Urban trees are a critical component of green infrastructure in tropical cities, yet city-scale evidence on tree health in Indonesia remains limited. This study assessed urban tree health in Medan City using the Forest Health Monitoring (FHM) protocol, Tree Level Index (TLI), GIS-based spatial analysis, and Normalized Difference Vegetation Index (NDVI) validation to support Eco Forest City planning. A total of 1184 trees, representing a 30% sample from 3947 inventoried trees across six sub-districts, were evaluated based on damage location, type, and severity. Average Nearest Neighbor (ANN) and Kernel Density Estimation (KDE) were applied to examine spatial clustering of health classes, while Sentinel-2 NDVI values were extracted as an indicator of vegetation greenness. Overall, 65.79% of trees were classified as healthy, 30.66% as lightly damaged, 3.04% as moderately damaged, and 0.51% as severely damaged. Tree health differed significantly among sub-districts (Kruskal-Wallis chi(2) = 82.31, p < 0.001), with Medan Tuntungan showing the best condition, whereas Medan Marelan and Medan Amplas showed the poorest profiles. ANN and KDE results indicated that tree health classes were spatially clustered, supporting geographically targeted management. NDVI values differed significantly among health classes (Kruskal-Wallis H = 49.144, p < 0.001), although the weak Spearman correlation suggests that NDVI is more appropriate as supplementary validation than as a substitute for field assessment. These findings support risk-based tree management through routine FHM monitoring, priority inspection in vulnerable sub-districts, spatially explicit maintenance zoning, and gradual species diversification to strengthen Medan's Eco Forest City planning.
Against the backdrop of rapid global urbanization, revealing the indirect ecological effects triggered by urban environmental restructuring and human management interventions carries early-warning significance for preventing cliff-like vegetation degradation driven by unchecked urban expansion. Existing studies predominantly rely on linear assumptions and neglect the temporal lags inherent in ecological responses, making it difficult to capture the long-run and short-run dynamics as well as potential nonlinear thresholds of indirect impacts. Using Nanjing as the study region, this research constructs a spatiotemporal panel dataset at 1 km & times; 1 km resolution based on multi-source remote sensing data from 2001-2018. Building upon the quantification of urbanization's indirect effects on vegetation, we introduce a panel CS-ARDL model (Cross-Sectionally Augmented Autoregressive Distributed Lag model) to decompose short-run dynamic shocks from long-run equilibrium effects. Results reveal that urban construction land continues to expand along the Yangtze River axis, forming a "core-corridor-cluster" spatial pattern. Concurrently, vegetation indirect effects display pronounced north-south spatial differentiation: the Yangtze River's north bank (exemplified by Luhe District) exhibits clustering of negative indirect effects, whereas the core urban area south of the Yangtze generally exhibits positive responses, accompanied by a synchronized evolution from "negative-weakly negative-shifting toward positive" across the study period. ARDL (Augmented Autoregressive Distributed Lag model) results further demonstrate that the short-run scale exhibits stronger spatial heterogeneity and volatility. At the long-run scale, urbanization's indirect effects on vegetation are predominantly positive, displaying a declining gradient of "core urban areas > suburban areas > rural areas"; however, significant clusters of negative long-run effects persist in both core urban zones and far-suburban expansion edges.
Soil degradation in Mediterranean agricultural systems is strongly conditioned by topography, water redistribution and solar exposure, factors that can be effectively studied using very high-resolution remote sensing. This study evaluates the potential of Unmanned Aerial Vehicle (UAV)-derived geomorphometry combined with machine learning techniques to analyse the spatial variability of the Normalized Difference Vegetation Index (NDVI) as a surface spectral response under post-harvest conditions in a Mediterranean cereal field affected by soil degradation and gully erosion, located near Casabermeja (M & aacute;laga, southern Spain). High-resolution RGB and multispectral UAV data were used to generate a Digital Terrain Model (DTM), multi-scale local relief metrics, hydrological indices and curvature derivatives, together with NDVI maps acquired after harvest under dry and compacted soil conditions. A Random Forest regression model was applied using 3000 sampling points to link geomorphometric variables with NDVI spatial patterns. Although predictive performance was moderate (R2 = 0.31; RMSE = 0.04), variable-importance analysis identified the main terrain-related factors associated with spatial variability in the spectral signal, highlighting slope, solar exposure and erosion-and flow-convergence-related indices. The results demonstrate the usefulness of UAV-based geomorphometry and machine learning as diagnostic tools for analysing terrain-controlled surface spectral patterns and identifying areas potentially affected by soil degradation processes in Mediterranean agroecosystems.
