Search Results (15,080)

One of the key problems humanity faces in this age of profound digitalization is globalization-related threats, which no longer affect just one country but pose a threat to a very large area, encompassing several or even a dozen countries, or, in the case of global warming, a threat to all of humanity worldwide. This topic inspired the investigation and verification of this threat in the Baltic Sea, along with other threats operating in the Baltic Sea region. This topic is highly topical, as estimates from maritime institutions indicate that the rate of sea level rise is an irreversible process, which, when combined with other threats, could lead to the degradation of the sea and the population living in the coastal zone. This led to the delegation clarifying the main objective of the article: to demonstrate the impact of potential global threats on the investment process in the Polish coastal belt. Based on this, an analysis of threats in the Baltic Sea region was conducted, preceded by a review of the literature and data from online resources, including data from industry portals in the maritime sector. This article presents a simulation of erosion-accumulation changes in selected areas of Poland’s Southern Baltic coast, focusing on the coastal real estate market and indicating the propensity to invest in these areas. Simulating erosion changes, using a cartographic base with a generated digital terrain model and interpolation tools to visualize the changes, represents an innovative approach to issues related to the outflow of investment land in the real estate market. This emphasizes the directionality of land changes, thus providing a predictive tool for decision-making and spatial planning in the coastal area. Full article

We present a novel concept of an electromagnetic mechanism for shifting the centre of gravity (CoG) in a small unmanned aerial vehicle with four rotors (quadrotor). Shifting the CoG is essential for controlling drones in which the thrust is unbalanced (e.g., upon the failure of one of the drives). The concept presented here involves using electromagnetic coils mounted under the drone and moving permanent magnets inside a cylindrical tube. Moving the positions of the masses can be controlled by means of currents in the coils. Changing the position of the magnets relative to the arms of the drone causes a shift in the CoG, allowing for controllability even when one of the four engines is not working, and making it possible for the drone to land safely. This article describes the geometrical and mechanical relationships in the proposed system, the design and numerical calculations of the electromagnetic mechanism with coils and permanent magnets, as well as the results of a simulation of the control variant. Additionally, the practical implementation of the mechanism, from CAD modelling through the manufacturing of its elements to the final structure prepared for mounting on a quadrotor, is discussed. Full article

The cover-management factor (C-factor) plays a pivotal role in soil erosion control and is the most easily influenced by policymakers. Despite the availability of numerous C-factor estimation methods, systematic comparisons of their applicability and associated uncertainties remain limited, particularly for future projections under climate change scenarios. This study systematically evaluates multiple widely used C-factor estimation models and projects potential C-factor changes under future scenarios up to 2070, using 2015 as a baseline. Results reveal substantial spatial variability among models, with the land use/land cover-based model (CLu) showing the strongest correlation with the reference model (r = 0.960) and the lowest error (RMSE = 0.048). Using the CLu model, global average C-factor values are projected to increase across all Shared Socioeconomic Pathways–Representative Concentration Pathways (SSP-RCP) scenarios, rising from 0.077 to 0.079–0.082 by 2070. Statistically significant trends were observed in 28.0% (SSP1-RCP2.6) and 26.6% (SSP5-RCP8.5) of global land areas, identified as hotspot regions (HRs). In these HRs, mean C-factor values are expected to increase by 16.1% and 33.4%, respectively, relative to the 2015 baseline. Economic development analysis revealed distinct trajectories across income categories. Low-income countries (LICs, World Bank classification) exhibited a pronounced dependency on development pathways, with C-factor values decreasing by −50.3% under SSP1-RCP2.6 but increasing by +95.8% under SSP5-RCP8.5 compared to 2015. In contrast, lower-middle-income, upper-middle-income, and high-income countries exhibited consistent C-factor increases across all scenarios. These variations were closely linked to cropland dynamics, with cropland areas in LICs decreasing by 64.6% under SSP1-RCP2.6 but expanding under other scenarios and income categories between 2015 and 2070. These findings highlight the critical importance of sustainable land-use policies, particularly in LICs, which demonstrate the highest magnitude of both improvement and degradation under varying scenarios. This research provides a scientific foundation basis for optimizing soil conservation strategies and land-use planning under future climate and socioeconomic scenarios. Full article

