Abstract: Urban areas are increasingly affected by geological and climate-driven processes that influence their safety, functionality, and long-term resilience. Conventional sustainability indicators mainly focus on anthropogenic impacts on the environment, while the role of subsurface conditions and physical processes shaping urban vulnerability remains underrepresented. To address this gap, the Urban Geo-climate Footprint (UGF) introduces an inverse perspective, assessing how geological and climatic factors exert pressure on urban systems. The methodology is based on the breakdown of geological effects into five drivers, Geology, Deep Geological Processes, Surface Processes, Exogenous and Climatic Processes, and Subsurface Anthropogenic Pressure. These drivers, in the derived tool, are articulated into 22 parameters evaluated from public datasets and expert judgment. These parameters are combined into a synthetic, standardised, reproducible and comparable index, the UGF Score Index (UGF-SI), ranging from 0 to 500 which enables direct comparison across cities and contexts. The application to 21 Italian cities highlights distinct spatial patterns, dominant drivers, and groups of cities facing similar geo-climatic challenges. The UGF framework represents a significant advancement in urban geoscience, supporting urban planning, risk awareness, and climate adaptation strategies by enhancing the understanding of subsurface-related pressures and promoting informed decision-making.

Abstract: With the increasing availability of high-resolution (< 50m) space borne night time light imagery, it is now becoming more feasible to examine the correspondence between space borne and ground based measurements of night lights. However, so far there were very few studies that conducted a ground-based campaign of night time brightness measurements during the overpass of a night light sensitive satellite. Here we tested whether the correspondence between measurements is higher when ground-based are conducted at the same time of the satellite overpass. We conducted measurements using a LANcube photometer along the same route in two consecutive nights (27-28 Aug 2025) in Brisbane, Australia, and compared them with a SDGSAT-1 (10-40m) and Haishao-1 (10m) images acquired concurrently at the evening, and with an early morning ISS photo (8m) acquired three months earlier. We found the correlation between ground based and space-borne measurements was not higher for simultaneous measurements, and the explanatory power of our model predicting night time brightness as measured from space increased when including horizontal and upwards ground-based brightness measurements along-side variables of canopy height, land use and road hierarchy. We confirmed the importance of multi-directional ground measurements and urban structure for understanding night-time brightness levels measured from space.

Abstract: Amaranthus palmeri has become established in agricultural areas of the Brazilian Cerrado, where it threatens soybean and cotton yields. Conventional field scouting cannot cover the large properties typical of the region fast enough to detect infestation foci before seed set. We tested an automated detection approach using aerial images from a remotely piloted aircraft (RPA), a DJI Matrice 300 RTK with a Zenmuse P1 camera (45 megapixels, MP), processed with YOLOv11x (You Only Look Once, version 11, extra-large). Four field campaigns in Sapezal, Mato Grosso, produced roughly 40,000 images over soybean and cotton at different weed growth stages; flight tests at 90, 20, and 12 m showed that 12–20 m altitude is needed to resolve individual plants. Two specialists annotated 382 Amaranthus individuals (A. palmeri and A. hybridus), split 70/30 for training and validation. Overall performance reached 84% precision, 84% recall, and 88% mean average precision at Intersection-over-Union 0.5 (mAP@0.5); for A. palmeri alone the figures were 95%, 93%, and 99%, respectively, with 98% accuracy in the confusion matrix and virtually no cross-class confusion. Within these limits, RPA imagery and deep learning can replace manual scouting for A. palmeri at the farm scale.

