Net Deep Learning for Citrus Tree Canopy Delineation from UAV Imagery

M.J. Daiaeddine — 2026-07-25

Accurate delineation of individual tree crowns from UAV imagery is essential for precision citrus management but remains difficult in orchards with overlapping crowns and inter-row weeds. This study compared two contrasting approaches for citrus crown delineation: a canopy height model (CHM)-based morphological pipeline and a U-Net deep-learning model applied to RGB imagery. Both used an identical marker-controlled Watershed step for individual crown separation, so that the comparison reflected the segmentation stage alone. RGB imagery was acquired with a DJI Mavic 3M UAV over two structurally contrasting citrus orchards in the Beni Mellal-Khenifra region of Morocco, planted with the Maroc Late and Sidi Aissa varieties, and performance was assessed at the pixel and object levels. In the structurally simple Maroc Late orchard the two methods were effectively equivalent, both achieving high object-level agreement (detection score, the Jaccard index of the detected and reference crown sets, about 94%) and recovering crown area almost perfectly. The methods diverged sharply in the dense, weed-affected Sidi Aissa orchard: the U-Net sustained robust performance (detection score 93.6%; crown-area R² = 0.93), whereas the CHM-based pipeline, although it still detected roughly two-thirds of the reference crowns, was heavily penalised by false positives from weeds and merged crowns, lowering its detection score to 46.9% (crown-area R² = 0.64). Because the separation step was held constant, this divergence is attributable to the quality of the upstream mask rather than to the delineation algorithm: learned spectral and textural features proved decisive where the height threshold lost discriminative power. The results indicate that method choice should be matched to orchard structure, a lightweight height-threshold approach being sufficient in clean orchards and an RGB-trained model preferable under structural complexity. These findings, obtained from two citrus varieties at a single site and date, require multi-site validation before broader generalization.

Comparative Accuracy Assessment of Nadir and Hybrid RTK-UAV Photogrammetry for Surface Detail Extraction

M.N. Alkan — 2026-07-25

This study evaluates the metric reliability of nadir and hybrid RTK-UAV photogrammetric models for surface detail extraction on a two-story building without using Ground Control Points (GCPs). Two photogrammetric models were generated: a standard model based only on nadir images and a hybrid model combining nadir, oblique, and manual façade-oriented images. Both models were assessed using terrestrial reference data obtained from GNSS and reflector less total station measurements. The evaluation was performed at three levels: point-based, length-based, and surface-based comparisons. The point-based results indicate that the nadir model allowed 21 of the 35 reference points to be reconstructed, whereas the hybrid model allowed the 3D positions of all. The hybrid model also substantially improved vertical and overall 3D accuracy, reducing the ΔZ MAE from 0.355 m to 0.029 m and the 3D RMSE from 0.454 m to 0.046 m. In the length-based analysis, the nadir model was included where both endpoints of a selected linear element could be clearly identified. The nadir model allowed only 4 of the 14 selected length pairs to be measured, whereas the hybrid model allowed all 14 length pairs to be evaluated. On the other hand, the hybrid model had close agreement with the reference lengths, with a MAE of 0.012 m and RMSE of 0.016 m. Also, there is no statistically significant difference observed between reference and model lengths. In the surface-based comparison, the nadir model could represent only 4 of 7 reference surfaces, a limited number of reference surfaces, whereas the hybrid model enabled all selected surfaces to be evaluated, including upper-floor and under-eave regions. Although the nadir model produced lower average errors on some reconstructed surfaces, its limited surface coverage reduced its practical applicability. Overall, the findings demonstrate that hybrid RTK-UAV image acquisition provides a more robust and reliable solution than nadir-only acquisition for building surface documentation, particularly in façade areas with restricted visibility.

Detecting Reactivation Mechanisms of an Active Landslide Using LiDAR-Derived Stem-Lean Metrics and Electrical Resistivity Tomography

H.R. Julias — 2026-07-25

Identification of reactivation zones in thick volcanic soils is critical for understanding landslide mechanisms. Volcanic soils are clay-rich, making slopes highly susceptible to water saturation and renewed movement. However, scientific understanding of the relationship between surface indicators and subsurface structure remains limited, particularly in thick and well-developed volcanic terrains. Few studies have combined vegetation-based deformation with subsurface resistivity structure within a single framework. This study integrates unmanned aerial vehicle (UAV)-based LiDAR and electrical resistivity tomography (ERT) to characterize an active landslide in Kalisari, Central Java, Indonesia. ERT serves as a non-invasive method to map subsurface moisture and weak zones. UAV-LiDAR data were processed to generate a detailed terrain representation of the landslide. The UAV-LiDAR point cloud was used to extract surface morphology and vegetation-based deformation metrics, including tree stem-lean angle and azimuth. ERT surveys were conducted along multiple profiles across the landslide to delineate low-resistivity zones interpreted as saturated clay-rich materials. Results indicate that reactivation areas are clearly expressed by surface morphology and coherent stem-lean patterns. Although the overall morphology is rotational, recent movement is dominated by coherent lateral displacement. Stem-lean orientations align with the inferred movement direction (azimuth 45–100°). LiDAR-derived stem-lean estimates show good agreement with field measurements (R² = 0.87 for lean angle and ρ² = 0.85 for azimuth). ERT results also show that reactivation areas correspond to low-resistivity zones (3–30 Ω·m) that spatially coincide with surface cracks, flow-accumulation features, and stem-lean anomalies. This indicates that water infiltration through surface discontinuities recharges the weak saturated clay layer and makes the slope more prone to instability. Overall, integrating UAV-LiDAR and ERT provides a strong basis for linking surface deformation with subsurface structure and identifying landslide reactivation zones in volcanic landscapes.

