Search Results (895)

Management for pheasant shooting is a widespread land use in the UK, with potential implications for forest and hedgerow habitats. This study evaluates whether sites managed for pheasant shooting differ ecologically from similar sites not used for shooting. A systematic evidence evaluation of comparative studies was combined with a spatial analysis using remote sensing data (2010–2024). The literature review identified only 32 studies meeting strict criteria for comparability, revealing inconsistent and often weak evidence, with few studies reporting detailed forest management or statistically robust outcomes. While some studies noted increased or decreased biodiversity associated with pheasant shooting, the evidence base was generally of low quality. Remote sensing assessed forest structural and spectral diversity, intactness, and hedgerow density across 1131 pheasant-managed and 1131 matched control sites. Biodiversity data for birds, plants, and butterflies were sourced from GBIF records. Structural diversity and hedgerow density were significantly higher on pheasant-managed sites, while no significant differences were found in forest spectral diversity, intactness, or biodiversity indicators. Pheasant management may shape certain habitat features but has limited demonstrable effects on overall biodiversity. Further field-based, controlled studies are required to understand causal mechanisms and inform ecologically sustainable shooting practices. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. 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

When people overextend their step length, it leads to an increase in vertical movement and braking force. The overextension elevates landing impacts, which may increase pain in the knees or lower back. The objective of this study was to examine the effects of soft-landing walking techniques in a 90 min, instructor-led group class for older adults. To evaluate a landing impact, an accelerometer measurement system (Descente LTD., Tokyo, Japan) was used to measure a participant 10 meter (m) of walking. Assessment outcomes included the average number of steps, step length, upward acceleration which reflects the landing impact, and survey questions. A total of 223 older adults (31 men, 192 women, mean age 74.4 ± 5.7 years) completed the walking lesson. Following the lesson, participants decreased their step lengths and reduced upward acceleration, along with an increased step count. The number of steps increased, and a positive correlation (r = 0.73, p < 0.01) was observed between the rate of change in step length and upward acceleration. Over 95% of participants gave high marks for practicality and understanding the accelerometer measurements. The information derived from this study will provide valuable insight into the effectiveness of soft-landing techniques as a promotion of a healthy walking program for older adults. Full article

Accurate regional-scale mapping of soil organic matter (SOM) is crucial for land productivity management and global carbon pool monitoring. Current remote sensing inversion of SOM faces challenges, including the underutilization of temporal information and low feature selection efficiency. To address these limitations, this study developed an integrated framework combining multi-temporal Landsat imagery, field-measured SOM data, intelligent feature optimization, and machine learning. The framework employs two novel image-processing strategies: the Maximum Annual Bare-Soil Composite (MABSC) method to extract background spectral information and the Multi-temporal Feature Optimization Composite (MFOC) method to capture seasonal and environmental dynamics. These features, along with topographic covariates, were processed using an improved Feature-Optimized and Interpretable XGBoost (FOI-XGB) model for key variable selection and spatial mapping. Validation across two subtropical coastal mountainous regions at different scales in southeastern China demonstrated the framework’s effectiveness and robustness. Key findings include the following: (1) Both the MABSC-derived spectral bands and the MFOC-optimized indices significantly outperformed traditional single-season approaches. Their combined use achieved a moderate SOM inversion accuracy (R² = 0.42–0.44). (2) The FOI-XGB model substantially outperformed traditional feature selection methods (Pearson, SHAP, and CorrSHAP), achieving significant regional R² improvements ranging from 9.72% to 88.89%. (3) The optimal model integrating the MABSC-derived features, MFOC-optimized indices, and topographic covariates attained the highest accuracy (R² up to 0.51). This represents major improvements compared with using topographic covariates alone (R² increase of up to 160.11%) or the combined spectral features (MABSC + MFOC) alone (R² increase of up to 15.91%). This study provides a robust, scalable, and practical technical solution for accurate SOM mapping in complex environments, with significant implications for sustainable land management and carbon monitoring. Full article

