Search Results (6,500)

Land use and land cover (LULC) changes are of vital significance in fields such as environmental impact assessment and natural disaster monitoring. This study, through an analysis of 1432 papers over the past decade employing quantitative, qualitative, bibliometric analysis, and knowledge graph techniques, aims to assess the evolution and current landscape of deep learning (DL) in LULC. The focus areas are: (1) trend analysis of the number and annual citations of published articles, (2) identification of leading institutions, countries/regions, and publication sources, (3) exploration of scientific collaborations among major institutions and countries/regions, and (4) examination of key research themes and their development trends. From 2013 to 2023 there was a substantial surge in the application of DL in LULC, with China standing out as the principal contributor. Notably, international cooperation, particularly between China and the USA, saw a significant increase. Furthermore, the study elucidates the challenges concerning sample data and models in the application of DL to LULC, providing insights that could guide future research directions to accelerate progress in this domain. Full article

Design guidelines and design review panel (DRPs) are crucial components in ensuring consistent and high-quality design within masterplan sites. This study focuses on New Zealand, examining the challenges faced by industry partners in adapting to master-planned design guidelines. The case study centres on Hobsonville Point, the largest government-led masterplan community in New Zealand, which promotes sustainable and higher-density living—an uncommon concept in the country. We conducted interviews with 18 key stakeholders involved in the development of Hobsonville Point. The results show that the DRP provides flexible judgments and insightful information that better accommodate individual development characteristics than the strict numerical standards specified in design guidelines. This study underscores the importance of the DRP in producing good design outcomes, especially when they are involved at the very beginning of the design process. Additionally, we took note of stakeholder concerns about DRP members’ expertise and the possible effects of high turnover rates on the design process. To strive for continual improvement, future empirical studies on the DRP process are encouraged to enhance proficiency and dependability. Design guidance should be prioritised to ensure that climate-related design is implemented to promote sustainable urban development. Full article

In recent years, smart city construction has brought significant social and economic impacts to emerging economies, especially in narrowing the urban–rural gap. However, there is relatively little empirical research on this aspect. We take China as an example for research. This study uses a Spatial Multi-period Difference-in-Differences (DID) approach to investigate the relationship between smart city construction and urban–rural collaborative development in Chinese cities, taking digital infrastructure as the mechanism variable. Our findings reveal that smart city construction significantly promotes urban–rural collaborative development and exhibits positive spatial externalities. These results remain robust after parallel trend tests, placebo checks, and controlling for other policy interferences. Further analysis suggests that this effect operates largely through the reinforcement of digital infrastructure, whereby the smart city initiatives enhance connectivity and interaction between urban and rural areas, fostering collaborative development. Moreover, the efficacy of smart city policies is found to be particularly prominent in cities with strong economic development, weak levels of urban–rural collaborative development, and high degrees of digitization. By illuminating the role of smart city construction in propelling urban–rural collaborative development, this study provides valuable insights for policymakers. Full article

This paper explores the relationship between high-speed rail (HSR) and industrial agglomeration within urban agglomerations. The paper selects the data of the Beijing–Tianjin–Hebei Urban Agglomeration (BJHUA) and Central Plains Urban Agglomeration (CPUA) from 2002 to 2016 as the research object. The time-varying difference-in-difference (TVDID) model is innovatively applied to analyze the impact of HSR on the agglomeration of secondary and tertiary industries in urban agglomerations, and the industrial agglomeration effects of the two urban agglomerations are compared. The results show that the influence of high-speed railways on the industrial agglomeration of urban agglomerations is heterogeneous. In the BJHUA, the impact of HSR on the agglomeration of secondary and tertiary industries is not particularly significant. On the other hand, in the CPUA, HSR does not have a significant impact on the agglomeration of secondary industry. However, it does have a significant negative effect on the agglomeration of tertiary industry. In addition, further analysis reveals significant variations in the impact of HSR on the agglomeration of industries within urban agglomerations after excluding the central cities. It is important to note that the impact of HSR on regional industries can be complex and multifaceted. The findings enrich the theoretical understanding of the relationship between HSR and industrial agglomeration. Full article

