Search Results (1,653)

The settlements of the Great Wall are the product of the overlap of ancient Chinese agricultural civilization and non-agricultural civilization. The structure of the settlement system is of great value for understanding the law of defense engineering and social spatial organization. The Great Wall, built by a non-agricultural civilization, is an important part of the development history of the Chinese civilization. Its uniqueness reflects the relationship between institution and space. However, the archaeological remains and related research methods for non-agricultural Great Wall settlements are not perfect. This paper takes the typical case of the Great Wall built by a non-agricultural civilization (Linhuang Lu settlements of the Jin Great Wall) as the object and integrates spatial analysis and social network analysis. It aims to explore the structure of the settlement system. The settlements of Linhuang Lu show non-random distribution characteristics. They can be divided into four levels. The number ratio from high-level to low-level settlements is 70:30:10:1. Through the weighted Voronoi and social network analysis of human connection and geographical connection, this paper clarifies the structural characteristics of spatial association and social association of settlements. Combined with accessibility and geographical environment, the Linhuang Lu settlements were finally divided into 10 Meng’an defense units and 12 Mouke defense units. Quantitative analysis of the settlement system structure shows the hierarchical management of nature and military by non-agricultural civilization. This provides an empirical basis for the reconstruction of the military defense system of the Great Wall of the Jin Dynasty and further explores the applicability of the research paradigm. This paper has methodological innovation value for solving the problem of spatial cognition of settlement heritage. Full article

This study aims to investigate a fundamental yet largely overlooked question: “Does investing in transportation infrastructure positively impact farms’ agricultural gross income?” It is examined based on the role of transportation infrastructure in ensuring equal access to market opportunities in the context of the widening regional economic disparity in Korea. The main novelty of this study lies in its attempt to introduce an accessibility measure for evaluating the benefits of transportation infrastructure in a rural setting, which has been limitedly applied in urban-centered studies. To accomplish this task, multilevel and spatial econometric models were employed to evaluate the ex-post impact of transportation accessibility on agricultural gross income from the perspectives of farmers, primarily, and rural autonomies, subsequently. This study found that the continuation of the current direction of transportation policy—without substantial consideration for agriculture as an industry and rural areas as living spaces—can intensify the economic alienation of agriculture and rural areas. This study concludes that opportunities for market access provided by the immense public investments in transportation infrastructure should be fairly distributed to farmers and rural autonomies to promote balanced regional development in Korea. Full article

Peanut cultivation currently plays a minor role in Portuguese agriculture, despite the country’s favorable soil and climatic conditions. In the Azores archipelago, where agriculture is a key economic activity, peanut production has recently sparked interest among rural producers. Weeds pose a major threat to crop development, particularly for short-cycle species like peanuts. This study aimed to determine the period prior to weed interference (PPI) in peanut crops under two row spacings (40 cm and 60 cm) on São Miguel Island, Azores. Eight treatments were established—0–15, 0–30, 0–45, 0–60, 0–75, 0–90 days after emergence (DAE), full-season coexistence, and a weed-free control—to represent increasing periods of weed competition. A randomized block design with four replicates was used for each spacing. The weed community included eight species, with Cyperus spp., Digitaria spp., Amaranthus blitum, and Portulaca oleracea being the most prevalent. Weed interference throughout the entire cycle led to yield losses exceeding 81% and 86% at 40 cm and 60 cm row spacings, respectively. The PPI was defined at a 5% yield reduction threshold, which is a commonly accepted benchmark in weed science to determine the beginning of the critical period of weed interference. Full article

The Chengdu–Chongqing Economic Mega Region (CCEMR), as a strategic economic hub in Western China, is increasingly facing challenges in balancing urban growth, agricultural stability, and ecological conservation within its territorial spatial planning framework. This study addresses the critical need to integrate multidimensional resilience assessment into China’s territorial spatial planning system. A framework for functional resilience assessment was developed through integrated GIS spatial analysis, with three resilience dimensions explicitly aligned to China’s “Three Zones and Three Lines” (referring to urban, agricultural, and ecological space and spatial control lines) territorial planning system: urban resilience was evaluated using KL-TOPSIS ranking, where weights were derived from combined Delphi expert consultation and AHP; agricultural resilience was quantified through the entropy method for weight determination and GIS raster calculation; and ecological resilience was assessed via a Risk–Recovery–Potential (RRP) model integrating Ecosystem Risk, Recovery Capacity (ERC), and Service Value (ESV) metrics, implemented through GIS spatial analysis and raster operations. Significant spatial disparities emerge, with only 1.29% of CCEMR exhibiting high resilience (concentrated in integrated urban–ecological zones like Chengdu). Rural and mountainous areas demonstrate moderate-to-low resilience due to resource constraints, creating misalignments between resilience patterns and current territorial spatial zoning schemes. These findings provide scientific evidence for optimizing the delineation of the Three Major Spatial Patterns: urbanized areas, major agricultural production zones, and ecological functional zones. In this research, a transformative methodology is established for translating resilience diagnostics directly into territorial spatial planning protocols. By bridging functional resilience assessment with statutory zoning systems, this methodology enables the following: (1) data-driven resilience construction for the Three Major Spatial Patterns (urbanized areas, major agricultural production zones, and ecological functional zones); (2) strategic infrastructure prioritization; and (3) enhanced cross-jurisdictional coordination mechanisms. The framework positions spatial planning as a proactive tool for adaptive territorial governance without requiring plan revision. Full article