Corporate environmental claims often neglect the substantial ecological impact of land-use changes. This case study examines the spatial dimension of retail-driven land-use transformation by analyzing supermarket expansion in the Veneto region (northern Italy), with a focus on a large grocery retailer. We evaluated its corporate environmental claims by assessing land consumption patterns from 1983 to 2024 using Geographic Information Systems (GIS). The GIS-based methodology involved geocoding 113 Points of Sale (POS—individual retail outlets), performing photo-interpretation of historical aerial imagery, and classifying land-cover types prior to construction. We applied spatial metrics such as total converted surface area, land-cover class frequency across eight categories (e.g., agricultural, herbaceous, arboreal), and the average linear distance between afforestation sites and POS developed on previously rural land. Our findings reveal that 65.97% of the total land converted for Points of Sale development occurred in rural areas, primarily agricultural and herbaceous lands. These landscapes play a critical role in supporting urban biodiversity and providing essential ecosystem services, which are increasingly threatened by unchecked land conversion. While the corporate sustainability reports and marketing strategies emphasize afforestation efforts under their “We Love Nature” initiative, our spatial analysis uncovers no evidence of actual land-use conversion. Additionally, reforestation activities are located an average of 40.75 km from converted sites, undermining their role as effective compensatory measures. These findings raise concerns about selective disclosure and greenwashing, driving the need for more comprehensive and transparent corporate sustainability reporting. The study argues for stronger policy frameworks to incentivize urban regeneration over greenfield development and calls for the integration of land-use data into corporate sustainability disclosures. By combining geospatial methods with content analysis, the research offers new insights into the intersection of land use, business practices, and environmental sustainability in climate-vulnerable regions. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

With the rapid advancement of urbanization in China, the demand for community-based elderly care facilities (CECFs) has been increasing. One pressing challenge is the question of how to provide CECFs that not only meet the health needs of the elderly but also make efficient use of limited urban land resources. This study addresses this issue by adopting an integrated multi-method research framework that combines multi-objective optimization (MOO) algorithms, Spearman rank correlation analysis, ensemble learning methods (Random Forest combined with SHapley Additive exPlanations (SHAP), where SHAP enhances the interpretability of ensemble models), and Self-Organizing Map (SOM) neural networks. This framework is employed to identify optimal building configurations and to examine how different architectural parameters influence key daylight performance indicators—Useful Daylight Illuminance (UDI) and Daylight Factor (DF). Results indicate that when UDI and DF meet the comfort thresholds for elderly users, the minimum building area can be controlled to as little as 351 m² and can achieve a balance between natural lighting and spatial efficiency. This ensures sufficient indoor daylight while mitigating excessive glare that could impair elderly vision. Significant correlations are observed between spatial form and daylight performance, with factors such as window-to-wall ratio (WWR) and wall thickness (WT) playing crucial roles. Specifically, wall thickness affects indoor daylight distribution by altering window depth and shading. Moreover, the ensemble learning models combined with SHAP analysis uncover nonlinear relationships between various architectural parameters and daylight performance. In addition, a decision support method based on SOM is proposed to replace the subjective decision-making process commonly found in traditional optimization frameworks. This method enables the visualization of a large Pareto solution set in a two-dimensional space, facilitating more informed and rational design decisions. Finally, the findings are translated into a set of practical design strategies for application in real-world projects. Full article

Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km² (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article

Land use and land cover (LULC) in Northern Bangladesh have undergone substantial transformations due to both anthropogenic and natural drivers. This study examines historical LULC changes (1990–2022) and projects future trends for 2030 and 2054 using remote sensing and the Cellular Automata-Artificial Neural Network (CA-ANN) model. Multi-temporal Landsat imagery was classified with 80.75–86.23% accuracy (Kappa: 0.75–0.81). Model validation comparing simulated and actual 2014 data yielded 79.98% accuracy, indicating a reasonably good performance given the region’s rapidly evolving and heterogeneous landscape. The results reveal a significant decline in waterbodies, which is projected to shrink by 34.4% by 2054, alongside a 1.21% reduction in cropland raising serious environmental and food security concerns. Vegetation, after an initial massive decrease (1990–2014), increased (2014–2022) due to different forms of agroforestry practices and is expected to increase by 4.64% by 2054. While the model demonstrated fair predictive power, its moderate accuracy highlights challenges in forecasting LULC in areas characterized by informal urbanization, seasonal land shifts, and riverbank erosion. These dynamics limit prediction reliability and reflect the region’s ecological vulnerability. The findings call for urgent policy action particularly afforestation, water resource management, and integrated land use planning to ensure environmental sustainability and resilience in this climate-sensitive area. Full article