Urban Heat Islands (UHIs) are urbanized areas that experience significantly higher temperatures than their surroundings, contributing to thermal discomfort, increased air pollution, heightened public health risks, and greater energy demand. In Bhutan, where urban expansion is concentrated within narrow valley systems, the formation and intensification of UHIs present emerging challenges for climate-resilient urban development. Thimphu, in particular, is experiencing rapid urban growth and densification, making it highly susceptible to UHI effects. Therefore, the aim of this study was to evaluate and simulate UHI conditions for Thimphu Thromde. We carried out the simulation using a GIS, multi-temporal Landsat imagery, and an Artificial Neural Network model. Land use and land cover classes were mapped through supervised classification in the GIS, and surface temperatures associated with each class were derived from thermal bands of Landsat data. These temperature values were normalized to identify existing UHI patterns. An Artificial Neural Network (ANN) model was then applied to simulate future UHI distribution under expected land use change scenarios. The results indicate that, by 2031, built-up areas in Thimphu Thromde are expected to increase to 72.82%, while vegetation cover is projected to decline to 23.52%. Correspondingly, both UHI and extreme UHI zones are projected to expand, accounting for approximately 14.26% and 6.08% of the total area, respectively. Existing hotspots, particularly dense residential areas, commercial centers, and major institutional or public spaces, are expected to intensify. In addition, new UHI zones are likely to develop along the urban fringe, where expansion is occurring around the current hotspots. These study findings will be useful for Thimphu Thromde authorities in deciding the mitigation measures and pre-emptive strategies required to reduce UHI effects. Full article

To address the insufficient quantitative understanding of vegetation driving mechanisms across spatio-temporal scales, this study integrated multi-source data and machine learning methods to simulate and analyze Normalized Difference Vegetation Index (NDVI) changes in the Beijing–Tianjin–Hebei (BTH) region over the past two decades. Using the SHapley Additive exPlanations (SHAP) method, we identified the most important predictors of climate and human activities in the XGBoost model and quantified their spatial contributions. We further analyzed the spatio-temporal variation of the main predictors across different land use types The main findings were as follows: (1) The XGBoost model achieved excellent performance (R² > 0.96, MEA < 0.02, RMSE < 0.027) on the datasets from 2000 to 2020, outperforming random forest (RF), support vector machines (SVM), and K-nearest neighbors (KNN) in prediction accuracy. (2) Vegetation showed an overall improving trend, with areas exhibiting significant improvement accounting for 47.96% of the total region. Precipitation, temperature, and human activities were identified as the most significant predictors of NDVI. Their relative importance varied over time, and NDVI responses to these factors exhibited clear spatial heterogeneity. (3) Primary predictors differed by land use type: NDVI in cropland and grassland was mainly driven by precipitation, forest NDVI by temperature, and urban/built-up areas by human activities. This study developed an analytical framework integrating nonlinearity and spatial heterogeneity, achieving a quantitative “overall-categorical” analysis of the important predictors behind NDVI changes. The approach provided a novel methodological reference for attributing vegetation dynamics. The findings contributed to the implementation of classified regulation in the BTH region, promoting the transition of human activities toward ecological restoration. Full article

Soil microbial carbon use efficiency (CUE) is the core of the soil carbon (C) cycle that captures a dual microbial control point between soil organic C (SOC) accumulation and loss. The interpretation of these patterns and drivers of microbial CUE after long-term afforestation remains, however, a major scientific challenge. In particular, there are major uncertainties about the role of microbial traits in driving CUE. Here, we compared sites along a 45-year afforestation chronosequence and combined the novel ¹⁸O-H₂O tracer method with metagenomic analysis to quantify CUE and explore the mechanisms underlying microbe-mediated C dynamics. The results showed that soil microbial CUE significantly increased following afforestation and showed a positive relationship with SOC, which suggested that microbial CUE could promote C accumulation in afforested ecosystems. We further found the critical role of microbial traits in the regulation of CUE through altering microbial life history strategies: microbial CUE was positively and significantly correlated with resource acquisition (A) genes, but showed a negative and significant correlation with stress tolerance (S) strategy genes. These results suggested that soil microbes reduce investment in S strategies and shift to A and high yield (Y) strategies, thereby increasing CUE. This knowledge is important because it advances our understanding of the microbial physiological and evolutionary tradeoffs mediating soil C cycling in the context of human-induced land use change. Full article