Modern agriculture is based on plant specialization, where the decrease in biodiversity makes vulnerable of our cultivated crops against climate change and the fluctuated market demands. Mixed cropping is a planned diversity in space, which is a powerful tool to conserving soil fertility. Our experiment was carried out in three growing seasons (2020/2021, 2021/2022, 2022/2023) with the combination of three winter wheat varieties (GK Szilárd, Cellule, GK Csillag) and a winter pea variety (Aviron) in four repeats and three seeding rates to determine yield and quality parameters (protein, gluten, Zeleny index, W-value) of wheat. Yield varied every year: The highest value was provided by the 50:100 mixture of GK Csillag/Aviron in 2021 (pure stands + 24%), then it gradually decreased in the following years. In terms of protein, Zeleny index and W-value, the Cellule/Aviron 50:100 achieved outstanding values, while in 2022 was preferred GK Szilárd/Aviron 50:100 combination. We verified a statistical difference between the wheat varieties in the case of gluten (in each year) in favor of GK Csillag/Cellule/GK Csillag and for the W-value (in 2021) in favor of Cellule. Plant density and seeding rate determined the final crop proportion within the mixture and indirectly affected yield quantity and quality. Full article

Drought is a complex and impactful natural hazard, with sometimes catastrophic impacts on small or subsistence agriculture and water security. In Pacific Island countries, there lacks an agreed approach for monitoring agricultural drought hazard with satellite-derived remote sensing data. This study addresses this gap through a framework for agricultural drought monitoring in the Pacific using freely available space-based observations. Applying World Meteorological Organization’s (WMO) recommendations and a set of objective selection criteria, three remotely sensed drought indicators were chosen and combined using fuzzy logic to form a composite drought hazard index: the Standardised Precipitation Index, Soil Water Index, and Normalised Difference Vegetation Index. Each indicator represents a subsequential flow-on effect of drought on agriculture. The index classes geographic areas as low, medium, high, or very high levels of drought hazard. To test the drought hazard index, two case studies for drought in the western Pacific, Papua New Guinea (PNG), and Vanuatu, are assessed for the 2015–2016 El Niño-related drought. Findings showed that at the height of the drought in October 2015, 58% of PNG and 72% of Vanuatu showed very high drought hazard, compared to 6% and 40%, respectively, at the beginning of the drought. The hazard levels calculated were consistent with conditions observed and events that were reported during the emergency drought period. Application of this framework to operational drought monitoring will promote adaptive capacity and improve resilience to future droughts for Pacific communities. Full article

Optimizing the regional spatial pattern of land use and high-quality economic development requires an accurate understanding of the multifunctional evolution of land use. Based on remote sensing data and socio-economic data from 2000 to 2023, this study utilizes a land transfer matrix, an evaluation index system, an obstacle degree model, and regression analysis to deeply explore the spatial distribution characteristics and influencing factors of the production–living–ecological functions (PLEF) in the Yangtze River Economic Belt (YREB) over the 23-year period. The results show the following: ① the living function area of the YREB has increased by 22,400 km², while the production function area has decreased by 20,600 km², and the ecological function area has decreased by 1800 km². ② The production and living function spaces are characterized by high values in the eastern region and low values in the western region, and the ecological function space is characterized by high values in the western region and low values in the eastern region. ③ In the YREB, production function was the main obstacle to the PLEF between 2000 and 2023. ④ Population growth, economic development, agricultural technology, and agricultural efficiency are the main factors that influence the spatial and temporal evolution of the PLEF. This study suggests exploring an interactive compensation mechanism of the PLEF that combines the government and the market to form a differentiated development strategy. Full article

Urban community gardens (UCGs) have been expanding globally. Initially created to provide fresh, organic produce for low-income populations, UCGs have evolved into models of sustainable agriculture with increasing economic significance. Beyond their economic role, UCGs serve as vital social spaces and may be categorized as third places—informal gathering spaces that foster social connections and promote well-being. This study analyzes and compares the impact of UCGs as third places in Toronto and São Paulo, focusing on their contributions to social cohesion, financial resilience, environmental sustainability, cultural transmission, and mental well-being. It is a review-based study utilizing publicly available data from policy documents, the academic literature, and official websites. Although the practice of community gardening has a long-standing history, the concept of gardens as third places is relatively recent, emerging in the late 1980s. In recent decades, there has been growing interest in their association not only with aesthetic and functional benefits but also with health, well-being, and social connection. UCGs are valuable not only for food production but also for fostering social interaction, preserving cultural practices, and promoting overall well-being. Cities must develop policies that strengthen community resilience by recognizing and supporting UCGs as essential third places. Full article