In recent years, remote sensing imagery has become indispensable for applications such as environmental monitoring, land use classification, and urban planning. However, the physical constraints of satellite imaging systems frequently limit the spatial resolution of these images, impeding the extraction of fine-grained information critical to downstream tasks. Super-resolution (SR) techniques thus emerge as a pivotal solution to enhance the spatial fidelity of remote sensing images via computational approaches. While deep learning-based SR methods have advanced reconstruction accuracy, their high computational complexity and large parameter counts restrict practical deployment in real-world remote sensing scenarios—particularly on edge or low-power devices. To address this gap, we propose LSANet, a lightweight SR network customized for remote sensing imagery. The core of LSANet is the large separable kernel attention mechanism, which efficiently expands the receptive field while retaining low computational overhead. By integrating this mechanism into an enhanced residual feature distillation module, the network captures long-range dependencies more effectively than traditional shallow residual blocks. Additionally, a residual feature enhancement module, leveraging contrast-aware channel attention and hierarchical skip connections, strengthens the extraction and integration of multi-level discriminative features. This design preserves fine textures and ensures smooth information propagation across the network. Extensive experiments on public datasets such as UC Merced Land Use and NWPU-RESISC45 demonstrate LSANet’s competitive or superior performance compared to state-of-the-art methods. On the UC Merced Land Use dataset, LSANet achieves a PSNR of 34.33, outperforming the best-baseline HSENet with its PSNR of 34.23 by 0.1. For SSIM, LSANet reaches 0.9328, closely matching HSENet’s 0.9332 while demonstrating excellent metric-balancing performance. On the NWPU-RESISC45 dataset, LSANet attains a PSNR of 35.02, marking a significant improvement over prior methods, and an SSIM of 0.9305, maintaining strong competitiveness. These results, combined with the notable reduction in parameters and floating-point operations, highlight the superiority of LSANet in remote sensing image super-resolution tasks. Full article

The construction of a fence in the sea made of bamboo sticks along the coastal areas of Tangerang, Indonesia, caused controversy and many public debates in most Indonesian media. The case is, however, not unique. It provides a means to pose three questions related to the following topics: (1) which controversies and contradictions between formal procedures and informal practices related to land and sea rights exist; (2) which values and perceptions of the involved stakeholders play a role in these controversies and contradictions; and (3) which kinds of boundary work or boundary objects could resolve these controversies and contradictions. The theoretical embedding for the subject lies in the theories of territory and space on the one hand and formal institutional models of land and sea on the other. The analytical model used to evaluate the controversies and contradictions is McKinsey’s 7S model, while the data used are extracted from journalistic public media reports and social media. The results show a significant discrepancy between the values connected to formal and informal territorial claims, as well as a lack of enforcement capacity to address this discrepancy. Instead, the policy response exhibits an excessive and uncontrolled discretionary space for all stakeholders to pursue their own interests. The theoretical novelty is that institutional models governing territorial sea and land rights, restrictions and responsibilities need to be aligned and connected based on detecting where and how the values of affected stakeholders can be harmonized, rather than enforcing a unilateral system of values of disconnected systems (of either land or sea). The policy implementation implications are to create stricter procedural steps when providing building permits in coastal areas, with better enforcement and stricter control. Soft governance campaigns should raise awareness of what is allowed and required for coastal building permits and reclamations. Additionally, there could be quicker, more thorough inspections of emerging or hidden practices of non-approved fencing and non-approved occupation of coastal land and sea. Full article

