Search Results (6,634)

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which requires 2–7 min per image and limits scalability. We present a hierarchical deep learning pipeline that automates this measurement by integrating YOLO-based detection with Segment Anything Model (SAM) segmentation. YOLO localizes recruits and classifies them by developmental stage; stage-specific fine-tuned SAM models then segment live tissue using bounding box and background point prompts to suppress segmentation leakage and improve boundary precision. Surface area is computed directly from the segmented masks using pixel size extracted from image metadata. The pipeline reduces processing time to approximately 3–5 s per image—a 24–140× speedup over manual tracing. Evaluated on 3668 microscopy images from two national coral research facilities, the system achieves a mean IoU exceeding 95% and an auto-acceptance rate (AAR) of 71.51%, where predicted-to-ground-truth area ratios fall within a ±5% tolerance of expert annotation, substantially reducing manual workload while maintaining measurement reliability across species, developmental stages, and imaging conditions. This workflow addresses a critical bottleneck in restoration research and demonstrates the broader applicability of AI-based image analysis in marine ecology. Full article

Soil organic carbon (SOC) stocks in cacao agroecosystems are characterized by accumulating large amounts. They depend on the balance between organic matter inputs (plant residues, roots) and losses (decomposition, erosion), being closely related to climatic conditions, soil nature, vegetation type, topography, and land management practices. The objective of this study was to quantify SOC stocks (0–30 cm) and assess key soil fertility indicators across 107 georeferenced sampling locations in cacao production systems of Guamal (Meta, Colombian Llanos Piedmont). Soil pH varies between extremely acidic and moderately acidic (3.8–6.0; mean 4.57), while available P (Bray II) and exchangeable bases showed low concentrations. Organic carbon concentration averaged 1.18% and bulk density averaged 1.17 g cm⁻³. SOC stocks averaged 41.10 Mg C ha⁻¹, ranging from 7.49 to 81.55 Mg C ha⁻¹, evidencing marked spatial contrasts in carbon storage. Spearman correlations highlighted coupled soil chemical controls, including positive associations of pH with Ca²⁺ and P availability and strong negative associations of pH and P with exchangeable Al³⁺, consistent with acidity-driven fertility constraints. Principal component analysis (PCA) further identified a dominant fertility gradient structured by pH, P availability, and Ca²⁺, and a second axis related to organic carbon and cation retention. Spatial modeling using inverse distance weighting (IDW) in ArcGIS supported the visualization of SOC stock variability across the study area. Overall, the results indicate that SOC stocks in these predominantly sandy soils are strongly influenced by acidity-related constraints and heterogeneous nutrient status, underscoring the need for site-specific management to jointly enhance soil fertility and climate-mitigation potential in cacao systems. Therefore, it would be advisable in the future to address the study of differential variations in soil C storage related to chemical fertilizer application rates, especially in the long term. Full article

The accuracy of Digital Elevation Models (DEMs) plays a crucial role in determining their reliability for geoscientific and engineering applications. Next-generation distributed interferometric synthetic aperture radar (SAR) constellations, such as the PIESAT-1 wheel constellation with its “one primary, three secondary” setup, provide a novel method for efficiently acquiring high-precision DEMs. However, a comprehensive and systematic performance evaluation of DEMs derived from such an innovative constellation is lacking, particularly in the context of comparative studies under complex terrain conditions. This study uses PIESAT-1 SAR imagery to generate a 10 m resolution DEM through multi-baseline interferometric processing. The ICESat-2 ATL08 dataset serves as the reference baseline, and mainstream products, including ZY-3, GLO-30, TanDEM-X DEM, and AW3D30, are incorporated for a multidimensional vertical accuracy evaluation, considering land cover, slope, aspect, and topographic profiles. The results indicate that, in three representative mountainous regions, the PIESAT-1 DEM achieves optimal overall accuracy (RMSE = 3.25 m). Furthermore, in regions with significant radar geometric distortions, such as south-facing slopes, vegetation-covered areas, and regions with noticeable anthropogenic topographic changes, the PIESAT-1 DEM demonstrates superior stability and information capture capabilities relative to conventional single- or dual-baseline SAR systems. This study validates the technological potential of the PIESAT-1 wheel constellation in enhancing DEM accuracy and terrain adaptability, and provides insights for the scientific selection of high-resolution topographic data and the design of future spaceborne interferometric missions. Full article