Although the realm of sustainable development has been sharply associated with energy savings and a decrease in contamination, the concept deserves a much more comprehensive approach. Sustainable development, as the only respectful and safe way to progress, involves—besides the mentioned rational use of energy and resources—a broad variety of economic, technical, cultural, and behavioral issues. In this context, the total or partial recovery of abandoned singular infrastructure facilities for alternative uses (leisure time activities for human welfare) is to be considered an original, interesting, and efficient approach. In this research, the so-called “El Caminito del Rey” [The King’s Little Path] will be studied as a paradigmatic model of successful achievement. The path is located in Málaga, South of Spain, and was originally built to provide access to a hydroelectric power plant. Due to the construction of new routes, the track and walkways were progressively abandoned. The recovery enterprise was conceived in the framework of circular economy-based planning and with full respect to environmental requirements. The facts and figures that will be presented and analyzed will show that when the project is thoroughly designed and carefully implemented, the rehabilitation of obsolete infrastructure facilities located in natural spaces is suitable, feasible, and profitable. Prudent and rational use of these assets for alternative purposes (human welfare through rural tourism and open-air activities) may be fruitful not only in economic terms but also in environmental, cultural, and social ones. The main objective of the current work is to demonstrate that obsolete abandoned facilities can be rehabilitated into worthwhile amenities that may turn into key factors for the sustainable development of determined rural areas. Moreover, the “El Caminito del Rey” experience could represent a model for similar development projects. Full article

Understanding the thermal effects of different urban patterns that constitute today’s urban landscapes is critical to the development of urban resilience to climate change. This article aims to assess the efficiency of urban green spaces in thermal regulation. Through thermography, we explored the interaction between air temperature and the spatial components within these environments. Through comparative analysis involving a UAV, we studied the relationship between air temperatures at varying altitudes and the temperature within tree canopies. The results revealed significant differences in the thermal distribution between impervious urban areas with buildings and green spaces. These findings provide important information for assessing thermal comfort and the efficiency of urban green spaces in mitigating the impact of extreme heat events. During the summer months, green spaces, due to shade and the enhanced absorption of solar radiation by trees, exhibited lower temperatures compared to impervious areas. However, in winter, urban areas displayed higher temperatures, attributable to their heat retention capacity. This study contributes to the existing knowledge base by providing an in-depth examination of the thermal efficiency of urban green spaces across different layers of their lower atmosphere. Our results underscore the crucial role of tree cover in thermal comfort regulation, offering valuable information for sustainable urban planning. These insights are particularly relevant for the design of more comfortable and resilient environments in response to climatic variations and for the crafting of a tree-planting strategy in Mediterranean climate cities, an area where the impacts of climate change are becoming increasingly apparent. Full article

The concept of soil health is increasingly being used as an indicator for sustainable soil management and even includes legislative actions. Current applications of soil health often lack geospatial and monetary analyses of damages (e.g., land development), which can degrade soil health through loss of carbon (C) and productive soils. This study aims to evaluate the damages to soil health (e.g., soil C, the primary soil health indicator) attributed to land developments within the state of Illinois (IL) in the United States of America (USA). All land developments in IL can be associated with damages to soil health, with 13,361.0 km² developed, resulting in midpoint losses of 2.5 × 10¹¹ of total soil carbon (TSC) and a midpoint social cost of carbon dioxide emissions (SC-CO₂) of $41.8B (where B = billion = 10⁹, USD). More recently developed land area (721.8 km²) between 2001 and 2016 likely caused the midpoint loss of 1.6 × 10¹⁰ kg of TSC and a corresponding midpoint of $2.7B in SC-CO₂. New developments occurred adjacent to current urban areas near the capital cities of Springfield, Chicago, and St. Louis (the border city between the states of Missouri and IL). Results of this study reveal several types of damage to soil health from developments: soil C loss, associated “realized” soil C social costs (SC-CO₂), and loss of soil C sequestration potential from developments. The innovation of this study has several aspects. Geospatial analysis of land cover combined with corresponding soil types can identify changes in the soil health continuum at the landscape level. Because soil C is a primary soil health indicator, land conversions caused by developments reduce soil health and the availability of productive soils for agriculture, forestry, and C sequestration. Current IL soil health legislation can benefit from this landscape level data on soil C loss with GHG emissions and associated SC-CO₂ costs by providing insight into the soil health continuum and its dynamics. These techniques and data can also be used to expand IL’s GHG emissions reduction efforts from being solely focused on the energy sector to include soil-based emissions from developments. Current soil health legislation does not recognize that soil’s health is harmed by disturbance from land developments and that this disturbance results in GHG emissions. Soil health programs could be broadened to encourage less disturbance of soil types that release high levels of GHG and set binding targets based on losses in the soil health continuum. Full article