Water scarcity is a significant challenge in arid and semi-arid countries, underscoring the importance of thoroughly studying groundwater resources. Egypt, especially in the Darb El-Arbaein region of the southern Western Desert, faces various water challenges and relies primarily on groundwater from the Nubian Sandstone aquifer. Proper management of this groundwater is essential for addressing these challenges. The study examines the spatial and temporal variations in the hydrogeochemistry of the Nubian sandstone aquifer. Data collected from the aquifer's monitoring network include key hydrogeochemical parameters, such as total dissolved solid (TDS) and piezometric heads, over different periods. Hydrogeochemical and hydrogeological maps for 2000 and 2012 were generated to identify the main lithogenic and anthropogenic sources. The data shows notable fluctuations over space and time. These maps highlight the presence of both lithogenic and anthropogenic influences. A significant finding is the sharp decline in piezometric levels (21-50 m) from 2000 to 2012, alongside increased TDS levels. This information is crucial for developing effective aquifer management and protection strategies. Aquifer deterministic modeling can pinpoint areas with the highest and lowest potential, aiding decisions on where to invest in additional wells. Remote sensing also provides valuable data about geology and irrigated regions.
This study aimed to evaluate the potential of Sentinel-3 as an alternative to Moderate Resolution Imaging Spectroradiometer (MODIS) for generating high spatiotemporal resolution land surface temperature (LST) data. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the machine-learning-based Extreme Gradient Boosting (XGBoost) algorithm were independently assessed for fusing MODIS-Landsat and Sentinel-3-Landsat data. This comparison enabled the evaluation of each model's capability to reconstruct spatiotemporal LST variations and assess the performance of the two sensors in the fusion process. The results showed that XGBoost outperformed ESTARFM in capturing complex and heterogeneous LST patterns, particularly under strong diurnal fluctuations and phenological differences. The mean Root Mean Square Error (RMSE) values for MODIS were 1.87 Kelvin (K) and 2.62 K using XGBoost and ESTARFM, respectively, while for Sentinel-3, they were 1.73 and 2.52 K, confirming XGBoost's superior accuracy for both sensors. Sentinel-3, owing to its higher spatial resolution, better radiometric quality, earlier overpass time closer to Landsat-8/9 acquisitions, and improved angular effect control, more accurately reconstructed spatial variations and daily temperature dynamics. In contrast, MODIS, with its broader temporal coverage and larger dynamic range, provided more stable Spatiotemporal Fusion (STF) results with slightly higher mean values. The model transferability analysis showed that training models with MODIS data and applying them to Sentinel-3 yielded higher accuracy than the reverse configuration, highlighting the importance of sensor selection and model generalizability. Overall, the findings indicate that Sentinel-3 can serve as a viable alternative to MODIS for STF of LST data under data gaps or reduced-quality time series. Moreover, integrating data from both sensors can ensure the continuity and stability of downscaled LST products and mitigate data limitations in long-term monitoring.