Low-level flight or maneuvering defines a flight phase that is particularly common and, in some cases, central to helicopter operations, but brings several safety concerns. At low altitude, helicopters are more susceptible to collisions with objects, while there is also limited time and space in which to perform an emergency landing. A total of 403 helicopter accidents in the low-level flight phase that occurred between 1 January 2009 and 31 December 2022 in the US were analyzed for their most common causes and differentiated based on the type of flight operation to gain insight into low-level flight accidents. It is shown that, for low-level flights, the proportion of fatal accidents in flights conducted under Federal Aviation Regulations Part 91, General Aviation, is 30%, but in flights conducted under Part 137, aerial application or agricultural flights, only 12%. Logistic regression analysis shows that while controlling for other factors, the proportion of fatal accidents was significantly higher in Part 91 operations. Flight experience measured as total flight hours was not a significant factor for estimating fatality. It is recommended that low-level helicopter training includes low-altitude autorotations in simulators to optimize the mitigating effect of this emergency procedure in this flight phase with a specific focus on Part 91 operations. Full article

Conventional applications of the CLUE-S model rely on a static driver assumption, using driver data and their associated coefficients from a base year to simulate land-use patterns for a future target year—an approach that implicitly assumes temporally invariant human–land relationships. To address this limitation, this study introduces and compares two simulation models: the Baseline-Driven Pattern (BDP), which follows the conventional protocol by employing base-year drivers to project future land use, and the Target-Driven Pattern (TDP), which instead utilizes driver data and coefficients that correspond synchronously to the target year, thereby capturing the dynamic evolution of driving mechanisms over time. In terms of implementation, the TDP involves updated driver datasets and regression coefficients, enabling a more accurate spatial allocation of land-use demand. Comparative experimental results from Xiamen in China demonstrate that the TDP achieves higher simulation accuracy than the BDP simulation, with notably greater sensitivity to dynamic factors such as transportation infrastructure and policy boundaries. For study periods 1989–2000 and 2000–2010, the accuracy of TDP simulation for all land-use types surpasses that of BDP simulation. As time progresses, the advantage of TDP simulation over BDP simulation becomes more pronounced, resulting in a significant improvement in the simulation accuracy. These findings confirm that the temporal alignment between driver data and the simulation period is a critical determinant of CLUE-S simulation accuracy. This methodological refinement holds significant implications for model-based land-use planning: it allows simulation procedures to explicitly incorporate future driver conditions articulated in planning documents. Moreover, it equips decision makers with a more realistic simulation tool for evaluating the land-use consequences of alternative planning interventions in scenario-based analyses. Full article

With the increasing complexity of wildfire emergency response, the aerial emergency response system is imposing increasing demands on both safety and decision rationality of takeoff and landing site selection. Site selection decisions are influenced by multi-dimensional factors, including geographical location, meteorological factors, and operational safety considerations, resulting in a pronounced coupling of multiple factors in the decision-making process. However, existing studies primarily focus on spatial suitability evaluation or technical implementation, often relying on predefined indicator systems and independence assumptions, while lacking a systematic characterization of the influencing factor system and its interrelationships in takeoff and landing site selection. To address this gap, this study proposes a novel structured decision-making framework to systematically analyze and optimize the selection of takeoff and landing sites for bucket firefighting helicopters in wildfire aerial emergency response scenarios. First, a procedural grounded theory approach is employed to systematically identify the influencing factors associated with site selection, thereby constructing a traceable decision-making factor system. Second, fuzzy DEMATEL is applied to model the causal relationships and structural interdependencies among these factors. Finally, a cumulative contribution rate based on centrality is introduced to screen and optimize the decision indicators, resulting in a refined set of key decision indicators. The results reveal the structural roles of different influencing factors in site selection, reduce the reliance on experience-driven judgment, and reconceptualize the problem from traditional indicator weighting and ranking into a structured decision-making process involving multi-factor coupling. This provides systematic decision support for takeoff and landing site selection in wildfire aerial emergency response and establishes a foundation for subsequent spatial suitability analysis and case-based validation. Furthermore, the results are consistent with expert experience and practical operational constraints, indicating the potential applicability of the proposed method in real-world decision-making. Full article

