Search Results (2,850)

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

Urban–rural integration realises the coordinated development and prosperity of urban and rural areas as a whole by optimising the allocation of resources and the flow of factors, and its connotations have been extended from a single dimension to multiple dimensions such as people, land and industry. The Beijing–Tianjin–Hebei Region has a typical “Core–Periphery Structure”, and this paper took the 187 county units within the region as the research object, taking into account indicators of development and coordination to construct an evaluation index system of urban–rural integration of the Beijing–Tianjin–Hebei region counties in the dimensions of “people–land–industry”. Global principal component analysis was used to measure the evolutionary pattern of the urban–rural integration level between 2005 and 2020, and its spatiotemporal drivers were analysed by using the Geographical and Temporal Weighted Regression model (GTWR). The results of the study show that (1) the level of urban–rural integration in the Beijing–Tianjin–Hebei region showed an increasing trend during the 15-year study period, the high-value areas of urban–rural integration were mainly distributed in Beijing and the Bohai Rim region in the eastern part of the Tianjin–Hebei region, and the level of urban–rural integration of the peri-urban county units of the city was better than that of the remote counties and cities as a whole. (2) In terms of spatial agglomeration, all dimensions were characterised by significant spatial agglomeration. The degree of agglomeration was categorised as urban–rural comprehensive integration (U-RCI) > urban–rural industry integration (U-RII) > urban–rural land integration (U-RLI) > urban–rural people integration (U-RPI). (3) In terms of spatial and temporal driving factors for urban–rural integration, the driving role of U-RPI, U-RLI and U-RII for U-RCI has gradually weakened during the past 15 years, and urban–rural integration in the counties shifted from a single role to a more central coordinated and multidimensional driving role. Full article

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. Full article

Planning conservation for multiple species while accounting for habitat availability and connectivity under uncertain land-use changes presents a major challenge. This study proposes a protocol to identify strategic conservation areas by assessing the functional connectivity of key bird species under future land-use scenarios in the Chaco Serrano of Córdoba, Argentina. We modeled three land-use scenarios for 2050: business as usual, sustainability, and intensification. Using the Equivalent Connected Area index, we evaluated functional connectivity for Chlorostilbon lucidus, Polioptila dumicola, Dryocopus schulzii, Milvago chimango, and Saltator aurantiirostris for 1989, 2019, and 2050, incorporating information about habitat specialization and dispersal capacity to reflect differences in ecological responses. All species showed declining connectivity from 1989 to 2019, with further losses expected under future scenarios. Connectivity declines varied by species and were not always proportional to habitat loss, highlighting the complex relationship between land-use change and functional connectivity. Surprisingly, the sustainability scenario led to the greatest losses in connectivity, emphasizing that habitat preservation alone does not ensure connectivity. Using the Integral Connectivity Index, we identified habitat patches critical for maintaining connectivity, particularly those vulnerable under the business as usual scenario. With a spatial prioritization analysis we identified priority conservation areas to support future landscape connectivity. These findings underscore the importance of multispecies, connectivity-based planning and offer a transferable framework applicable to other regions. Full article

The global population is increasing rapidly and, according to the United Nations (UN), it is expected to reach 9.8 billion by 2050. The demand for food is also increasing with a growing population. Food shortages, land scarcity, resource depletion, and climate change are significant issues raised due to an increasing population. Meat is a vital source of high-quality protein in the human diet, and addressing the sustainability of meat production is essential to ensuring long-term food security. To cover the meat demand of a growing population, meat scientists are working on several meat alternatives. Bacteria, fungi, yeast, and algae have been identified as sources of microbial proteins that are both effective and sustainable, making them suitable for use in the development of meat analogs. Unlike livestock farming, microbial proteins produce less environmental pollution, need less space and water, and contain all the necessary dietary components. This review examines the status and future of microbial proteins in regard to consolidating and stabilizing the global food system. This review explores the production methods, nutritional benefits, environmental impact, regulatory landscape, and consumer perception of microbial protein-based meat analogs. Additionally, this review highlights the importance of microbial proteins by elaborating on the connection between microbial protein-based meat analogs and multiple UN Sustainable Development Goals. Full article