Small Island Developing States (SIDS) face ongoing challenges in balancing agricultural sustainability with economic growth due to limited land resources, rapid urbanisation, climate change, and reliance on food imports. This study explores the evolution of land use and the future of agriculture in Mauritius from 2002 to 2022, using satellite imagery, policy reviews, and stakeholder interviews. Findings show a 9% decrease in agricultural and non-agricultural vegetation cover, alongside a doubling of built-up areas from 10% to 20%, indicating continued land conversion pressures. The analysis highlights major barriers to agricultural sustainability, including declining food self-sufficiency, an ageing farming population, and slow movements towards sustainable practices caused by low profitability and weak institutional support. Diverging priorities among government agencies, sugar companies, smallholder farmers, and NGOs further hinder coordinated policy efforts. To address these challenges, the study identifies strategies for aligning economic and environmental goals through integrated land-use planning, boosting productivity, and providing targeted support for sustainable ecological farming systems. Policy recommendations include protecting agricultural land, encouraging agroecological practices, alleviating labour shortages, and promoting multi-stakeholder engagement within policy development. Overall, this research enhances understanding of land-use dynamics and agricultural resilience in SIDS, offering practical insights for policymakers and practitioners working towards sustainable food systems amid spatial and climatic constraints. Full article

Community-managed forests within agroforestry landscapes are vital for both carbon sequestration and agricultural sustainability. This study assesses the Hariyali Community Forest (HCF) in western Nepal, emphasizing its role in carbon storage within a Sal (Shorea robusta)-dominated lowland forest containing diverse native and medicinal species. Stratified field inventories combined with satellite-derived biomass and land-use/land-cover data were used to quantify carbon stocks and spatial trends. In 2022, the mean aboveground carbon density was 165 tC ha⁻¹, totaling approximately 101,640 tC (~373,017 tCO₂e), which closely matches satellite-based trends and indicates consistent carbon accumulation. Remote sensing from 2015–2022 showed a net tree cover gain of 427 ha compared to a 2000 baseline of 188 ha, evidencing effective community-led regeneration. The 615 ha Sal-dominated landscape also sustains agroforestry, small-scale horticulture, and subsistence crops, integrating livelihoods with conservation. Temporary carbon declines between 2020 and 2022, linked to localized harvesting and management shifts, highlight the need for stronger governance and local capacity. This study, among the first integrated carbon assessments in Nepal’s lowland Sal forests, demonstrates how community forestry advances REDD+ (Reducing Emissions from Deforestation and Forest Degradation, and the role of conservation, sustainable management of forests, and enhancement of forest carbon stocks in developing countries) objectives while enhancing rural resilience. Linking field inventories with satellite-derived biomass and land-cover data situates community forestry within regional environmental change and SDG (Sustainable Development Goals) targets (13, 15, and 1) through measurable ecosystem restoration and livelihood gains. Full article

Land use change is a critical factor influencing regional carbon emissions, and understanding its spatiotemporal variability is essential for supporting science-based emission-reduction strategies. In this study, we constructed an improved measurement framework by integrating high-resolution land use data, gridded anthropogenic carbon emission data, multi-source remote sensing indicators, and socioeconomic variables to quantify land use carbon emissions (LUCEs) in the Chengdu–Chongqing Urban Agglomeration (CCUA) from 2000 to 2022. We analyzed the temporal trends and spatial clustering of carbon emissions using the Mann–Kendall (MK) trend test and global/local Moran’s I statistics, and further explored the driving mechanisms through the Geodetector (GD) model, including both single-factor explanatory power and two-factor interaction effects. The results show that total LUCEs in the CCEC increased continuously during the study period, with significant spatial clustering characterized by high–high emission hotspots in the core areas of Chengdu and Chongqing and low–low clusters in western mountainous regions. Socioeconomic factors played a dominant role in shaping emission patterns, with construction land proportion, nighttime light intensity, and population density identified as the strongest drivers. Interaction detection revealed nonlinear enhancement effects among key socioeconomic variables, indicating an increasing spatial lock-in of human activities on carbon emissions. These findings provide scientific evidence for optimizing land use structure and formulating region-specific low-carbon development policies in rapidly urbanizing megaregions. Full article