The black soil region of Northeast China is one of the most fertile soil areas in the world and serves as a crucial grain-producing region in China. However, excessive development and improper utilization have led to severe land use issues. Conducting land cover extraction in this region can provide essential data support for monitoring and managing natural resources effectively. This article utilizes GF-6 remote sensing imagery as the data source and adopts the U-Net model as the backbone network. By incorporating residual modules and adjusting the convolution kernel size, a high-precision land cover extraction model called RAT-UNet is developed. Taking Qiqihar City as an example, the RAT-UNet model is applied to extract land cover information. The results are as follows: (1) The RAT-UNet model achieves high accuracy in land cover extraction, with the following accuracies for different land types: cropland (95.11%), forestland (93.61%), grassland (68.41%), water bodies (94.67%), residential land (89.40%), and unused land (87.25%). (2) The land cover extraction performance of the RAT-UNet model is superior to DeepLabV3, U-Net, SegNet, and LinkNet34 models. This research outcome provides methodological support for the intelligent and high-precision extraction of land cover information and also offers timely data for Qiqihar city’s land use planning. Full article

Tropical Indian river basins are well-known for high and low discharges with high peaks of flood during the summer and the rest of the year, respectively. A high intensity of rainfall due to cyclonic and monsoon winds have caused the tropical Indian rivers to witness more runoff. These rivers are also known for carrying a significant amount of sediment load. The complex and non-linear nature of the sediment yield and runoff processes and the variability of these processes depend on precipitation patterns and river basin characteristics. There are a number of other elements that make it difficult to forecast with great precision. The present study attempts to model rainfall–runoff–sediment yield with the help of five machine learning (ML) algorithms—support vector regression (SVR), artificial neural network (ANN) with Elman network, artificial neural network with multilayer perceptron network, adaptive neuro-fuzzy inference system (ANFIS), and local linear regression, which are useful in river basins with scarce hydrological data. Daily, weekly, and monthly runoff and sediment yield (SY) time series of Vamsadhara river basin, India for a period from 1 June to 31 October for the years 1984 to 1995 were simulated using models based on these multiple machine learning algorithms. Simulated results were tested and compared by means of three evaluation criteria, namely Pearson correlation coefficient, Nash–Sutcliffe efficiency, and the difference of slope. The results suggested that daily and weekly predictions of runoff based on all the models can be successfully employed together with precipitation observations to predict future sediment yield in the study basin. The models prepared in the present study can be helpful in providing essential insight to the erosion–deposition dynamics of the river basin. Full article

