Search Results (1,372)

Northeastern Brazil (NEB) has a high potential for wind energy generation, making it a strategic area for the development of this renewable source. However, the region’s complex wind regime, driven by interactions between large-scale atmospheric systems, local circulations, and coastal topography, presents significant challenges for weather forecasting and wind energy applications. Despite this, detailed assessments of forecast performance using mesoscale models remain limited. The main objective was to develop an efficient strategy that enables satisfactory results by optimizing data assimilation, land use and topography information as well as improvements in physical parameterizations and post-processing, optimizing computational effort. Forecasting conducted during the year 2020 were validated with data from 20 anemometric measurement towers (AMTs), located at strategic points across various wind power complexes. The model’s performance was evaluated using statistical metrics such as MBE, MAE, nRMSE, standard deviation ratio, and correlation. Additionally, the impact of bias removal was assessed using two approaches: one that eliminates the mean error per forecasted time step and another employing artificial intelligence for bias removal training. The results revealed distinct characteristics for each analyzed location, with errors of diverse nature due to the local nuances of the measurements. However, both bias removal approaches showed significant improvements in wind characterization across all complexes. Full article

Arid inland basins represent critical hotspots of intensified conflict among water resources, ecological integrity, and economic development on a global scale. The coevolution of groundwater systems and land use patterns plays a pivotal role in shaping regional sustainability trajectories. This study synthesizes multi-source data spanning 2000 to 2020 from the Wuwei Basin, located within the Shiyang River watershed in China, to elucidate the synergistic dynamics between hydrological and land use transformations. Key findings reveal: (1) Around 2010, a significant structural shift in land use occurred, transitioning from production-oriented expansion to ecologically driven priorities. This shift was characterized by a reduction in cultivated land, increased utilization of artificial surfaces, and accelerated ecological restoration efforts. These changes were jointly influenced by enhanced water governance frameworks and spatial planning policies. (2) Groundwater levels exhibit marked spatial variability. While stability is maintained in piedmont and discharge zones, persistent overdraft has led to pronounced declines in transitional and distal recharge areas. This heterogeneity is primarily governed by the interplay of hydrogeological factors—such as recharge capacity and aquifer permeability—and anthropogenic pressures, including the extent of cultivated land and intensity of groundwater extraction. Notably, these patterns cannot be explained solely by the proportion of cultivated land or total extraction volumes. (3) A positive feedback mechanism—termed the “gain-loss regime shift”—has been identified in the discharge zone, where simultaneous increases in groundwater extraction and water-level recovery are observed. However, human activities have disrupted the natural coupling between precipitation and groundwater recharge, resulting in a significant attenuation of recharge rates (exceeding 80%). These findings offer a robust scientific basis for implementing spatially differentiated water resource management strategies and optimizing land use in arid basin environments. The implications extend beyond regional contexts, contributing to broader efforts in harmonizing human–environment interactions globally. Full article

Among the Central Asian republics, Uzbekistan is unique in that approximately 80% of its territory lies within a plain, characterized by an arid geographic zone and dry climate. Agricultural production in these regions is possible only through artificial irrigation. In recent years, global climate change and challenges related to transboundary water use have led to a reduction in water availability. The average annual water allocation to Uzbekistan is estimated at 51–53 billion m³, of which 90–91% is consumed by the agricultural sector. Due to the uneven distribution of water resources and the complex topography of irrigated lands, water supply is supported by numerous pumping stations operated by the state, water users associations, farms, and clusters. Additionally, well-based pumping systems are employed to maintain groundwater levels and ensure irrigation. On average, these facilities consume around 8.0 billion kWh of electricity annually. The agricultural sector faces several critical challenges, including crop water deficits caused by water shortages, slow adoption of water-saving technologies, and limited implementation of drip irrigation on household plots, dachas, and greenhouses that play a key role in food supply. Moreover, the delivery of water to fertile lands situated far from main power lines and water sources remains problematic. This article aims to explore the integration of solar energy solutions to support drip irrigation in both large-scale agricultural lands (ω = 1.0–100.0 ha and above) and small-scale areas such as homestead plots, dachas, and greenhouses (ω = 0.01–1.0 ha), as well as their application in small- to medium-sized pumping stations. Based on the research and experimental design work carried out, three mobile photovoltaic units—MPPU-8-500-4000, MPPU-2-550-1100, and MPPU-4-500-2000—were developed and implemented to address water and energy shortages in agriculture. Full article