Abstract: The reuse of drinking water (direct and indirect; DPR/IPR) is increasingly being proposed as a strategy to strengthen urban water security in the face of climate variability and increasing demand. Although technological barriers have decreased considerably, many projects continue to face intense social and political conflicts. This article examines why technically viable reuse initiatives thrive in some contexts while failing in others, by developing a conceptual framework for analysing the conflicts associated with DPR/IPR. The study proposes three complementary typological matrices: Justification × Acceptance (J×A), Justification × Urgency (J×U) and Demands × Repertoires (D×R), which integrate the structural conditions of the projects with the strategic dynamics of the actors involved. The framework is illustrated by an empirical corpus of 25 global DPR/IPR cases, compiled through a realistic synthesis of academic literature, technical reports and contextual sources. The analysis shows that project trajectories do not depend solely on technological maturity or water scarcity, but on the interaction between technical justification, social acceptance, perceived urgency and, especially, the strategy and agency capacity of actors to mobilise demands, narratives and repertoires of action. Consequently, the advancement, transformation or blocking of potable reuse projects is mainly explained by how these strategies shape the legitimacy of water risk governance.

Abstract: Understanding the extent to which anthropogenic activity shapes vegetation dynamics is a central challenge in palaeoecology. In the Eastern Mediterranean, pollen-based studies have traditionally identified human impact through qualitative interpretations of anthropogenic indicators, particularly within the framework of the Beyşehir Occupation Phase (BOP) . However, quantitative comparison of anthropogenic signals across multiple sites remains limited. This study compiles pollen datasets from multiple lacustrine records across Anatolia (Türkiye) to construct a regional multi-site dataset and evaluates anthropogenic influence using a quantitative BOP period anthropogenic taxa integrated with Principal Component Analysis (PCA). Anthropogenic impact was quantified using a composite pollen index based on Olea, Juglans, Plantago lanceolata-type, Cerealia and Rumex acetosa-type taxa. The results reveal substantial spatial variability in anthropogenic signals, with combined pollen percentages ranging from less than 1% to 16% among lakes. PCA results show clear inter-site differentiation, with the first two components explaining 42.94% and 21.95% of the total variance, respectively. In particular Olea emerges as the most influential indicator, strongly contributing to the primary ecological gradient. These findings provide a quantitative extension of the traditionally qualitative BOP concept and demonstrate that anthropogenic influence is a fundamental and spatially heterogeneous component of vegetation dynamics across Anatolia. By integrating a composite anthropogenic index with multivariate analysis, this study offers a robust and transferable framework for comparing human–environment interactions across different regions and ecological settings.

Abstract: Real-time quantitative precipitation estimation (QPE) from weather radar is essential for hydrological forecasting, flash flood warning systems, and water resource management. Despite significant advances in radar technology and signal processing, operational QPE systems face persistent challenges including non-meteorological clutter contamination, signal attenuation, vertical profile biases, and systematic errors that require integration with ground-based rain gauge networks. This review synthesizes recent developments in open-source frameworks for radar QPE, spanning the complete processing chain from raw signal correction to operative hydrological validation. We examine state-of-the-art methods for clutter removal (polarimetric fuzzy logic, CLEAN-AP, neural network quality control), C-band attenuation correction (self-consistent and KDP-based approaches), and vertical profile of reflectivity (VPR) correction for warm-rain events. We compare gauge-radar merging techniques including mean field bias adjustment, spatially variable corrections, Kriging with External Drift (KED), and Conditional Merging, with emphasis on real-time applicability and look-back window strategies. The review identifies key open-source Python libraries (wradlib, Py-ART, pySTEPS, radproc, weatherDataHarmonizer) and documents operational latency constraints for flash flood warning systems. A critical research gap is identified: current open-source solutions lack documented workflows for integrating delayed 24-hour manual gauge readings into real-time QPE streams while maintaining low latency. This review provides researchers and practitioners with a comprehensive roadmap for developing robust, open-source, real-time radar QPE systems suitable for operational hydrological applications.