The urgency to mitigate greenhouse gas emissions and address the accelerating impacts of climate change has placed renewable energy as a core part of global climate strategies. However, the expansion of renewable infrastructures with a focus on solar systems often generates competition with other land uses, raising concerns about land availability, environmental integrity, and social acceptance. Renewable energy solutions deployment must be aligned with sustainable land-use planning, particularly in diverse and multifunctional landscapes. This study presents a GIS-based Multi-Criteria Decision-Making (MCDM) methodology to identify the most suitable areas for implementing a set of six land-use-based adaptation and mitigation solutions (LAMSs) focused on solar energy. Using Python-based processing algorithms and high-resolution spatial datasets, the methodology integrates technical, environmental, and socioeconomic criteria to generate suitability maps for three different case studies across Europe: Almería (Spain), Valle d’Aosta (Italy), and the Azores (Portugal). Results reveal significant geographical disparities in suitability due to the different land constraints. Almería and the Azores demonstrate high potential for photovoltaic and agrovoltaic farms, while Valle d’Aosta’s mountainous terrain is more limited for these measures. Floating solar and solar land management measures show limited applicability across all sites. The analysis highlights the value of place-based approaches in energy planning and the utility of GIS-MCDM tools to support evidence-based decision-making, enabling context-sensitive deployment of renewable energy infrastructure. Full article

The paper presents SFPD, the new open algorithm developed by the University of Padova (PD in the acronym) for computing the steady-state regime due to any number of simultaneous faults (SF at the beginning of the acronym) both short circuits and open conductors. The algorithm does not have simplified hypotheses, since it benefits from the pre-fault regime based on PFPD_MCA (power flow by University of Padova with multiconductor cell analysis), a multiconductor power flow (developed and published by the first author) which takes into account both the active conductors (i.e., the phases subjected to the impressed voltages) and the passive conductors (i.e., the interfered metallic conductors, namely earth wires of overhead lines, metallic screens and armors of land and submarine cables, enclosures of gas insulated lines, return and earth wires of 2 × 25 kV AC high-speed railway supply system, etc.). Different types of faults are considered, and where they occur (also along the lines), by means of a suitable admittance matrix in phase frame of reference and embedded inside the overall network bus admittance matrix. Some comparisons with simplified approaches are presented in order to demonstrate the power of the method. Eventually, application to the real Italian network is comprehensively shown. Full article

The use of satellite Differential Synthetic Aperture Radar Interferometry (DInSAR) has transformed the analysis of landslide dynamics by enabling detailed spatiotemporal monitoring of slow and subtle ground deformations. DInSAR enables comprehensive geomorphological characterization and identification of triggering factors. Retrospective applications of DInSAR provide valuable insights into past events and support causal analysis linked to rainfall episodes or piezometric fluctuations. Moreover, integration with numerical modeling enhances predictive capabilities and facilitates the calibration of geotechnical parameters. DInSAR is also instrumental in assessing infrastructure impacts and in the generation of susceptibility, hazard, vulnerability, and risk maps, which are key for land-use planning and risk management. Nevertheless, this technique has inherent limitations that must be carefully considered when interpreting results. Future developments, driven by the integration of artificial intelligence and enhanced computing capacities, are transforming the landscape of InSAR applications in landslide studies. These advancements, combined with upcoming satellite missions, are expected to significantly improve measurement accuracy, temporal resolution, and overall operational potential, paving the way for more robust quasi-early warning systems for landslide prevention. In this work, an overview of the current applications, future trends, and challenges of DInSAR in landslide studies is presented, with particular emphasis on the practical dimension of landslide studies and on the exploitation of DInSAR outcomes to support risk management and mitigation strategies. Full article

Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments. Full article

The large-scale deployment of photovoltaic (PV) systems increasingly faces land-use conflicts, particularly in regions with high environmental sensitivity resulting from intensive urban development. Consequently, decision-makers require transparent, spatially explicit tools to identify suitable areas for utility-scale PV installations (>100 kWp). This study addresses these challenges through the application of a Geographic Information System (GIS) to locate optimal sites for solar farms along the coastal zone of the Region of Murcia (southeastern Spain). First, the research characterizes the territorial context and systematically reviews the European, national, and regional regulatory frameworks to identify relevant legal and environmental constraints. These constraints are translated into thematic layers within the GIS environment and progressively applied to exclude unsuitable land through spatial editing and overlay analyses. The remaining feasible areas are subsequently evaluated according to their photovoltaic potential using publicly available solar resource data. The results show that nearly one quarter of the coastal territory is legally and environmentally suitable for PV deployment. Furthermore, due to the favourable geographical conditions of this Spanish region, the annual photovoltaic potential along the coastal zone reaches nearly 48,000 GWh, which would not only meet the Region of Murcia’s annual electricity demand (approximately 8000 GWh) but also supply neighbouring areas in southeastern Spain. Full article

Against the backdrop of intensified climate change and increasingly prominent imbalances in resource supply and demand, achieving multi-objective collaborative optimization of the Water-Energy-Food-Carbon (WEFC) nexus under uncertain conditions has become a pivotal task for regional sustainable development. Taking the Heihe River Basin, a typical arid inland river basin in northwest China with a complex WEFC nexus, as the research area, this study develops a multi-objective collaborative optimization model for the WEFC nexus, targeting three core goals: maximizing crop irrigation water productivity, minimizing carbon emissions, and enhancing low-carbon agricultural competitiveness. The model embeds constraints of regional water security, food security, land policy, and total water resource availability, introduces the uncertainty parameter τ to quantify fluctuations in available surface water, and adopts the ideal point method to convert the multi-objective problem into a single-objective optimization task by minimizing the Euclidean distance between feasible solutions and the ideal solution, with a case application in the oasis area of the basin’s middle reaches. Results show the model exhibits excellent stability across varying uncertainty levels: crop irrigation water productivity stabilizes around 1.5 kg/m³, low-carbon agricultural competitiveness at approximately 0.1003 kg/yuan, and spatial differences in resource allocation are evident. Linze gains the most water resources (16.47 × 10⁸ m³) due to geographical advantages, while Gaotai obtains the least (6.51 × 10⁸ m³). In terms of planting structure, vegetables dominate the sown area owing to low carbon emissions and high water use efficiency, while wheat planting is relatively limited by climate adaptability and market demand. Carbon sink analysis confirms vegetables as the primary carbon sequestration contributor in Ganzhou and Linze, offering a practical pathway for agricultural carbon reduction. These findings provide tailored theoretical and practical support for balancing food security, efficient resource utilization, low-carbon development, and ecological protection in arid and semi-arid regions, facilitating regional carbon neutrality and sustainable agricultural development. Full article