The karst ecologically fragile region of northern Guangxi faces dual pressures from wetland shrinkage and landscape functional degradation driven by rapid urbanisation. The mechanisms governing its multi-scale landscape pattern evolution and the dominance of disturbances require urgent clarification. This study integrates land use data from 1980 to 2020, employing ArcGIS 10.8 analysis, Fragstats landscape indices, and optimal parameter geographic detectors to construct a ‘pattern-process-driver’ interpretative framework in northern Guangxi. It quantitatively reveals the evolution characteristics and driving mechanisms of wetland landscape patterns in northern Guangxi, thereby optimising wetland ecological conservation pathways. Results indicate the following: (1) Between 1980 and 2020, total wetland area decreased by 65.58 km², exhibiting a ‘structural substitution’ trend characterised by natural wetland decline and artificial wetland expansion. (2) Wetland landscape patterns exhibited intensified fragmentation and increased structural complexity. (3) Wetland evolution was primarily driven by annual mean temperature, GDP, and annual mean precipitation, reflecting a composite mechanism characterised by climate dominance, economic pressure, and policy failure. Specifically, the increase in temperature is the main reason for the decrease in natural wetlands, while economic growth dominates the expansion of artificial wetlands. This study provides scientific basis for karst wetland ecological restoration and differentiated territorial spatial planning, offering reference for ecological and environmental governance in karst watersheds. Full article

The intensification of agricultural land use (A-LUI) is a central driver of global environmental change, affecting soil health, water quality, biodiversity, and greenhouse gas balances. Monitoring A-LUI remains challenging because it is shaped by multiple management practices, ecological processes, and spatio-temporal dynamics. This review provides a comprehensive synthesis of existing definitions and standards of A-LUI at national and international levels (FAO, OECD, World Bank, EUROSTAT) and evaluates in situ methods alongside the rapidly expanding potential of remote sensing (RS). We introduce a novel RS-based taxonomy of A-LUI indicators, structured into five complementary categories: trait, genesis, structural, taxonomic, and functional indicators. Numerous examples illustrate how traits and management practices can be translated into RS proxies and linked to intensity signals, while highlighting key challenges such as sensor limitations, cultivar variability, and confounding environmental factors. We further propose an integrative framework that connects management practices, plant and soil traits, RS observables, validation needs, and policy relevance. Emerging technologies—such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration—are discussed as promising pathways to advance the monitoring of A-LUI across scales. By compiling and structuring RS-derived indicators, this review establishes a conceptual and methodological foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, thereby supporting both scientific progress and evidence-based agricultural policy. Full article

Continuous urban land development is causing environmental changes. The most visible effects are a decline in biodiversity, an increase in urban temperatures, and changes in the water balance. Recently, very intense and sudden rainfall events have been observed, and existing drainage systems are not effective enough. Urban surfaces tend to be impermeable with low retention, so there is no way to respond to both the rainy periods and the drought periods that often follow. A good remedy for these factors is urban greening, which can be achieved through the design of green roofs and living walls. The substrate used for this type of construction should be light, permeable, and retentive. This study aimed to produce artificial aggregate granules with various additives that modify the structure to create open mesopores and facilitate better rainwater management. Through suitable sintering, materials with water absorption of more than 40%, retention in simulated rainfall of over 35% and a bulk density of ~0.70 g/cm³ were obtained. Detailed microstructural analyses were carried out using various microscopic techniques. Strength tests and simple vegetation tests were also carried out. Full article