Groundwater is an essential resource contributing substantially to the annual total water supply. It enables agricultural irrigation and provides billions of people with their main source of drinking water. But overuse of groundwater has decreased its supply and, in certain places, resulted in soil subsidence. In the complex hydrogeological terrain of Timergara, traditional groundwater exploration is challenging and costly, requiring more efficient mapping approaches. Groundwater recharge potential zones must be assessed in order to guarantee sustainable groundwater management. This study uses Remote Sensing (RS) and Geographic Information System (GIS) methodologies to evaluate groundwater potential sites in Timergara, Khyber Pakhtunkhwa. This research integrates eight thematic layers; rainfall, geology, slope, soil, land use/land cover (LULC), drainage density, lineament density, and lithology, using the Multi-Influencing Factor (MIF) technique to enhance mapping precision. Data from several sources were integrated to investigate groundwater occurrences through surface and subsurface investigations. Existing water bodies were identified by a GPS survey. For this study, seven important factors were taken into account: rainfall, lineament density, drainage density, geology, soil, land use/land cover, and slope. To create the final Groundwater Potential Sites (GWPS) Index map, these elements were weighted and categorized using the Multi-Influencing Factor (MIF) procedure. This was followed by a weighted overlay analysis in ArcGIS. The results reveal five distinct potential zones: very poor, poor, moderate, good, and very good. The study found that areas that fall under 'very good' and 'good' potential are primarily located in regions with low slope and high lineament density. A regression analysis between the GWPS index and well depth data was performed for validation, showing a strong positive correlation (R2 = 0.7126), confirming the reliability of the GIS-based model. Finding the best locations for water extraction and supporting sustainable groundwater resource management can be facilitated by using the verified groundwater potential map.
This study addresses the challenges of traditional forest inventory methods for Norway spruce (Picea abies (L.) Karst.) by leveraging Sentinel-2 multispectral data to derive critical forest parameters, including biomass, stand density, and site class. Remote sensing offers scalable solutions for large-scale monitoring, yet topographic variability and spectral saturation limit the use of empirical vegetation index (VI)-based approaches. The methodology analyzed 43 Norway spruce subcompartments in Bulgaria's Parangalitsa Reserve using a 2017 Sentinel-2 L2A scene, calculating 24 vegetation indices (e.g., Canopy Chlorophyll Content Index (CCCI), Forest Cover Index (FCI1/FCI2), Normalized Difference Water Index (NDWI) and three biophysical parameters Leaf Area Index, Fraction of Absorbed Photosynthetically Active Radiation, and Fraction of green Vegetation Cover (LAI, FAPAR, FCover). Statistical correlations between spectral indices and inventory data were stratified by slope aspect (north-east vs. south-west) to account for microclimatic influences. Results revealed that the CCCI index showed strong positive correlations with age, height, and stock on NE slopes (r = 0.5-0.6), while FCI1/FCI2 achieved high correlations with site class on SW slopes (r = 0.9-0.93). Negative correlations between FCover/LAI and structural parameters on SW slopes highlighted water stress and shadowing effects. Slope-aspect stratification improved VI-parameter correlations by 12%-18%, demonstrating the necessity of topographic calibration. Overall, the results demonstrate that slope-aspect stratification is the key factor improving the reliability of vegetation index-based estimation of forest structural parameters in complex mountainous terrain. The study validates Sentinel-2's operational potential for Norway spruce monitoring but emphasizes the need for aspect-specific models to address spectral saturation and environmental variability. These findings advance precision forestry by integrating topographic modulation into remote sensing workflows, enabling scalable forest health assessments and informing climate-resilient management strategies.