Historical remote sensing imagery, including archival aerial photographs and declassified satellite imagery, has been increasingly used to extend earth observation records into periods not covered by modern satellite missions. However, the broader application of these data remains constrained by georeferencing challenges related to incomplete metadata, uncertain acquisition geometry, and heterogeneous image characteristics. This systematic review examines georeferencing practices for historical remote sensing imagery. Out of the 2547 studies identified in the literature, 205 peer-reviewed journal articles were deemed eligible for analysis. This systematic review provides the first comprehensive, PRISMA-compliant synthesis of georeferencing practices for historical remote sensing imagery, analyzing 205 peer-reviewed studies to establish methodological patterns and identify critical gaps. The review considers imagery types, spatial and temporal distributions of case studies, georeferencing workflows, geometric constraints, and accuracy reporting practices. The results indicate a strong reliance on ground control points and a clear preference for manual or semi-automatic georeferencing approaches, while fully automatic methods remain rare. Although the use of historical imagery has increased over time, its potential has not been fully exploited due to persistent georeferencing difficulties, and study areas are often spatially limited or selectively processed to achieve acceptable accuracy. Nevertheless, properly georeferenced historical imagery has been widely applied to land-cover analysis, geomorphology, cryosphere research, hazard assessment, and archeology by extending observation records into earlier decades. Full article

Comparative Life Cycle Assessments (LCAs) of bio-based materials are highly influenced by methodological choices, so the term “bio-based” does not necessarily imply a low environmental impact. This review analyzes over 50 peer-reviewed LCAs (2010–2024) to quantify how four methodological pillars—(i) attributional versus consequential modeling, (ii) timing and storage of biogenic carbon, (iii) Direct Land-Use Change (LUC) and Indirect Land-Use Change (ILUC), and (iv) allocation in multifunctional systems—drive variability across long-life construction and short-life packaging/composites; adding regionalized perspectives (e.g., water scarcity according to the AWARE initiative, and relevant inventories for the MENA region) and ex-ante LCA guidance aligned with technology readiness levels. Methods included systematic selection from Web of Science/Scopus databases, standardized functional units, system boundaries, impact methods (ReCiPe/EF/TRACI/AWARE), biogenic carbon conventions (GWP100, dynamic/GWPbio), LUC/ILUC handling, allocation rules, and end-of-life scenarios, followed by qualitative meta-synthesis. Results show ~85% of studies used attributional approaches; consequential models typically report higher climate impacts when ILUC is included. In the building applications, bio-based alternatives—particularly wood—reduced cradle-to-critical-state global warming potential (GWP) by 30–70%; a “negative GWP” only emerged when storage balances or dynamic characterization were applied. For bioplastics, climate benefits are context-dependent and can disappear once ILUC and agricultural inputs are considered; acidification and eutrophication frequently increase. We conclude that environmental performance is subject to methodological choices rather than bio-based origin; systematic trade-offs persist between reducing GWP, increasing eutrophication/acidification, and increasing pressure on water/biodiversity. Full article

The flying car, academically known as electric vertical takeoff and landing (eVTOL) aircraft, is one of the core vehicles for low-altitude transportation. The split-type tilt-rotor cargo flying vehicle that is composed of tilt rotors, a fixed wing, and a detachable cargo pod exhibits characteristics of rotor–wing coupling and significant changes in weight and center of gravity (CG). Therefore, empirical design rules for conventional aircraft are not directly applicable. This paper presents the stability analysis of two configurations, i.e., the aerial vehicle module (AVM) and the aerial cargo configuration (ACC). The dynamic model of the proposed cargo flying vehicle is developed. Based on test data from the tilt-rotor experimental bench, the CFD models of the rotor subsystems and the full vehicle were validated and subsequently used to simulate the aerodynamic performance and stability of the flying vehicle under various operating conditions. The results indicate that vertical takeoff and landing (VTOL) stability is highly sensitive to the rotor–CG lever arm. Under cruise conditions, the CG positions were tested within a range of 1.4–1.7 $c_A$ (mean aerodynamic chord) from the wing leading edge with the most favorable static stability observed at 1.62 $c_A$. Among the three proposed tilt-rotor strategies, initiating the secondary tilt rotors first while keeping the main tilt rotors vertical results in the weakest rotor–surface aerodynamic coupling, the lowest pitching-moment peaks, and favorable longitudinal static stability. These findings inform CG management, aerodynamic layout, and tilt-schedule design for split-type tilt-rotor cargo vehicles in low-altitude transportation. Full article