Trakošćan Lake is an artificial lake created in the mid-19th century for aesthetic and economic purposes. The area around the lake has been protected as park forest. Recently, the lake has become the most famous example of eutrophication in Croatia, as by 2022, a significant amount of sediment had accumulated in it. Therefore, the lake was drained that same year, followed by mechanical removal of the sediment. The total amount of sediment removed was 204,000 m³. After the removal work, a particularly important question arose of what to do with such a large amount of sediment. The objective of this research was to gain specific insight into the chemical composition of the sediment with the aim of its possible use in agricultural production for increasing the quality of arable land. A comprehensive qualitative geochemical and agrochemical analysis of the sediment composition was carried out for the first time, including indicators of the pH value, amount of organic matter and carbon, total nitrogen, available phosphorus and potassium, amount of carbonates, and the presence of metals, metalloids, and non-metals, of which As, Cd, Hg, and Pb are toxic. Electrochemical, spectrophotometric, and titration methods were used, along with three atomic absorption spectrometry techniques. The results of the analyses were interpreted in comparison with the natural substrate, as well as with the current regulations for agricultural land in the Republic of Croatia. According to this, sediment is not harmful for the environment. Full article

Google Earth Engine (GEE) has become one of the most widely used platforms for Land Use and Land Cover (LULC) research, offering cloud-based access to petabyte-scale datasets and scalable analytical tools. While earlier reviews provided valuable overviews of data and applications, this study synthesizes 72 selected articles published between 2016 and February 2025 to examine the evolution of GEE–LULC research. Results show exponential growth in publications, with Landsat and Sentinel imagery dominating datasets and Random Forest (RF) and Support Vector Machine (SVM) remaining the most common classifiers. Geographically, output is concentrated in China and India, reflecting regional leadership in GEE adoption. Despite its strengths, GEE faces persistent challenges, including memory limits, restricted support for advanced Deep Learning (DL), and reliance on labeled data. Promising directions include integrating few-shot semantic segmentation and hybrid workflows combining GEE scalability with local Graphics Processing Unit (GPU) computing. By bridging platform-focused and application-focused studies, this review provides a comprehensive synthesis of GEE–LULC research and outlines actionable pathways for advancing scalable and Artificial Intelligence (AI)-enabled geospatial analysis. Full article

Background: In recent decades, malaria vector species distribution and insecticide resistance have taken new colonization steps across Africa. Understanding the malaria vector insecticide resistance status, blood meal source, and species composition is of paramount importance in designing evidence-based vector control strategies. This study assessed the blood meal sources, sporozoite (infectivity) rate, and knockdown resistance allele’s frequency in female Anopheles arabiensis in chosen villages of Jabi Tehnan District, Northwest Ethiopia. Methods: The host-seeking and resting Anopheles gambiae s.l. were collected using human landing catches (HLCs), CDC light traps (CDC-LTs), pyrethrum spray catches (PSCs), and pit shelters (PSs) both indoors and outdoors. The analysis of both blood meal sources and circumsporozoite proteins was performed using enzyme-linked immunosorbent assay (ELISA). The detection of knockdown resistance gene mutations and species identification were conducted using a polymerase chain reaction (PCR). Results: A total of 5098 female Anopheles gambiae s.l. were collected. Of these, 1690 (33.2%) were collected from HLCs, 1423 (27.9%) from CDC light traps, 1635 (32.0%) from PSCs, and only 350 (6.9%) from pit shelters (PSs). Of these, 57.2% (n = 2915) female Anopheles mosquitoes were collected indoors using CDC light traps (CDC-LTs), human landing catches (HLCs), and pyrethrum spray catches (PSCs), while 38.2% (n = 2183) were collected outdoors using human landing collection (HLC), CDC light traps (CDC-LTs), and artificial pit shelters (PSs). Molecular identification to the species level showed that among the 530 An. gambiae s.l. samples analyzed using PCR, 96.03% (509) were An. arabiensis, and 3.97% (21) were unidentified species. The biting peak was found to be from 22:00 to 00:00 h for An. arabiensis. However, their activity decreased sharply after 23:00 to 00:00 h. The distribution of knockdown resistance genes in the tested specimens of An. arabiensis consisted of 1.4% (n = 3) heterozygous resistant (RS), 17.9% (n = 38) homozygous resistant (RR), and 80.7% (n = 171) homozygous susceptible (SS) genotypes. A higher proportion of Anopheles mosquitoes analyzed for blood meal analysis had a human blood meal origin at 13.1% (n = 47), followed by bovine at 8.9% (n = 32) and mixed at 5.8% (n = 21). Conclusions: The dominant malaria vector species was Anopheles arabiensis in the study area with a higher human blood meal origin. The Kdr gene was confirmed in the tested An. arabiensis, indicating that an alternative insecticide class should be used in the study area. Full article