Abstract: Side-scan sonar is a critical instrument for underwater cultural heritage preservation, as it allows large-scale detection of shipwrecks in turbid waters where optical methods fail. However, the automated segmentation of these targets remains a significant challenge, as severe speckle noise and complex seabed reverberations often obscure the distinct geometric features of submerge structures. To address this challenge, this paper proposes SW-Net, which utilizes a multi-scale input strategy and a novel Directional Filter Bank to inject physical priors into the feature extraction process. Furthermore, by coupling this with a Directional Attention Mechanism, the network dynamically modulates structural features to accurately segment targets despite intensity inversions and speckle noise. As demonstrated by the experimental results on the AI4Shipwrecks dataset, the SW-Net outperforms five state-of-the-art architectures, achieving the highest intersection over union of 39.26% and F1-score of 56.38%. In addition, the model exhibits superior robustness against complex seabed interference while maintaining the lowest computational complexity of 4.01 million parameters among the evaluated methods. Taken together, the SW-Net is proposed to offer a practical solution for shipwreck detection on resource-constrained autonomous underwater vehicles.

Abstract: Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through the multi-criteria analysis using GIS and hydrogeological field exploration, water balance modelling. The suitability testing was preliminarily performed in the Google Earth Engine environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests, produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could be recharged into the suitable sites is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that there are recharge rates of between 174 and 5,282 m³/day, with a high degree of spatial variability which is caused by local hydrogeological circumstances. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies.

Abstract: Individual tree classification has a long history of diverse development, with recent trends focusing on the adoption of machine learning and deep learning approaches. A simple and powerful approach that lets the model auto-pilot, but weakens the need for physical characteristic understanding. Over more than a decade of our research, we have focused on establishing a direct representation of individual trees that bridges 2D top-down imagery and true 3D models. In this study, we investigated the fundamental question of the influence of the input data on these ML/DL models. In 2024, we introduced a novel data transformation method, the Pseudo Tree Crown (PTC), which provides a pseudo-3D pixel-value perspective that enhances the informational richness of images and significantly improves classification performance. Our original implementation was successfully tested on urban and deciduous trees in 2024 and was later extended to Canadian natural conifer species under snow conditions in 2025. However, the original PTC relied on the green band, limiting its applicability to green-leaf species. In this study, we analyzed and compared the performance of different data variations and transformations, such as the Green–Red Vegetation Index (GRVI) and Principal Component Analysis (PCA), as direct input and used their PTC forms. Classifications were conducted using Random Forest, ResNet50, and YOLOv10. The results confirmed the effectiveness of the PTC, which consistently improves classification accuracy by at least 7% without introducing additional computational time or complexity. Furthermore, PTC exhibits robust, consistent behaviour across all data forms, demonstrating its strong resilience and reliability.

Abstract: Agricultural waste accumulation offers potential for sustainable soil management in climate-resilient farming systems, but it also poses ongoing environmental challenges. This study examines the effects of vermicomposting, which turns agricultural waste into nutrient-rich organic fertilizer using Eisenia fetida, on crop productivity and soil fertility. Treatments were compared using a randomized experimental design that included many combinations of organic waste and a control. Crop growth and yield indices were examined in addition to soil physicochemical characteristics such as pH, organic carbon, total nitrogen, available phosphorus, and exchangeable potassium. Comparing vermicompost treatments to the control, the soil's nutritional content and structural quality significantly increased (p < 0.05). Mixed organic waste substrate trials outperformed single substrate trials, suggesting synergistic interactions that enhance microbial activity and nutrient cycling. Vermicompost application improved soil fertility indicators and increased crop growth and production. These findings show that vermicomposting is an effective waste valorization technique that supports the circular economy and sustainable agriculture. The study demonstrates how it can reduce environmental pollutants while enhancing soil health, agricultural yield, and fertilizer use efficiency. All factors considered, vermicomposting is a scalable and environmentally friendly way to increase the climate resilience of agricultural systems. More research should be done on long-term field performance, economic viability, and substrate combination optimization under different agroecological conditions.