Relying on information technologies such as the Internet, big data, and cloud computing, smart cities (SC) fully integrate urban resources, constantly strengthen the ability of urban economic systems, infrastructure systems, ecosystems, social systems, institutional systems, and other systems to withstand disaster disturbance and external risk shocks, and promote urban resilience (UR) construction. This study uses panel data from 254 prefecture-level cities in China from 2009 to 2021, and employs a multiperiod difference-in-differences method to examine the direct and heterogeneous effects of SC on UR. After a series of empirical tests, this study obtains the following results: (1) SC have a significant impact on the improvement of UR, which objectively demonstrates the reciprocity between SC and the level of UR construction, providing data support for promoting the in-depth practice of SC. (2) From the mechanism test of the impact of SC on UR, urban land green utilization efficiency and industrial structure transformation are intermediate mechanisms through which SC affect the improvement of UR. In addition, public environmental attention (PEA) has a positive regulatory effect on SC and UR, that is, PEA strengthens the role of SC in improving UR. (3) From the heterogeneity of urban characteristics that affect UR, SC have a more significant effect on improving UR in eastern cities and non-resource-based cities. This study provides new ideas for studying UR and provides useful insights for promoting SC construction and enhancing UR. This study proposes that the government should continue to promote the intelligent construction of Chinese cities, advance the industrial structure and improve the green land utilization efficiency, and strengthen their positive impact on UR. Full article

Remotely sensed cropland abandonment monitoring is crucial for providing spatially explicit references for maintaining sustainable agricultural practices and ensuring food security. However, abandoned cropland is commonly detected based on multi-date classification or the dynamics of a single vegetation index, with the interactions between vegetation and soil time series often being neglected, leading to a failure to understand its full-life-cycle succession processes. To fill this gap, we propose a new full-life-cycle modeling framework based on the interactive trajectories of vegetation–soil-related endmembers to identify abandoned and reclaimed cropland in Jinan from 2000 to 2022. In this framework, highly accurate annual fractional vegetation- and soil-related endmember time series are generated for Jinan City for the 2000–2022 period using spectral mixture models. These are then used to integrally reconstruct temporal trajectories for complex scenarios (e.g., abandonment, weed invasion, reclamation, and fallow) using logistic and double-logistic models. The parameters of the optimization model (fitting type, change magnitude, start timing, and change duration) are subsequently integrated to develop a rule-based hierarchical identification scheme for cropland abandonment based on these complex scenarios. After applying this scheme, we observed a significant decline in green vegetation (a slope of −0.40% per year) and an increase in the soil fraction (a rate of 0.53% per year). These pathways are mostly linked to a duration between 8 and 15 years, with the beginning of the change trend around 2010. Finally, the results show that our framework can effectively separate abandoned cropland from reclamation dynamics and other classes with satisfactory precision, as indicated by an overall accuracy of 86.02%. Compared to the traditional yearly land cover-based approach (with an overall accuracy of 77.39%), this algorithm can overcome the propagation of classification errors (with product accuracy from 74.47% to 85.11%), especially in terms of improving the ability to capture changes at finer spatial scales. Furthermore, it also provides a better understanding of the whole abandonment process under the influence of multi-factor interactions in the context of specific climatic backgrounds and human disturbances, thus helping to inform adaptive abandonment management and sustainable agricultural policies. Full article

This article examines the land-use politics of recreation development in rural China. Extending the lens of spatial informality, it analyzes how the appropriation and acquisition of space by small suburban leisure enterprises have constituted a de facto vehicle for rural spatial reconfiguration amidst land-use constraints. Drawing on ethnographic fieldwork and case studies, we illuminate emerging scenarios in which inbound businesses burgeoned through the production of informal spaces, which were subsequently formalized or tolerated by local governments geared towards social economic growth. More so, we reveal the potential and limitations of such an informal-to-formal approach for rural spatial reconfiguration by showing how its sustainability and survival depend upon the enterprises’ ability to enter into a tacit alliance of interests with local authorities. This article casts new light on emerging bottom-up processes of spatial reconfiguration, alongside its repercussions for local suburbs, in the development of rural tourism and suburban leisure. It further suggests that, as an analytical approach, a nuanced understanding of rural restructuring under the recent national rural revitalization strategy can benefit from moving beyond the sole emphasis on formal institutions to analyze the role played by ordinary market actors and their spatial practices that shape rural territories and spatial relationships. Full article