Extreme climate and weather conditions pose significant risks to public health, economic stability, and the quality of both built and natural environments. This study evaluates projected changes in climate extreme events over the Omo-Gibe River Basin (OGRB), Ethiopia, using 12 CMIP6 Global Climate Models (GCMs). Additionally, rainfall-runoff simulations were assessed using the HEC-HMS hydrological model. Due to the region's susceptibility to extreme hydro-meteorological events like floods and droughts, gaining insight into future climate variability is essential for managing water resources effectively and reducing disaster risks. This analysis examines extreme precipitation and temperature indices under two future climate scenarios, SSP2-4.5 and SSP5-8.5, across the short-term (2023-2053) and mid-term (2054-2084) periods. Daily precipitation and temperature observations from 1984 to 2014, provided by the Ethiopian Meteorology Institute, were utilized for model validation. Bias correction was applied using the distribution mapping method to enhance the accuracy of CMIP6 simulations. Model performance was assessed using statistical evaluation metrics, including the coefficient of determination (R2), mean bias error (MBE), root mean square error (RMSE), and categorical indices such as the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). Results indicate a significant increase in extreme precipitation indices such as R95pTOT (up to +8.73 mm/year) and PRCPTOT (+7.49 mm/year) in specific clusters, while consecutive dry days (CDD) show decreasing trends. Temperature extremes are projected to rise, with TXn and TNn increasing by approximately 0.03 circle C per year. Bias correction substantially improved model performance, reducing mean precipitation bias by approximately 60% and lowering RMSE for extreme indices such as R95pTOT and Rx1day by about 40% relative to the raw simulations. The projections also indicate a decline in consecutive dry days (CDD), with reductions of about 9 days under both SSP2-4.5 and SSP5-8.5 compared to the historical period. Streamflow simulations using the HEC-HMS model reveal a projected increase of 36.3%-92.8% in annual discharge by 2073, with shifting seasonal peaks. These findings highlight the growing risk of extreme climate events in the basin, necessitating adaptive water resource management strategies and improved climate resilience planning.
The integration of Light Detection and Ranging (LiDAR) technology into consumer electronics like smartphones has created new opportunities for the use of three-dimensional (3D) modelling, especially in the domains of infrastructure inspection and civil engineering. This paper presents the accuracy of a 3D bridge model generated using a smartphone LiDAR application in comparison with conventional surveying methods. In this study, LiDAR data were captured using an iPhone 13 Pro and processed to generate 3D models. The accuracy of the generated model was then validated against reference data obtained from a tacheometry survey, which served as a ground truth for Root Mean Square Error (RMSE) determination based upon coordinates, distance measurements and volumetric comparison. The results indicate that smartphone LiDAR achieves coordinate RMSE values within the decimeter to submeter range for both Ground Control Points (GCPs) and bridge edge features. The RMSE values obtained for the GCPs were 0.324, 0.274 and 0.298 m, respectively. Distance analysis revealed moderate differences of 0.278, 0.668 and 0.455 m, respectively, while the volumetric comparison showed a relatively small overall difference of 0.679 m3 between the two methods. These values validate that the smartphone LiDAR is suitable for preliminary bridge documentation, 3D visualization and rapid geometric assessment. Nevertheless, it cannot serve as a replacement for high precision surveying methods required for detailed structural inspection. Overall, the findings demonstrate the potential of smartphone LiDAR as a cost-effective complementary device for bridge modelling applications in the fields of geomatics and civil engineering.
Rainfall data from four weather stations, quite far from each other, but located in the Zambezian phytogeographic region, were analysed for the research for indices of climate change. Two variables, rainfall and the annual number of rainy days, were considered. The rainfall data examined are 114 years for Luanda (1901-2014), 106 years for Lubumbashi (1916-2021), respectively, 54 and 41 years for Huambo (1961-2014) and Boma (1981-2021); 100 years (1921-2021) for the annual number of rainy days for only the Lubumbashi weather station. The results were a widespread decline in rainfall at all weather stations. Despite the general decrease in rainfall, the Mann Kendall trend test, except for Huambo only, within the space-time limits of the present study, does not confirm it in the other three weather stations, corroborating the conclusion of Nicholson's work in the Congo Basin. The declines described in this study would therefore only be episodes in the current climate. Moreover, there would be no causal links between the decline in rainfall and in annual number of rainy days. Indeed, on the one hand, although the decrease in the annual number of rainy days, the annual rainfall totals recorded at weather stations remain very close to the usual annual averages. On the other hand, the association of these two parameters gives a correlation coefficient r = 0.45; that of determination r2 = 0.2012. The decrease in the annual number of rainy days would therefore only explain 20% of the decrease in rainfall. In reality, the concomitant decreases in two parameters have harmful effects on exopercolation and endopercolation in the feeding of groundwater, water retention basins for electricity production and crop irrigation.