Abstract: This study presents a comprehensive geospatial framework for landslide risk assessment across the 4,523 km road network of the Region of Epirus in Greece. Utilizing a field-verified inventory of 295 active landslides, the research evaluates five key predisposing factors—lithology, slope inclination, elevation, land use, and cumulative annual precipitation—using the bivariate Frequency Ratio (FR) statistical model. Among six tested scenarios, the most robust model integrated all factors, achieving high predictive accuracy by classifying nearly 80% of the study area within Moderate to Very High susceptibility zones. The resulting Landslide Susceptibility Index (LSI) was converted into a Landslide Hazard Index (LHI) and integrated with a weighted Road Vulnerability Map, which categorized road sectors based on functional importance and traffic volume. The final Landslide Risk Map indicates that the most critical risk zones are clustered along major transportation corridors that traverse geologically weak formations, moderate to high precipitation areas and steep mountainous sectors. This quantitative approach provides a vital decision-support tool for regional authorities, enabling the prioritization of geotechnical monitoring and the strategic allocation of resources for infrastructure stabilization. The methodology offers a replicable workflow for enhancing the resilience of transportation networks in landslide-prone Mediterranean regions facing evolving climatic threats.

Abstract: The color origin of precious coral, a highly valued organic polycrystalline gemstone, has long remained elusive. In this study, an integrated approach employing spectrophotometry, Raman, FTIR, and UV-Vis spectroscopy, coupled with Spearman correlation analysis, was utilized to investigate a color-graded series of precious coral samples ranging from white to red. The results demonstrate that the calcareous skeleton consists exclusively of calcite. The actual chromophores are identified as a blend of multiple distinct polyene species, characterized by Raman shifts at 1126 and 1515 cm⁻¹. Inherently exhibiting a red-orange hue, the progressive accumulation of these polyenes drives a systematic color transition from orange to red.Both absorption bands at 314 nm and 532 nm in the UV-Vis spectra originate from the polyene pigment molecules. Specifically, the broad 532 nm band is dominated by π-π* electronic transitions. As the pigment concentration increases, this band exhibits pronounced broadening and enhancement, accompanied by a redshift of the maximum absorption peak. This spectral evolution leads to an intensified absorption in the yellow-orange region, elucidating the intrinsic mechanism underlying the color transition of precious coral from orange to red with increasing pigment content. This work lays a solid foundation for the non-destructive identification of precious corals and future research on their color genesis.

Abstract: Forests of Lualaba Province (DR Congo) form a compositionally complex mosaic of dry dense forest, gallery forest, and Miombo woodland. Yet, categorical land-cover maps impose discrete boundaries on these inherently continuous vegetation gradients, systematically discarding subpixel compositional information critical for forest monitoring and carbon accounting. The magnitude of this information loss at the landscape scale, however, remains largely unquantified. In this study, we train a Multi-Output Neural Network (MONN) using Sentinel-2 spectral and textural predictors (2025) to estimate the proportional cover of three forest types across the province. Model performance is benchmarked against a normalised Random Forest (RF) using spatial block cross-validation. Categorical information loss is quantified pixel-wise using two complementary metrics, dominant class proportion and Shannon compositional entropy, alongside a derived interpretive quantity, categorical information loss. The MONN slightly outperformed RF (R² = 0.648 vs 0.630; RMSE = 0.224 vs 0.229), yet the results reveal a fundamentally heterogeneous landscape structure. The mean dominant-class proportion was only 56.2%, indicating that categorical maps discard, on average, 43.8% of compositional information per pixel. Only 7.9% of forested pixels exceeded the 75% dominance threshold, while Shannon entropy reached 74.1% of its theoretical maximum, indicating that forest types coexist in near-equal proportions across most pixels. This renders categorical attribution structurally inadequate for most of the forested landscape. Across 92.1% of forested pixels, no single forest type achieved clear dominance. These results show that compositional mixing is the dominant structural condition of the landscape, and that compositional mapping is essential for representing tropical forest structure in heterogeneous drylands. By formally quantifying categorical information loss at the landscape scale, this study shows that continuous compositional mapping converts this structural ambiguity into a spatially explicit ecological signal, with direct implications for monitoring vegetation dynamics and biodiversity, highlighting a structural source of error in carbon stock estimation in tropical dry forests.