Rapid socio-economic development and the impact of human activities have exerted tremendous pressure on the groundwater system of the Dawen River Basin (DRB), the largest tributary in the middle and lower reaches of the Yellow River. Hydrochemical studies on the DRB have largely centered on the upstream Muwen River catchment and downstream Dongping Lake, with some focusing solely on karst groundwater. Basin-wide evaluations suggest good overall groundwater quality, but moderate to severe contamination is confined to the lower Dongping Lake area. The hydrogeologically complex mid-reach, where the Muwen and Chaiwen rivers merge, warrants specific focus. This region, adjacent to populous areas and industrial/agricultural zones, features diverse aquifer systems, necessitating a thorough analysis of its hydrochemistry and origins. This study presents an integrated hydrochemical, isotopic investigation and EWQI evaluation of groundwater quality and formation mechanisms within the multiple groundwater types of the central DRB. Central DRB groundwater has a pH of 7.5–8.2 (avg. 7.8) and TDSs at 450–2420 mg/L (avg. 1075.4 mg/L) and is mainly brackish, with Ca²⁺ as the primary cation (68.3% of total cations) and SO₄²⁻ (33.6%) and NO₃⁻ (28.4%) as key anions. The Piper diagram reveals complex hydrochemical types, primarily HCO₃·SO₄-Ca and SO₄·Cl-Ca. Isotopic analysis (δ²H, δ¹⁸O) confirms atmospheric precipitation as the principal recharge source, with pore water showing evaporative enrichment due to shallow depths. The Gibbs diagram and ion ratios demonstrate that hydrochemistry is primarily controlled by silicate and carbonate weathering (especially calcite dissolution), active cation exchange, and anthropogenic influences. EWQI assessment (avg. 156.2) indicates generally “good” overall quality but significant spatial variability. Pore water exhibits the highest exceedance rates (50% > Class III), driven by nitrate pollution from intensive vegetable cultivation in eastern areas (Xiyangzhuang–Liangzhuang) and sulfate contamination from gypsum mining (Guojialou–Nanxiyao). Karst water (26.7% > Class III) shows localized pollution belts (Huafeng–Dongzhuang) linked to coal mining and industrial discharges. Compared to basin-wide studies suggesting good quality in mid-upper reaches, this intensive mid-reach sampling identifies critical localized pollution zones within an overall low-EWQI background. The findings highlight the necessity for aquifer-specific and land-use-targeted groundwater protection strategies in this hydrogeologically complex region. Full article

Investigating land cover patterns, changes in carbon stocks, and forecasting future conditions are essential for formulating regional sustainable development strategies and enhancing ecological and environmental quality. This study centers on Guiyang, a mountainous urban area in southwestern China, to analyze the dynamic changes in land cover and their effects on carbon stocks from 2000 to 2035. A carbon stocks assessment framework was developed using a cellular automaton-based artificial neural network model (CA-ANN), the InVEST model, and the geographical detector model to predict future land cover changes and identify the primary drivers of variations in carbon stocks. The results indicate that (1) from 2000 to 2020, impervious surfaces expanded significantly, increasing by 199.73 km². Compared to 2020, impervious surfaces are projected to increase by 1.06 km², 13.54 km², and 34.97 km² in 2025, 2030, and 2035, respectively, leading to further reductions in grassland and forest areas. (2) Over time, carbon stocks in Guiyang exhibited a general decreasing trend; spatially, carbon stocks were higher in the western and northern regions and lower in the central and southern regions. (3) The level of greenness, measured by the normalized vegetation index (NDVI), significantly influenced the spatial variation of carbon stocks in Guiyang. Changes in carbon stocks resulted from the combined effects of multiple factors, with the annual average temperature and NDVI being the most influential. These findings provide a scientific basis for advancing low-carbon development and constructing an ecological civilization in Guiyang. Full article

Given that the Sunkoshi River watershed (located in the southern foot of the Himalayas) is sensitive to climate change and its mountain ecosystem provides important services, we aim to evaluate its spatial and temporal variation patterns of vegetation, represented by the Normalized Difference Vegetation Index (NDVI), during 2000–2021 and identify the dominant driving factors of vegetation change. Based on the NDVI dataset (MOD13A1), we used the simple linear trend model, seasonal and trend decomposition using loess (STL) method, and Mann–Kendall test to investigate the spatiotemporal variation features of NDVI during 2000–2021 on multiple scales (annual, seasonal, monthly). We used the partial correlation coefficient (PCC) to quantify the response of the NDVI to land surface temperature (LST), precipitation, humidity, and soil moisture. The results indicate that the annual NDVI in 52.6% of the study area (with elevation of 1–3 km) increased significantly, while 0.9% of the study area (due to urbanization) degraded significantly during 2000–2021. Daytime LST dominates NDVI changes on spring, summer, and winter scales, while precipitation, soil moisture, and nighttime LST are the primary impact factors on annual NDVI changes. After removing the influence of soil moisture, the contributions of climate factors to NDVI change are enhanced. Precipitation shows a 3-month lag effect and a 5-month cumulative effect on the NDVI; both daytime LST and soil moisture have a 4-month lag effect on the NDVI; and humidity exhibits a 2-month cumulative effect on the NDVI. Overall, the study area turned green during 2000–2021. The dominant driving factors of NDVI change may vary on different time scales. The findings will be beneficial for climate change impact assessment on the regional eco-environment, and for integrated watershed management. Full article