Shipborne fighters play a critical role in far-sea operations. However, their landing process on aircraft carrier decks involves significant risks, where accidents can lead to substantial losses. Timely and accurate risk prediction is, therefore, essential for improving flight training efficiency and enhancing the combat capability of naval aviation forces. Machine-learning algorithms have been explored for predicting landing risks in land-based aircraft. However, owing to the challenges in acquiring relevant data, the application of such methods to shipborne aircraft remains limited. To address this gap, the present study proposes a deep learning-based method for predicting landing risks of shipborne aircraft. A dataset was constructed using simulated ship movements recorded during the sliding phase along with relevant flight parameters. Model training and prediction were conducted using up to ten different input combinations with artificial neural networks, long short-term memory, and transformer neural networks. Experimental results demonstrate that all three models can effectively predict landing parameters, with the lowest average test error reaching 3.5620. The study offers a comprehensive comparison of traditional machine learning and deep learning methods, providing practical insights into input variable selection and model performance evaluation. Although deep learning models, particularly the Transformer, achieved the highest accuracy, in practical applications, the support of hardware performance still needs to be fully considered. Full article

Soil aggregate stability plays a central role in mediating slope erosion, a key ecological process in North China. This study aimed to investigate how aggregate structures (reflected by rainfall intensity and vegetation-type differences) influence the erosion process. Using wasteland as the control, we conducted artificial simulated rainfall experiments on soils covered by Quercus variabilis, Platycladus orientalis, and shrubs, with three rainfall intensity gradients. Key findings showed that Platycladus orientalis exhibited the strongest infiltration capacity and longest runoff initiation delay due to its high proportion of stable macroaggregates (>0.25 mm), while barren land readily formed surface crusts, leading to the fastest runoff. Increased rainfall intensity significantly exacerbated runoff and erosion. When the macroaggregate content exceeded 60%, sediment yield rates dropped sharply, with a significant negative exponential relationship between the mean weight diameter (MWD) and sediment yield; barren land (dominated by microaggregates) faced the highest erosion risk and fell into an erosion–fragmentation vicious cycle. Redundancy analysis revealed that microbial communities (e.g., Ascomycota) and fine roots were dominant erosion-controlling factors under heavy rainfall. Ultimately, the synergistic system of the macroaggregate architecture and root-microbial cementation enabled Platycladus orientalis and other tree stands to reduce soil erodibility via maintaining aggregate stability, whereas shrubs and barren land amplified rainfall intensity effects. barren landbarren landmm·h⁻¹ mm·h⁻¹ mm·h⁻¹ barren land. Full article