Abstract: This study presents an integrated assessment of surface water and groundwater quality in the Tula River basin, Mexico, encompassing the Endhó Dam and its associated aquifer. Water quality index (WQI) analysis revealed severe contamination along the Tula River (WQI >300), driven primarily by untreated sewage discharges from Mexico City and inadequate regional sanitation infrastructure. Elevated concentrations of COD, BOD, and nutrients indicate significant organic loading and eutrophication risk across aquatic ecosystems. Near Tula City, heavy metals including arsenic, copper, and zinc were detected at levels posing direct risks to human health. Groundwater quality was com-paratively favorable, with 71% of sampled wells recording WQI < 100; however, arsenic concentrations exceeding permissible limits by more than twentyfold were identified in select wells, attributed to geological sources. Semi-volatile organic compounds (SVOCs) were detected in both hydrological compartments, confirming cross-compartment con-tamination and highlighting the need for contaminant transport and fate modelling. The inertial contamination trajectory of the aquifer indicates that point-source reduction alone is insufficient for remediation. Comprehensive sanitation strategies, including pre-discharge treatment of Mexico City effluents, alongside proactive long-term aquifer monitoring and remediation programs, are urgently required to safeguard water sup-plies, public health, and ecological integrity in the Tula Valley.

Abstract: Microplastics are particles derived from polymer degradation, and their occurrence and abundance have been assessed across various environments and compartments. The method commonly used for their evaluation and quantification in sediments involves a marine salt solution for decantation. However, due to the high incidence of plastics in marine environments, this salt may already contain a considerable concentration of microplastics and must be carefully filtered to minimize interference during laboratory processing. To assess the importance of this procedure, sediment samples from an estuarine environment, in which the salt used for laboratory sorting was not filtered, were compared with samples from semiarid reservoirs, in which the salt underwent filtration before decantation. All other procedures were identical, performed by the same team under controlled airborne contamination conditions. The Mann–Whitney test applied to samples with and without NaCl filtration revealed a significantly lower incidence of microplastics in samples whose salt had been filtered. Based on these findings, a filtration protocol for NaCl used in sediment decantation was developed, emphasizing an accessible, low-cost product widely applied in natural environmental quality assessments. Only through the standardization of methodologies and sampling units will it be possible to compare environments in terms of actual anthropogenic impact, generating outcomes that provide scientific support for conservation actions and impact mitigation.

Abstract: Mars climatology is a growing interest domain for planetary research and for operational missions. In the last three decades, Martian General Circulation Models have been developed to support the interpretation of spacecraft and telescopic observations and for the advancement of theoretical understanding of the climate. They have been designed to represent key processes, such as dust cycle, seasonal CO2 condensation and interaction between boundary layer and surface. At the same time, new observations from orbiters and landers have enhanced the diagnostics, but several uncertainties in the parameterization, especially in dust representation and turbulent mixing, require further improvements. This review represents a synthesis of the state of the art of existing global and regional models, comparing numerical and physical approaches, identifying the main challenges for the next years, with particular attention to the needs of operational missions and machine learning techniques.

Abstract: Tropical forests in southwestern Ethiopia are increasingly shaped by land-use systems that integrate biodiversity conservation with agricultural production, particularly semi-coffee forest management. This study compared woody species diversity, composition, forest structure, and regeneration status between natural forest and adjacent semi-coffee forest in Kersa District, Jimma Zone. A systematic stratified sampling design using 60 plots (20 m × 20 m) was employed. Vegetation data were analyzed using the Shannon–Wiener diversity index, species richness, Sørensen similarity index, Importance Value Index (IVI), and regeneration status. A total of 75 woody species from 45 families were recorded, with 69 species in natural forest and 55 in semi-coffee forest. Floristic similarity was high (0.79), but natural forest retained more unique species (29%) than semi-coffee forest (10.9%). Shannon diversity was higher in natural forest (3.79) than semi-coffee forest (2.55), though not statistically significant. Stem density was significantly greater in natural forest. Regeneration was good in natural forest and fair in semi-coffee forest, with significantly higher seedling and sapling densities. Overall, semi-coffee forests maintain species similarity but show reduced structural complexity and regeneration potential due to management practices.