Exploring the evolution of ecosystem services value (ESV) and its drivers is pivotal for optimizing the land-use structure and improving the value of ecosystem services. Using the 1980–2020 land-use/land-cover (LULC) dataset of the Mu Us Sandy Land, this study quantitatively evaluated ESV through LULC change, analyzing the spatiotemporal evolution characteristics of ESV and its driving forces. The results showed that (1) the LULC changes were stable from 1980 to 2020, and the ESV showed a slight downward trend in general. Grassland and water ecosystem services predominantly influenced ecosystem service function value fluctuations across the study area. (2) ESV demonstrated strong positive spatial autocorrelation, with high-value areas concentrated primarily in Red Alkali Nur, Dawa Nur, Batu Bay, and Ulanmulun Lake and low-value areas mainly distributed in unused land and certain agricultural zones. (3) The land-use degree and human activity intensity index were the main factors leading to the differentiation of ESV. The synergistic effects of human activities, landscape pattern changes, and natural factors led to the spatial differentiation of ESV in the study area. Beyond artificial ecological restoration projects, policies for ecosystem service management should pay more attention to the role of geodiversity in service provision. Full article

Synthetic aperture radar images can be used for ship target detection. However, due to the unclear ship outline in SAR images, noise and land background factors affect the difficulty and accuracy of ship (especially small target ship) detection. Therefore, based on the YOLOv5s model, this paper improves its backbone network and feature fusion network algorithm to improve the accuracy of ship detection target recognition. First, the LSKModule is used to improve the backbone network of YOLOv5s. By adaptively aggregating the features extracted by large-size convolution kernels to fully obtain context information, at the same time, key features are enhanced and noise interference is suppressed. Secondly, multiple Depthwise Separable Convolution layers are added to the SPPF (Spatial Pyramid Pooling-Fast) structure. Although a small number of parameters and calculations are introduced, features of different receptive fields can be extracted. Third, the feature fusion network of YOLOv5s is improved based on BIFPN, and the shallow feature map is used to optimize the small target detection performance. Finally, the CoordConv module is added before the detect head of YOLOv5, and two coordinate channels are added during the convolution operation to further improve the accuracy of target detection. The map50 of this method for the SSDD dataset and HRSID dataset reached 97.6% and 91.7%, respectively, and was compared with a variety of advanced target detection models. The results show that the detection accuracy of this method is higher than other similar target detection algorithms. Full article

This paper presents a novel design for precise vertical landing of drones based on the detection of three phase shifts in the range of ±180°. The design has three inputs to which the signal transmitted from an oscillator located at the landing point arrives with different delays. The circuit increases the aerial tracking volume relative to that achieved by detectors with theoretical unambiguous detection ranges of ±90°. The phase shift measurement circuit uses an analog phase detector (mixer), detecting a maximum range of ±90°and a double multiplication of the input signals, in phase and phase-shifted, without the need to fulfill the quadrature condition. The calibration procedure, phase detector curve modeling, and calculation of the input signal phase shift are significantly simplified by the use of an automatic gain control on each branch, dwhich keeps input amplitudes to the analog phase detectors constant. A simple program to determine phase shifts and guidance instructions is proposed, which could be integrated into the same flight control platform, thus avoiding the need to add additional processing components. A prototype has been manufactured in C band to explain the details of the procedure design. The circuit uses commercial circuits and microstrip technology, avoiding the crossing of lines by means of switches, which allows the design topology to be extrapolated to much higher frequencies. Calibration and measurements at 5.3 GHz show a dynamic range greater than 50 dB and a non-ambiguous detection range of ±180°. These specifications would allow one to track the drone during the landing maneuver in an inverted cone formed by a surface with an 11 m radius at 10 m high and the landing point, when 4 cm between RF inputs is considered. The errors of the phase shifts used in the landing maneuver are less than ±3°, which translates into 1.7% losses over the detector theoretical range in the worst case. The circuit has a frequency bandwidth of 4.8 GHz to 5.6 GHz, considering a 3 dB variation in the input power when the AGC is limiting the output signal to 0 dBm at the circuit reference point of each branch. In addition, the evolution of phases in the landing maneuver is shown by means of a small simulation program in which the drone trajectory is inside and outside the tracking range of ±180°. Full article