Beaches are important geomorphic units shaped by land–sea interactions. Changes in their profiles and surface sediments are directly influenced by both natural processes and human activities. This study is based on continuous topographic and sediment monitoring from 2021 to 2023 on the open and sheltered beaches of Puqian Bay, Hainan Island. It investigates the interannual profile evolution and the spatiotemporal response of sediment grain size under the influence of an artificial island. The results show that the Guilinyang Beach profile is mainly characterized by seasonal erosion–accretion cycles and the seaward migration of sandbars, while the Hilton Beach profile has undergone long-term erosion. At Hilton, sediment grain size changes are strongly coupled with profile erosion and accretion. Seasonal waves drive spatial differences in both profile and grain-size variation across Puqian Bay. The artificial island has reshaped local alongshore sediment transport and wave energy distribution. This has led to continuous erosion and coarsening in the open sector, while the sheltered sector remains morphologically stable. These findings reveal the spatiotemporal response patterns of headland-bay beaches under both natural and anthropogenic forcing, and provide scientific evidence for understanding coastal sediment dynamics and the impacts of artificial structures. Full article

The study of tropical landscape dynamics is of critical importance, particularly within protected areas, for evaluating ecosystem functioning and the effectiveness of natural conservation efforts. This study aims to identify landscape dynamics within the Dong Nai Biosphere Reserve (including Cat Tien National Park and the Ma Da Forest) using remote sensing (Landsat and others) and geographic information system methods. The analysis is based on changes in the Enhanced Vegetation Index (EVI), land cover transformations, landscape metrics (Class area, Percentage of Landscape and others), and natural landscape fragmentation, as well as a spatio-temporal assessment of anthropogenic impacts on the area. The results revealed structural changes in the landscapes of the Dong Nai Biosphere Reserve between 2000 and 2024. According to Sen’s slope estimates, a generally EVI growth was observed in both the core and buffer zones of the reserve. This trend was evident in forested areas as well as in regions of the buffer zone that were previously occupied by highly productive agricultural land. An analysis of Environmental Systems Research Institute (ESRI) Land Cover and Land Cover Climate Change Initiative (CCI) data confirms the relative stability of land cover in the core zone, while anthropogenic pressure has increased due to the expansion of agricultural lands, mosaic landscapes, and urban development. The calculation of landscape metrics revealed the growing isolation of natural forests and the dominance of artificial plantations, forming transitional zones between natural and anthropogenically modified landscapes. The human disturbance index, calculated for the years 2000 and 2024, shows only a slight change in the average value across the territory. However, the coefficient of variation increased significantly by 2024, indicating a localized rise in anthropogenic pressure within the buffer zone, while a reduction was observed in the core zone. The practical significance of the results obtained lies in the possibility of their use for the management of the Dongnai biosphere Reserve based on a differentiated approach: for the core and the buffer zone. There should be a ban on agriculture and development in the core zone, and restrictions on urbanized areas in the buffer zone. Full article

The accelerated global urbanization process has positioned land use/land cover change modeling as a critical component of contemporary geographic science and urban planning research. Traditional approaches face substantial challenges when addressing urban system complexity, multiscale spatial interactions, and high-dimensional data associations, creating urgent demand for sophisticated analytical frameworks. This review comprehensively evaluates machine learning applications in land use prediction through systematic analysis of 74 publications spanning 2020–2024, establishing a taxonomic framework distinguishing traditional machine learning, deep learning, and hybrid methodologies. The review contributes a comprehensive methodological assessment identifying algorithmic evolution patterns and performance benchmarks across diverse geographic contexts. Traditional methods demonstrate sustained reliability, while deep learning architectures excel in complex pattern recognition. Most significantly, hybrid methodologies have emerged as the dominant paradigm through algorithmic complementarity, consistently outperforming single-algorithm implementations. However, contemporary applications face critical constraints including computational complexity, scalability limitations, and interpretability issues impeding practical adoption. This review advances the field by synthesizing fragmented knowledge into a coherent framework and identifying research trajectories toward integrated intelligent systems with explainable artificial intelligence. Full article