The advent of digital transformation, social learning, and the increasing use of artificial intelligence is driving requisite changes in the development of data centres, which are buildings designed to process and store data. Green innovation is an integral component of the sustainable development of data centre units. Solutions utilising green and blue infrastructure in data centres are being currently introduced with the objective of optimising energy consumption and reducing energy demand. The primary aim of the research is to analyse the utilisation of biomass production and blue–green infrastructure in data centres. The article provides a consolidated set of key performance indicators (KPIs): energy efficiency, water use, waste heat utilisation, renewable energy integration, hourly carbon-free matching, embodied carbon, and land use impacts, that can be used to compare different data centre designs. Traditional PUE-centric evaluations are broadened by added metrics such as biodiversity/green area, intensity, and 24/7 CFE, reflecting the broader, multi-dimensional sustainability challenges highlighted in the current literature. Twelve international case studies described in the literature were compared and the feasibility of the Polish pilot project in Michalowo was assessed to illustrate specific cases related to energy-saving solutions and the use of renewable energy sources in data centres. Full article

The SDG 15.3.1 framework provides a standardized approach using land use/land cover (LULC) change, land productivity, and soil organic carbon (SOC) dynamics to assess land degradation. However, SDG 15.3.1. faces limitations like coarse resolutions of Landsat-8 and Sentinel-2, particularly for fine-scale studies. Accordingly, this paper integrates Very Deep Super-Resolution (VDSR) for downscaling Landsat-8 imagery to 1 m resolution and the Vegetation Health Index (VHI) into SDG 15.3.1 to enhance detection in the heterogeneous Loiret region, France—a temperate agricultural hub featuring mixed croplands and peri-urban interfaces—using 2017 as baseline and 2024 as target. Results demonstrated that 1 m resolution detected more degraded LULC areas than coarser scales. SOC degradation was minimal (0.15%), concentrated in transitioned zones. VHI reduced overestimation of productivity declines compared to the Normalized Difference Vegetation Index by identifying more stable areas and 2.69 times less degradation in integrated assessments. The “One Out, All Out” rule classified 2.6% (using VHI) and 7.1% (using NDVI) of the region as degraded, mainly in peri-urban and cropland hotspots. This approach enables metre-scale land degradation mapping that remains effective in heterogeneous landscapes where fine-scale LULC changes drive degradation and would be missed at lower resolutions. However, future ground validation and longer timelines are essential to enhance the presented methodology. Full article

Many deep learning-based spatiotemporal fusion (STF) methods have been proven to achieve high accuracy and robustness. Due to the variable shapes and sizes of objects in remote sensing images, pyramid networks are generally introduced to extract multi-scale features. However, the down-sampling operation in the pyramid structure may lead to the loss of image detail information, affecting the model’s ability to reconstruct fine-grained targets. To address this issue, we propose a novel Dual-Stream Enhanced Pyramid based on Generative Adversarial Network (DSEPGAN) for the spatiotemporal fusion of remote sensing images. The network adopts a dual-stream architecture to separately process coarse and fine images, tailoring feature extraction to their respective characteristics: coarse images provide temporal dynamics, while fine images contain rich spatial details. A reversible feature transformation is embedded in the pyramid feature extraction stage to preserve high-frequency information, and a fusion module employing large-kernel and depthwise separable convolutions captures long-range dependencies across inputs. To further enhance realism and detail fidelity, adversarial training encourages the network to generate sharper and more visually convincing fusion results. The proposed DSEPGAN is compared with widely used and state-of-the-art STF models in three publicly available datasets. The results illustrate that DSEPGAN achieves superior performance across various evaluation metrics, highlighting its notable advantages for predicting seasonal variations in highly heterogeneous regions and abrupt changes in land use. Full article