This study developed a deep learning–based water level recognition model using Closed-Circuit Television (CCTV) footage. The model focuses on real-time water level recognition in agricultural reservoirs that lack automated water level gauges, with the potential for future extension to flood forecasting applications. Video data collected over approximately two years at the Myeonggyeong Reservoir in Chungcheongbuk-do, South Korea, were utilized. A semantic segmentation approach using the U-Net model was employed to extract water surface areas, followed by the classification of water levels using Convolutional Neural Network (CNN), ResNet, and EfficientNet models. To improve learning efficiency, water level intervals were defined using both equal spacing and the Jenks natural breaks classification method. Among the models, EfficientNet achieved the highest performance with an accuracy of approximately 99%, while ResNet also demonstrated stable learning outcomes. In contrast, CNN showed faster initial convergence but lower accuracy in classifying complex intervals. This study confirms the feasibility of applying vision-based water level prediction technology to flood-prone agricultural reservoirs. Future work will focus on enhancing system performance through low-light video correction, multi-sensor integration, and model optimization using AutoML, thereby contributing to the development of an intelligent, flood-resilient water resource management system. Full article

Non-urbanized areas (NUAs), including residual urban green areas, urban parks, agricultural, natural and semi-natural areas, are a fundamental part of the green infrastructure. They are essential in sustaining life and future development, providing a series of ecosystem services (ESs) vital to human society. However, the rapid expansion of urban areas has led to a significant reduction in green spaces. Land-take, reducing available land resources, impacts ecosystem functionality, making it crucial to preserve high-quality territories and the relative ESs provided. In this context, the aim of this study was to evaluate the reduction in ESs due to the land-take having occurred in the last 20 years in the Province of Monza–Brianza, the Italian province with the highest land-take. To achieve this goal, authors used the official data of land use/cover of the Lombardy Region, with three time thresholds (T0: 1999–2003, T1: 2012–2013, T2: 2021) and applied a methodology for ESs assessment originally developed for the municipal level, adapting it to the provincial scale. The study analyzes trends in land-take and land-use changes and assesses how these changes have led to variations in ES provision. The approach involves calculating multiple indices reflecting different ESs provided by NUAs: provisioning ESs coming from agriculture, regulating ESs provided by natural resources, cultural ESs provided by landscape. Findings reveal that urban expansion has decreased provisioning ESs coming from agriculture, while ESs provided by landscape and natural resources have remained stable or improved, respectively. The natural quality index has improved due to conservation policies, despite the high land-take recorded. Anyway, although regional policies have mitigated some negative effects, the overall reduction in green spaces remains a critical issue. Full article

Evaluating the restoration quality of university outdoor spaces is often constrained by subjective surveys and manual assessment, limiting scalability and objectivity. This study addresses this gap by applying explainable machine learning to predict restorative quality from campus imagery, enabling large-scale, data-driven evaluation and capturing complex nonlinear relationships that traditional methods may overlook. Using Fujian Agriculture and Forestry University as a case study, this study extracted road network data, generated 297 coordinates at 50-m intervals, and collected 1197 images. Surveys were conducted to obtain restorative quality scores. The Mask2Former model was used to extract landscape features, and decision tree algorithms (RF, XGBoost, GBR) were selected based on MAE, MSE, and EVS metrics. The combination of optimal algorithms and SHAP was employed to predict restoration quality and identify key features. This research also used a multivariate linear regression model to identify features with significant statistical impact but lower features importance ranking. Finally, the study also analyzed heterogeneity in scores for three restoration indicators and five campus zones using k-means clustering. Empirical results show that natural elements like vegetation and water positively affect psychological perception, while structural components like walls and fences have negative or nonlinear effects. On this basis, this study proposes spatial optimization strategies for different campus areas, offering a foundation for creating high-quality outdoor environments with restorative and social functions. Full article