Abstract: Three-dimensional geological structural modelling provides the geometric framework for subsurface exploration and development. However, conventional workflows, driven primarily by seismic interpretation, often lack explicit constraints from expert knowledge and are difficult to update when interpretations evolve. This study proposes an intelligent modelling methodology guided by a geological structure knowledge graph. The method includes: (i) a Three-tier Knowledge Architecture (TKA) that formalises domain knowledge in entity, relationship and inference layers using RDF/OWL; (ii) a Knowledge-driven Intersection Line Generation Algorithm (KILGA) coupled with a hierarchical adaptive mesh refinement scheme based on a posteriori error estimation (HAMR-APEE) to integrate geological constraints and mitigate boundary aliasing; and (iii) a bidirectional linkage mechanism between the knowledge graph and 3D models to support incremental updates following knowledge revision. The approach is validated in three petroliferous basins in China (Ordos, Qaidam and Sichuan), representing micro-amplitude, thrust nappe and deep complex structural styles. Compared with a conventional Petrel-based workflow, the proposed method reduces modelling RMSE from 15–20 m to 5–8 m, improves geological reasonableness from ~85% to >95%, and shortens modelling cycles from months to weeks.

Abstract: Building change detection from bi-temporal remote sensing imagery underpins urban planning, infrastructure monitoring, and disaster assessment. Existing deep-learning methods achieve high accuracy but rely on large parameter counts, while pixel-level supervision provides limited boundary guidance. We propose POCA-lite, a lightweight encoder-decoder with an inference-coupled geometry branch: three geometric prediction heads—distance transform, boundary, and center heatmap—whose outputs are fused back into the decoder via a feedback pathway active at both training and inference. On the LEVIR-CD benchmark under a unified retraining protocol, multi-seed evaluation shows that POCA-lite matches SNUNet in mean F1 while using 47% fewer parameters and 53% fewer FLOPs. Boundary F1 improves by 9.22 pp over the no-geometry baseline. Decomposition ablations reveal two complementary improvement sources: geometric supervision alone recovers 85% of the total gain, while the feedback fusion pathway recovers 92%; their combination achieves the full result. Geometry-aware targets outperform a generic multi-task control. Cross-architecture transfer to SNUNet yields +1.06 pp F1. However, cross-dataset evaluation on WHU-CD shows that the method underperforms SNUNet on dense urban morphology, and zero-shot cross-dataset transfer is not established. These results indicate that inference-coupled geometric supervision is effective for lightweight, boundary-sensitive change detection on domains with well-separated building morphology, but its applicability is scope-bounded.

Abstract: Energy stands as an integral thread, intricately woven into the grand tapestry of a nation's progress, forming the very fabric of development. While extensive research has shed light on energy production and consumption in industrialized nations, there has been a noticeable dearth of focus on renewable energy research and development in the realm of developing countries. These nations, including Ghana, find themselves bound to fossil fuels, with scant regard for the inherent value of traditional fuels like biomass. Ghana, heavily depends on imported fossil fuels, which is not sustainable in the long run. The pollution caused by these fuels is a major concern, and their increasing cost hampers economic growth. However, there is a glimmer of hope as renewable energy sources are gaining prominence. Biomass, biofuel, wind, and solar energy are emerging as promising alternatives for the future. In this study, we embark on an exploratory study into these renewable energy sources, and how they are intricately entwined with policies, market dynamics, and the impact on food security. The government of Ghana has fostered a conducive environment for the renewable energy sector, epitomized by the establishment of the novel feed-in tariffs (FITs) program. Adept institutions have developed acts and legislations, exemplified by the visionary Renewable Energy Act (832) of 2011, paving a path for progress. The research showed that the use of renewable energy sources has increased gradually during the previous decades. However, limitations on exploitation remain owing to factors like high technology costs, little funding, and gaps in knowledge.