Many studies quantify the impact of climate change and human activities on runoff changes on an annual scale, but few studies have examined this on multiple time scales. This paper quantifies the contribution of different factors to the variability of Jinsha River runoff at multiple time scales (annual, seasonal and monthly). First, the trend analysis of Jinsha River runoff is carried out, and the Mann–Kendall mutation test was then applied to the runoff data for mutation analysis. According to the mutation year, the research period is divided into the base period and the mutation period. By constructing an ABCD hydrological model simulation and monthly scale Budyko model, the contribution rate of human and climate factors to the multitime-scale runoff of Jinsha River is calculated. The results showed that: (1) The sudden year of change in the Jinsha River runoff is 1978, and the Nash coefficients of the ABCD hydrological model in the base period and sudden change period were 0.85 and 0.86, respectively. (2) Climate factors were the dominant factor affecting annual runoff changes (98.62%), while human factors were the secondary factor affecting annual runoff changes (1.38%). (3) The contribution rates of climate factors in spring, summer, autumn, and winter to runoff were 91.68%, 74.08%, 95.30%, and 96.15%, respectively. The contribution rates of human factors in spring, summer, autumn, and winter to runoff were 8.32%, 25.92%, 4.70%, and 3.85%, respectively. (4) The contribution rates of climate factors to runoff in May, June, and July were 95.14%, 102.15%, and 87.79%, respectively. The contribution rates of human factors to runoff in May, June, and July were 4.86%, −2.15%, and 12.21%, respectively. Full article

Vigorous emphasis has been placed on optimizing land spatial planning to protect carbon storage and enhance ecosystem resilience. What is the effectiveness of the Major Function-Oriented Zone (MFOZ) planning implemented to achieve this goal in China? Especially in urbanized areas where there are more pronounced conflicts between humans and land. Taking the Beijing-Tianjin-Hebei (BTH) urban agglomeration as the target area, this study explored the response of carbon storage to land use/cover change (LUCC) and its vulnerability to ecological service functions under MFOZ planning. The 30 m × 30 m spatially resolved Landsat TM/ETM remote sensing images from 2000 to 2020 were used. The data preprocessing was performed mainly through radiometric calibration, clipping, and reclassification through the ArcGIS 10.7 software. Applying the InVEST model, which uses the LUCC map and carbon storage density of the four carbon pools, including above-ground carbon density, below-ground carbon density, dead organic carbon density, and soil organic carbon density, to evaluate the carbon storage under the current landscape or in the future, the results show that: (1) The BTH ecosystem experienced a carbon storage reduction of about 7.25 × 10⁷ Mg from 2000 to 2020 due to the expansion of construction land, which crowded out cropland. Carbon storage in the BTH showed a high concentration in the “northeast-southwest” direction and a tiny distribution in the “middle-east” direction. (2) From 2015, the initial effects of the MFOZ planning were seen, with the ecological land in the Central Core Zone and Eastern Coastal Development Zone decreasing while the proportion of high-carbon storage areas in the Eastern Coastal Development Zone increasing. (3) Over the two decades, the land use intensity index improved by 4.65 overall, and vulnerability worsened from 2000 to 2015 and was alleviated from 2015 to 2020. This study will provide a scientific reference for optimizing urban spatial land use planning and promoting carbon sequestration in ecosystems. Full article

Non-grain production of cultivated land (NGPCL) is an essential factor that affects food security and sustainable agricultural development. Due to the large population and limited land area present in China, cultivated land resources are more extensive and valuable in hilly and mountainous areas, though the expansion of NGPCL threatens food security and is detrimental to the sustainable use of cultivated land resources. To better understand the problem of NGPCL in hilly and mountainous areas, this paper initially classifies NGPCL into four distinct types, namely unplanted cultivated land (UCL), planted non-grain crops (PNGC), engineering recoverable (ENR), and immediately recoverable (IMR), based on their planting types and attributes. Subsequently, we analyzed the spatial patterns and differentiation characteristics of these NGPCL types in Le’an County at the village scale through exploratory spatial data analysis and studied their driving factors using the geographical detector model. The findings show that the NGPCL rate in Le’an County was 18.53%, with ENR occupying the largest area, followed by PNGC and UCL, while IMR had the smallest area. The spatial distribution of NGPCL in Le’an County suggests that there are correlations with all four types of NGPCL, which exhibit spatial clustering, except for IMR, which displays spatial heterogeneity. We attribute the phenomenon of NGPCL in Le’an County to natural, social, and economic factors, with the driving forces having varying degrees of influence. Specifically, slope, altitude, and cultivated land protection intensity play significant roles in the overall NGPCL. This paper is of crucial significance to the local agricultural management department’s efforts to prevent and control the non-grain production of cultivated land and the Chinese Government’s efforts to ensure food security. Full article