Low clouds significantly influence Earth’s radiation budget, but their climate feedback remains highly uncertain due to complex interactions with meteorological conditions across spatial and temporal scales. The cloud controlling factor framework is widely used to link meteorological variables with cloud properties. However, most studies assume a static, linear relationship, potentially obscuring the timescale-dependent responses. In this study, we apply the Ensemble Empirical Mode Decomposition method to ISCCP-H cloud observations and ERA5 data (1987–2016) to isolate low cloud amount across multiple intrinsic timescales and trends over global land and ocean. The trends show a nonlinear increase in stratocumulus (Sc) and a significant nonlinear decline in cumulus (Cu), while stratus (St) exhibits weaker trends. We categorize timescales short (≤1 year) for annual variations, medium (1–8 years) for interannual variability such as ENSO, and long (>8 years) for decadal and longer-term climate changes. It is found that Sc and Cu over land are primarily influenced by near-surface heating, while sea surface temperature and surface sensible heat flux (SHF) dominate over ocean at short timescales. SHF becomes dominant over land at medium timescales, largely reflecting ENSO-related induced surface anomalies. At long timescales, atmospheric stability and wind speed influence continental clouds, while SHF remains dominant over ocean. Trend components reveal that Sc and Cu are most sensitive to temperature changes, whereas St responds to mid-level humidity over ocean and SHF over land. These findings underscore the importance of timescale-dependent cloud–meteorology relationships to improve cloud parameterizations and reduce climate projection uncertainties. Overall, our results demonstrate that low cloud variability and trends cannot be explained by a single linear mechanism but instead arise from distinct meteorological controls that change across timescales, cloud types, and surface regimes. Full article

As critical ecosystems, coastal zones necessitate the identification of their ecosystem service values, trade-off/synergy patterns, spatiotemporal evolution, and driving factors to inform scientific decision-making for sustainable ecosystem management. This study selected the coastal zone of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the research region. By incorporating land-use types such as mangroves, tidal flats, and aquaculture areas, we analyzed land-use changes in 1990, 2000, 2010, and 2020. The InVEST model was employed to quantify six key ecosystem services (ESs): annual water yield, urban stormwater retention, urban flood risk mitigation, soil conservation, coastal blue carbon storage, and habitat quality, while spatial correlations among them were examined. Furthermore, Spearman’s rank correlation coefficient was used to assess trade-offs and synergies between ecosystem services, and redundancy analysis (RDA) combined with the geographically and temporally weighted regression (GTWR) model were applied to identify driving factors and their spatial heterogeneity. The results indicate that: (1) Cultivated land, forest land, impervious surfaces, and water bodies exhibited the most significant changes over the 30-year period; (2) Synergies predominated among most ecosystem services, whereas habitat quality showed trade-offs with others; (3) Among natural drivers, the normalized difference vegetation index (NDVI, positive effect) and evapotranspiration were critical factors. The proportion of impervious surfaces served as a key land-use change driver, and the nighttime light index emerged as a primary socioeconomic factor (negative effect). The impacts of drivers on ecosystem services displayed notable spatial heterogeneity. These findings provide scientific support for managing the supply-demand balance of coastal ecosystem services, rational land development, and sustainable development. Full article

The study examines the recent transformation of traditional agricultural landscapes in the transition zone between Sierra de las Nieves and the Western Costa del Sol (Andalusia, Spain), one of the European regions where urban development pressure has reached its peak in replacing agricultural land with residential and tourism uses. Through a diachronic analysis of historical orthophotos (1956–2025), statistical sources (Agricultural Census, SIGPAC), and fieldwork, land-use changes and their impact on ecological functionality and territorial resilience are quantified. Results reveal a widespread loss of agricultural land—exceeding 68% overall—particularly severe in Benahavís, where more than 70% of farmland has disappeared, largely converted to urban areas. In contrast, in Istán and Ojén, the predominant trend is the renaturalization of abandoned agricultural land. These dynamics illustrate a coastal–inland territorial model that epitomizes the Mediterranean “territorial consumption” process, in which tourism-driven urbanization fragments traditional agroforestry mosaics. The study concludes that, despite their regression, traditional agricultural landscapes continue to play a key role in water regulation, soil conservation, and wildfire risk mitigation. Their restoration and maintenance are proposed as a replicable strategy for climate change adaptation and for reconciling territorial sustainability with economic development in other Mediterranean regions under intense urban pressure. Full article