Moon-based Synthetic Aperture Radar (SAR) is particularly suitable for monitoring polar regions because of its consistent and continuous imaging. It has promising applications in the observation of sea ice by capturing rapid freeze-thaw cycles in the Arctic and Antarctic. However, the long synthetic aperture time inherent in Moon-based SAR may lead to image defocusing due to water fluctuations. Additionally, large incidence angles during observations in polar regions can result in weak backscatter from sea ice, thereby affecting the signal-to-noise ratio and ice-water discrimination. In this study, a ground-based experiment was conducted to evaluate the impact of imaging characteristics. Dissimilarity measures were employed to assess the ice-water discrimination performance under different SAR parameters. The results showed that VV polarization provides higher accuracy in ice-water discrimination compared to HH, HV, and VH polarizations in the C band. Incidence angles of 30-60 circle ensure effective backscatter of ice in quad-polarization. Underwater fluctuation scenarios, ice and water can still be distinguished in the SAR images. These findings provide insights into the imaging behavior of Moon-based SAR and support the optimization of the system design for polar sea ice monitoring.
Climate classification systems are essential tools for analyzing regional climatic behavior, assessing longterm aridity patterns, and evaluating the impacts of climate change on water resources and ecosystem resilience. This study introduces a new Climate Classification Method based on uniform and unitless variables, referred to as the U2 Climate Classification (U2CC). The proposed U2 Index was designed to overcome structural limitations of the classical De Martonne (1942) and Erin & ccedil; (1949) indices, which rely on raw precipitation-temperature ratios and are sensitive to extreme values, particularly subzero temperatures. The U2 methodology consisted of two key steps: (i) normalization of temperature and precipitation relative to their long-term provincial means, and (ii) restructuring of the climatic year to begin on 1 April, aligning the index with hydrological and agricultural cycles. This approach provided a more stable and comparable representation of climatic moisture balance across T & uuml;rkiye. Using long-term meteorological observations covering the period 1927-2023 obtained from the Turkish State Meteorological Service, national-scale climate maps based on the U2 Index were produced and evaluated in comparison with the De Martonne and Erin & ccedil; classifications. The results indicated that U2CC captured broad-scale climatic patterns consistent with established methods while providing improved representation of transitional climatic zones. Spatial patterns revealed increasing aridity across Central Anatolia associated with declining winter precipitation, whereas the Black Sea and southwestern coastal regions retained humid characteristics due to persistent maritime influence. Overall, the findings demonstrated that U2CC offered a refined and robust framework for climate zoning, agricultural planning, drought assessment, and sustainable water resource management under ongoing climate change conditions.
The expansion of transportation networks, including railways and ports, has been a major force driving urban growth, mobility, and socio-economic transformations since the Industrial Revolution. This study utilizes Historical Geographic Information Systems to examine the global evolution of transportation infrastructure, focusing on railways and ports, from 1880 to 2020. The dataset enables a multidimensional analysis of how transportation systems have shaped cities, influenced regional development, and helped to make possible sustainability efforts. By offering insights into transport accessibility, land-use changes, and economic connectivity, the study provides a robust empirical foundation for understanding long-term infrastructure dynamics. While the dataset supports policy-relevant applications-particularly in relation to sustainability and Sustainable Development Goals (SDG)-related targets- its primary contribution lies in the methodological framework and analytical potential it offers for future research. The integration of historical transport data into urban planning and decision-making processes can help design more resilient, inclusive, and efficient mobility systems, aligned with sustainable development principles.
The use of Unmanned Aerial Vehicles (UAVs) in photogrammetry has grown rapidly due to enhanced flight stability, high-resolution imaging, and advanced Structure from Motion (SfM) algorithms. This study investigates the potential of UAVs as a cost-effective alternative to Terrestrial Laser Scanners (TLS) for 3D building reconstruction. A 3D model of Bangunan Sarjana was generated in Agisoft Metashape Professional v.2.0.2 using 492 aerial images captured at flying altitudes of 40, 50, and 60 m. Ground control points were established using GNSS (RTK-VRS), and Total Station measurements were employed for accuracy validation. The results indicate that the 60 m flight produced the most accurate reconstruction, with a maximum dimensional deviation of 0.051 m. These findings demonstrate that UAV photogrammetry can achieve centimetre-level accuracy in building modelling while significantly reducing costs and operational effort. The study highlights the integration of SfM processing with UAV technology as a practical and efficient method for spatial data acquisition, supporting applications in urban planning, digital twin development, and heritage documentation.