Spatiotemporal Characteristics of Atmospheric NO2 Concentration in Southeast Asia using Satellite Observations

P. Piromthong — 2026-07-25

Southeast Asia has long experienced severe air pollution, posing significant risks to public health. Contrary to global trends of declining emissions, the region’s rapid industrialization may exacerbate air quality problems. This study investigates the spatiotemporal characteristics of atmospheric nitrogen dioxide (NO₂) concentrations across Southeast Asia using satellite-based observations. Two analytical approaches are employed: an analysis of tropospheric NO₂ concentration fields and a flux-divergence-based assessment of NO₂ emissions. First, regional and local patterns of NO₂ concentrations are examined using tropospheric column density data from the Ozone Monitoring Instrument (OMI) for the period 2005–2022. The results indicate that elevated NO₂ concentrations are primarily concentrated in major urban centers, likely associated with vehicular emissions and industrial activities. Notably, enhanced NO₂ levels are also observed in several forested regions. These anomalies are hypothesized to be associated with biomass burning, a relationship further supported through integration with MODIS burned area products. Detailed analyses are then conducted for 12 hotspot regions using time series decomposition to isolate long-term trends, seasonal variability, and residual components. To account for the potential influence of the COVID‑19 pandemic, the analysis period for each hotspot is divided into pre‑ and post‑pandemic phases, revealing distinct concentration trends for individual regions. Second, NO₂ emission patterns are investigated using flux divergence calculations derived from TROPOspheric Monitoring Instrument (TROPOMI) observations in combination with wind fields from the ECMWF ERA5 reanalysis. Regional-scale emission maps are first produced for Southeast Asia, followed by focused analyses for three selected areas: the Bangkok Metropolitan Region, the Northern Vietnam Industrial Corridor, and the Singapore–Kuala Lumpur Region. Independent auxiliary datasets are used to reference the accuracy of the inferred emission patterns. The results demonstrate that the flux divergence approach effectively identifies major emission sources, especially in complex areas like Bangkok's urban region.

Geospatial Deep Learning for Pomelo Tree Detection in Mixed Orchard Systems: A UAV-Based Assessment of YOLO Models

M. Worachairungreung — 2026-07-25

This study addresses the global transition in agricultural area monitoring and assessment from traditional field survey methods to the widespread use of geospatial technology and artificial intelligence under the concepts of precision agriculture and digital agriculture. Accordingly, this study aims to apply unmanned aerial vehicle (UAV) imagery in combination with deep learning techniques to classify pomelo trees in mixed orchard systems, while comparatively analyzing the performance of YOLO models in order to identify the most suitable model for complex terrain. The findings are expected to support the development of a precise farm-plot-level spatial database, which is important for the management of geographical indications (GI) pomelo, yield estimation, and effective regional agricultural planning. The results indicate that YOLOv5 is more suitable for tasks requiring high accuracy in tree counting and spatial density analysis, whereas YOLOv8 demonstrates architectural stability and potential for further development in the future. Overall, the findings will facilitate the selection of an appropriate model for developing an accurate farm-plot-level spatial database, which is important for the management of various orchard systems, as well as for yield estimation and effective regional agricultural planning

GIS-Based Spatial Optimization Framework for Defense Industrial Planning and Logistics Accessibility in Indonesia

I. Nyoman Suadyana — 2026-07-25

Global geopolitical developments, particularly in the Middle East, indicate a significant transformation in modern warfare, which is no longer dominated by conventional weaponry but by the integration of artificial intelligence, information warfare, and asymmetric weapon systems such as naval and aerial mines. In this context, the use of Geographic Information Systems (GIS) has become increasingly important to support strategic analysis and defense industrial policy formulation at both national and global levels. This study aims to analyze the impact of global geopolitical dynamics on the urgency of defense industrial independence in Indonesia using a geospatial approach, and to identify patterns of industrial distribution and strategic vulnerabilities based on GIS. This research employs a qualitative method with a descriptive case study design of the defense industry in Indonesia. Data analysis follows the Creswell model, integrated with geospatial analysis using Geographic Information Systems (GIS). Qualitative data were processed using NVivo to identify key policy themes, while spatial analysis was applied to map the distribution of defense industries, logistics networks, and regional disparities through location-allocation and spatial clustering approaches. Indonesia’s defense industry remains dependent on imported spare parts and is concentrated on Java Island, resulting in spatial inequality and logistical vulnerabilities. GIS analysis reveals a mismatch between industrial locations and strategic operational areas, particularly in eastern Indonesia. This condition weakens national resilience due to limited logistics independence, high supply chain disruption risks, uneven industrial development, delayed defense responses, and limited regional economic impact. Indonesia’s defense industrial independence remains insufficient in addressing global geopolitical dynamics and technology-driven warfare. Integrating geoinformatics into defense industrial policy is essential to enhance distribution efficiency, reduce regional disparities, and strengthen national resilience. Policymakers should promote geospatial-based strategies to achieve industrial independence, improve deterrence capability, and reinforce Indonesia’s position in the global geopolitical landscape.