Renewable Energy Communities (RECs) are central to Europe’s strategy for reducing greenhouse gas emissions and advancing a sustainable, decentralized energy system. RECs aim to transform consumers into prosumers—individuals who both produce and consume energy—thereby enhancing energy efficiency, local autonomy, and citizen engagement. This study introduces a novel Geographic Information System (GIS)-based methodology that integrates socio-economic and spatial data to support the design of optimal REC configurations. QGIS 3.40.9 “Batislava” tool is used to simulate site-specific energy distribution scenarios, enabling data-driven planning. By combining a Composite Energy Vulnerability Index (CEVI), Rooftop Solar Potential (RSP), and the distribution of urban gardens (UGs), the approach identifies priority urban zones for intervention. Urban gardens offer multifunctional public spaces that can support renewable infrastructures while fostering local resilience and energy equity. Applied to the city of Rome, the methodology provides a replicable framework to guide REC deployment in vulnerable urban contexts. The results demonstrate that 11 of the 18 highest-priority areas already host urban gardens, highlighting their potential as catalysts for collective PV systems and social engagement. The proposed model advances sustainability objectives by integrating environmental, social, and spatial dimensions—positioning RECs and urban agriculture as synergistic tools for inclusive energy transition and climate change mitigation. Full article

As cities grow faster and food systems grow more fragile, architects and planners are increasingly challenged to design spaces that not only house people but also support environmental and social well-being. This study investigates how vertical farming can be integrated into residential building facades in Egypt as a strategy to promote local food production and sustainable design. Focusing on a government housing project in Port Said, three façade-based design options were developed and assessed through structured surveys targeting two stakeholder groups: experts and residents. This research revealed a strong interest and awareness across both samples. While users prioritized benefits such as esthetics, air quality, and the ease of use, experts emphasized feasibility concerns, maintenance needs, and policy barriers. Both groups favored the second design option as the most balanced and applicable solution. By foregrounding stakeholder input, this study fills a gap in the existing literature on building-integrated agriculture and provides design and policy recommendations grounded in the local context. It advocates for inclusive design thinking, where technical viability and community values are considered together. While limited to single case and visual assessment methods, this research offers a foundation for further applied studies and broader sustainable design frameworks. Full article

Point clouds from 3D sensors such as LiDAR are increasingly used in agriculture for tasks like crop characterisation, pest detection, and leaf area estimation. While traditional point cloud processing typically occurs in Cartesian space using methods such as principal component analysis (PCA), this paper introduces a novel frequency-domain approach for point cloud registration. The central idea is that point clouds can be transformed and analysed in the spectral domain, where key frequency components capture the most informative spatial structures. By selecting and registering only the dominant frequencies, our method achieves significant reductions in localisation error and computational complexity. We validate this approach using public datasets and compare it with standard Iterative Closest Point (ICP) techniques. Our method, which applies ICP only to points in selected frequency bands, reduces localisation error from 4.37 m to 1.22 m (MSE), an improvement of approximately 72%. These findings highlight the potential of frequency-domain analysis as a powerful and efficient tool for point cloud registration in agricultural and other GNSS-challenged environments. Full article

Tea is a prominent cash crop in global agriculture, and it is Sri Lanka’s top agricultural export known as ‘Ceylon Tea,’ employing nearly one million people, with land covering an area of 267,000 ha. However, over the past decade, many tea lands in Sri Lanka have been abandoned, leading to a gradual decline in production. This research aims to identify, map, and verify tea land abandonment over time and space by identifying and analyzing a series of land use trajectories with Landsat, Google Earth, and PlanetScope imageries to provide a substantial knowledge base. The study area covers five Divisional Secretariats Divisions in Kandy District, Central Highlands of Sri Lanka: Delthota, Doluwa, Udapalatha, Ganga Ihala Korale, and Pasbage Korale, where around 70% of the tea lands in Kandy District are covered. Six land use/cover (LULC) classes were considered: tea, Home Garden and Other Crop, forest, grass and bare land, built-up area, and Water Body. Abandoned tea lands were identified if the tea land was converted to another land use between 2015 and 2023. The results revealed the following: (1) 85% accuracy in LULC classification, revealing tea as the second-largest land use. Home Garden and Other Crop dominated, with an expanding built-up area. (2) The top 22 trajectories dominating the tea trajectories were identified, indicating that tea abandonment peaked between 2017 and 2023. (3) In total, 12% (5457 ha) of pixels were identified as abandoned tea lands during the observation period (2015–2023) at an accuracy rate of 94.7% in the validation. Significant changes were observed between the two urban centers of Gampola and Nawalapitiya towns. (4) Tea land abandonment over 7 years was the highest at 35% (1892.3 ha), while 5-year and 3-year periods accounted for 535.4 ha and 353.6 ha, respectively, highlighting a significant long-term trend. (5) The predominant conversion observed is the shift in tea towards Home Garden and Other Crop (2986.2 ha) during the timeframe. The findings underscore the extent and dynamics of tea land abandonment, providing critical insights into the patterns and characteristics of abandoned lands. This study fills a key research gap by offering a comprehensive spatial analysis of tea land abandonment in Sri Lanka. The results are valuable for stakeholders in the tea industry, providing essential information for sustainable management, policy-making, and future research on the spatial factors driving tea land abandonment. Full article