The assessment of groundwater quality is crucial for ensuring its safe and sustainable use for domestic and agricultural purposes. The Kurukshetra district in the Indian state of Haryana relies heavily on groundwater to meet household and agricultural needs. Sustainable groundwater management must be assessed in terms of suitability for domestic and agricultural needs in a region. The current study analyzed pre-monsoon geochemical data from groundwater samples in the study area for 1991, 2000, 2010, and 2020. A Geographic Information System (GIS) was used to create spatial distribution maps for hydrogen ion concentration, total hardness, total dissolved solids, electrical conductivity, sodium adsorption ratio, percent sodium, and residual sodium carbonate. The study area was divided into different groundwater quality zones for domestic and agricultural use as per Bureau of Indian Standards and World Health Organization norms. The integrated maps for agriculture and domestic use were prepared by weighted overlays of these parameters in GIS for 2020, highlighting spatial variations across the district. In 2020, approximately 0.52% of the district's area fell under the good class, while 94.41% was classified as permissible, and 5.07% as the doubtful class in terms of groundwater quality for domestic use. This indicates that the majority of the district falls under the permissible category for domestic water consumption. An area of 51.18% was found as good class, 48.43% as permissible class, and 0.39% as doubtful class for agricultural suitability, which indicates that almost the entire district's water is suitable for agricultural use. These results suggest that a significant portion of the district's groundwater is of acceptable quality for both domestic and agricultural purposes, although certain areas may require closer monitoring and management due to water quality issues. This study offers valuable insights into local water resource management and the promotion of sustainable agricultural practices at the district level.
Urban expansion in semi-arid regions poses critical challenges for sustainable land management, ecological resilience, and heritage conservation. Jaipur, India-a United Nations Educational, Scientific and Cultural Organization (UNESCO) World Heritage City located in a semi-arid environment-faces rapid urbanization that threatens agricultural productivity, fragile ecosystems, and cultural assets. This study quantifies past and projects future land use/land cover (LULC) dynamics in Jaipur to support evidence-based planning. Using the Dynamic World dataset, we generated annual 10-m LULC maps from 2016 to 2025 within the municipal boundary. Temporal change detection was conducted through empirical transition probability analysis, and future scenarios for 2026-2030 were simulated with a Markov chain model coupled with a neighbour-aware cellular automata (CA-Markov) allocation to capture spatial diffusion and terrain constraints. Validation on a 2025 hold-out achieved an Overall Accuracy of 0.79, Cohen's kappa of 0.15, and a figure of Merit of 0.073 for built-up gains, confirming credible localization of urban growth. Results reveal that the built-up area expanded from 340.57 km(2) in 2016 to 387.25 km(2) in 2025 (+13.71%) and is projected to rise by +44.96% by 2030. Over 2016-2025, cropland declined by -40.83%, shrub/scrub by -27.71%, tree cover by -4.12%, and flooded vegetation by -41.28%, while bare ground (+3.14%), grass (-4.22%), and water (similar to+0.18%) showed minimal change. Forecasts for 2016-2030 indicate severe contractions in crops (-98.40%), shrub/scrub (-93.10%), trees (-80.44%), grass (-95.36%), water (-99.53%), bare ground (-99.51%), and flooded vegetation (-99.80%). These findings highlight an accelerating transformation of Jaipur's peri-urban landscape, with built-up expansion occurring at the expense of nearly all productive and ecological land classes. The study demonstrates that CA-Markov-based LULC forecasting provides a reproducible and transparent framework for high-frequency monitoring and offers actionable insights for sustainable urban management in heritage cities under rapid growth pressure.