Exploring the spatial pattern and development strategies of urbanization from the perspective of the multi-dimensional coordination of population, economy, and land is the key to solving the problems of the urban–rural gap and human–land contradiction. This paper analyzed the spatial agglomeration of population, economy, and construction land area growth rates and explored their coordinated development in Chinese prefecture-level cities from 2010 to 2020 by using the spatial autocorrelation model, elasticity coefficient model, and coupling coordination model. The results are as follows: (1) China’s population, economy, and construction land area were all growing, with the highest economic growth and the lowest population growth, and most prefecture-level cities in central and northeastern China had negative population growth. (2) The growth rates of the population, economy, and construction land in Chinese prefecture-level cities had significant positive spatial clustering characteristics; the spatial agglomeration of the economy was the most prominent and the high-value areas were mainly concentrated in western China. (3) The elasticity coefficients between the population, economy, and construction land in most Chinese prefecture-level cities indicate uneven development of urbanization, manifested as population growth lagging behind construction land expansion and further lagging behind economic development. (4) More than 56% of Chinese prefecture-level cities have uncoordinated development among the population, economy, and construction land mainly distributed in northeast China and central China. The results can provide references and decision-making support for promoting the sustainable development of China’s new urbanization. Full article

The urban land administration system (LAS) of any country serves as a key pillar for good governance, resource planning, service delivery, infrastructure development, and revenue collection. To reform their LASs, countries need a thorough understanding of their existing context and global relevance. The goal of this paper is to examine the status and challenges of urban LASs in Pakistan using the United Nations Framework for Effective Land Administration (FELA). The exploratory case study method used in the paper employs a mixed approach, which includes FELA-based questionnaire surveys, group discussions, and desk reviews. A total of 525 urban LAS stakeholders, including owner-buyers, real estate agents, bankers, lawyers, and LAS organizations, participated in the activity. The results show that more than half of the stakeholders are not satisfied with existing urban LASs, their governance and accountability, laws, and policies. Corruption is prevalent mostly in government organizations. Fraud and joint ownership are the most common sources of dispute, with 67 percent of the respondents stating that the cases take more than two years to resolve in court. The financial aspect of urban LASs is suffering due to property undervaluation and low revenue collection. Manual data and record keeping in LASs further complicate the system, with 87 percent of all respondents interested in innovating the urban LAS using modern technologies. Furthermore, 92 percent of all respondents expressed the need to standardize the existing LASs. There is a lack of capacity and skills, and 89 percent of organizations’ respondents believe that human resources skilled in Geographical Information Systems (GIS) and Remote Sensing (RS) can improve the efficiency of urban LASs. There is a lack of partnership among LAS organizations and a gap in the accessibility of LAS-related quality information. The country’s vision of building smart cities can be realized through LAS standardization and 3D and GIS innovation. Full article

The composition of urban forests (UFs) exhibits homogenization among cities compared with rural forests (RFs) among different climate zones. However, the degree of homogenization in subtropical UFs and its difference from RFs remain unclear. In this study, we examined tree species composition and diversity in UFs in 19 cities in China’s subtropical zone with precipitation ranging from 458 to 1852 mm and compared them with RFs. We found that (1) the species composition similarity, Jaccard index (J~0.27), between UFs was significantly higher than that (J~0.15) of RFs, indicating biotic homogenization; (2) tree species richness, Simpson, Shannon–Wiener, and Pielou index of UFs converged along the precipitation gradient; (3) the similarity of tree composition between UFs increased as precipitation of the cities were more similar; (4) the UFs in the 19 cities contained a total of 932 tree species, among which the nonnative species were more prevalent than the native species, and the top 37 species with high frequency appeared in 80% of the cities; and (5) Salix babylonica, Ginkgo biloba, Platycladus orientalis, Juniperus chinensis, and other tree species were suitable for planting in UFs in subtropical zones, regardless of humidity. The findings contribute to the understanding of urban forest development and provide insights for implementing greening policies aimed at providing additional ecosystem services. Full article