The demand for land monitoring information continues to increase, but the range and diversity of the available products to date have made their integrated use challenging and, at times, counterproductive. There has therefore been a growing need to enhance and harmonise the practice of land monitoring on a pan-European level with the formulation of a more consistent and standardised set of modelling criteria. The outcome has been a paradigm shift away from a “paper map”-based world where features are given a single, fixed label to one where features have a rich characterisation which is more informative, flexible and powerful. The approach allows the characteristics to be dynamic so that, over time, a feature may only change part of its description (i.e., a forest can be felled, but it may remain as forestry if replanted) or it can have multiple descriptors (i.e., a forest may be used for both timber production and recreation). The concept proposed by the authors has evolved since 2008 from first drafts to a comprehensive and powerful tool adopted by the European Union’s Copernicus programme. It provides for the semantic decomposition of existing nomenclatures, as well as supports a descriptive approach to the mapping of all landscape features in a flexible and object-oriented manner. In this way, the key move away from classification towards the characterisation of the Earth’s surface represents a novel and innovate approach to handling complex land surface information more suited to the age of distributed databases, cloud computing and object-oriented data modelling. In this paper, the motivation for and technical approach of the EAGLE concept with its matrix and UML model implementation are explained. This is followed by an update of the latest developments and the presentation of a number of experimental and operational use cases at national and European levels, and it then concludes with thoughts on the future outlook. Full article

As the cradle of Jiangnan culture, Suzhou is home to a dense concentration of historical architectural heritage that is currently facing existential threats from rapid urbanization. This study aims to develop a spatial heritage corridor network for conservation and sustainable utilization. Using kernel density estimation, this study identifies 15 kernel density groups, along with the Analytic Hierarchy Process (AHP), to pinpoint clusters of historical architectural heritage and assess the involved resistance factors. Current Effective Conductance (CEC) theory is further applied to model spatial flow relationships among heritage nodes, leading to the delineation of 27 heritage corridors and revealing a spatial structure characterized by one primary core, one secondary core, and multiple peripheral zones. Based on 15 source points, six cultural relics-themed routes are proposed—three land-based and three waterfront routes—connecting historical sites, towns, and ecological areas. The study further recommends a resource management strategy centered on departmental collaboration, digital integration, and community co-governance. By integrating historical architectural types, settlement forms, and ecological patterns, the research builds a multi-scale narrative and experience system that addresses fragmentation while improving coordination and sustainability. This framework delivers practical advice on heritage conservation and cultural tourism development in Suzhou and the broader Jiangnan region. Full article

Our study focused on identifying the evolution of soil erosion and its key drivers under multiple future scenarios in the Yiluo River Basin. Integrating the Universal Soil Loss Equation (USLE), future land use and vegetation cover simulation methods, and the Geodetector model, we analyzed historical soil erosion trends (2000–2020), projected future soil erosion risks under multiple Shared Socioeconomic Pathways (SSPs), and quantified the interactive effects of key driving factors. The results showed that soil erosion within the basin exhibited moderate intensity. Over the past 20 years, soil erosion decreased by 28.78%, with 76.29% of the area experiencing reduced erosion intensity. Future projections indicated an overall declining trend in soil erosion, showing reductions of 4.93–35.95% compared to baseline levels. However, heterogeneous patterns emerged across various scenarios, with the highest risk observed under SSP585. Land use type was identified as the core driving factor behind soil erosion (explanatory capacity q-value > 5%). Under diverse future climate scenarios, interactions between land use type and precipitation and temperature exhibited high sensitivity, highlighting the critical regulatory role of climate change in regulating erosion processes. This research provides a scientific foundation for the precise prevention and adaptive management of soil erosion in the Loess Plateau region. Full article

Climate change poses one of the greatest challenges of our time, with greenhouse gas (GHG) emissions significantly contributing to global warming. The agriculture, forestry, and land-use (AFOLU) sectors not only emit GHGs but also offer the potential for carbon sequestration, which can mitigate climate change. This study presents a methodological framework for estimating soil organic carbon (SOC) changes based on carbon farming practices in northern Lithuania. Using satellite-derived indicators of cover crops, no-till farming, and residue retention combined with soil and climate data, SOC dynamics were modeled across the Joniškis municipality for the period 2019–2020 using the Rothamsted Carbon Model (RothC) model. The integration of geospatial data and process-based modeling allowed for spatial estimation of SOC change, revealing positive trends ranging from 0.23 to 0.32 t C ha⁻¹ year⁻¹. Higher increases were observed in areas where multiple carbon farming practices overlapped. The proposed workflow demonstrates the potential of combining Earth observation and modeling approaches for regional-scale carbon assessment and provides a basis for future applications in sustainable land management and climate policy support. Full article