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To comprehend environmental changes and develop sustainable land management plans, it is crucial to thoroughly understand the complexities involved in land use change. This study investigated land use changes in Mae Sot district, Tak province, Thailand, in the years 2003, 2013, and 2023 using pixel-based supervised classification employing the Random Forest (RF) algorithm. The findings revealed that a substantial portion of the area is dedicated to agricultural purposes, accompanied by a decrease in forest areas and an increase in urban development. Furthermore, five spatial factors—elevation, slope, distance from the city center, distance from the main road, and population—were integrated as explanatory variables in training the cellular automata-artificial neuron network (CA-ANN) model to construct a land use change simulation for 2023. A comparison of the projected and actual land use maps for 2023 demonstrated a high level of agreement, with a kappa coefficient of 0.81, confirming the reliability of the model’s predictions. Subsequently, the CA-ANN model was used to forecast future land use for the year 2033. The projection suggested a significant expansion of built-up areas, primarily originating from the core of Mae Sot sub-district and spreading towards Tha Sai Luat, Mae Pa, and Mae Tao, and potentially extending northward along the national highway to Mae Kasa sub-district. Additionally, forested areas are anticipated to diminish, transitioning into agricultural zones, while certain existing agricultural regions are expected to be converted into urban spaces. These findings underscore the imminent expansion of urban areas within Mae Sot, emphasizing the necessity for well-thought-out planning to ensure long-term development. [ABSTRACT FROM AUTHOR]

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations' (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia's urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries. [ABSTRACT FROM AUTHOR]

Land use can affect energy consumption by changing the economic and social structure of cities. Thus, the optimization of land use patterns is key to promoting energy sustainability. In this study, we explored the spatiotemporal evolution of China's urban energy consumption and its driving factors from the role of land use, with the application of high-precision nighttime light images and land use data acquired from remote sensing satellites. A deep clustering network in deep learning was used for clustering analysis of urban energy consumption. The results indicated that the economic and structural effects of land use were the primary driving factors of the increasing urban energy consumption, whereas the decrease in the energy intensity (caused by technological progress) restrained the growth rate of energy consumption. With the exception of economically developed cities, generally, the contribution of the population size to the temporal increase in energy consumption was relatively small. The spatial difference in urban energy consumption was mainly due to the between-group differences among the diversified cluster groups, which were strongly influenced by land urbanization and population size. These conclusions can help the Chinese government formulate differentiated urban energy policies. [ABSTRACT FROM AUTHOR]

The population of Africa is expected to rise to 2.5 billion by 2050, with more than 80% of this increase concentrated in cities. Africa's anticipated population growth has serious implications for urban resource utilization and management, necessitating multi-level monitoring efforts that can inform planning and decision-making. Commonly, broad extent (e.g., country level) urban change analyses only examine a homogenous "developed" or "built-up" area, which may not capture patterns influenced by the heterogeneity of landscape features within urban areas. Contrarily, studies examining landscape heterogeneity at a finer resolution are typically limited in spatial extent (e.g., single city level). The goal of this study was to develop and test a hierarchical integrated mapping framework using globally available Earth Observation data (e.g., Landsat, Sentinel-2, Sentinel-1, and nightlight imagery) and accessible methodologies to produce national-level land use (LU) and urban-level land cover (LC) map products which may support a range of global and local monitoring and planning initiatives. We test our multi-tier methodology across three rapidly urbanizing African countries for the 2016–2020 period: Ethiopia, Nigeria, and South Africa. The initial output of our methodology includes annual national land use maps (Tier 1) for the purpose of delineating the dynamic boundaries of individual urban areas and monitoring national LU change. To complement Tier 1 LU maps, we detailed urban heterogeneity through LC classifications within urban areas (Tier 2) delineated using Tier 1 LU maps. Based on country-optimized sets of selected features that leverage spatial/texture and temporal dimensions of available data, we obtained an overall map accuracy of between 65 and 80% for Tier 1 maps and between 60 and 80% for Tier 2 maps, dependent on the evaluation country, although with consistent performance across study years providing a solid foundation for monitoring changes. We demonstrate the potential applications for our products through various analyses, including urbanization-driven LU change, and examine LC urban patterns across the three African study countries. While our findings allude to general differences in urban patterns across national scales, further analyses are needed to better understand the complex drivers behind urban LC configurations and their change patterns across different countries, city sizes, and rates of urbanization. Our multi-tier mapping framework is a viable strategy for producing harmonious, multi-level LULC products in developing countries using publicly available data and methodologies, which can serve as a basis for a wide range of informative and insightful monitoring analyses. [ABSTRACT FROM AUTHOR]

Debates over the development and density of housing have gained visibility in recent years as housing costs have skyrocketed in many metropolitan areas. With those who seek to limit development (NIMBYs) on one side and those who seek to promote it (YIMBYs) on the other, scholarly attention has turned to understanding preferences and power in the politics of development. This article reviews scholarship relating to the politics of land use. After discussing the history of land use regulation, we describe who the important actors are (and, generally, what they want) in the politics of land use; we explain the distribution of power and inequality in the politics of land use; and we briefly review how land use regulation affects other societal outcomes. We end with a set of unanswered political questions that could push the field forward. [ABSTRACT FROM AUTHOR]

Urban land serves as the medium for urban economic and residential activity. The degree of intensive and efficient use of urban land and the coordinated growth of the economy and society are significantly impacted by utilization efficiency. Northwest China's temperature, geography, water, and soil characteristics make less of the region suited for urban development. Thus, it is imperative to investigate Northwest China's urban land use efficiency (ULUE). This study applies the efficiency loss model to highlight the discrepancy between the optimal and actual values of each input-output variable of the ULUE, and uses the Super-SBM model to compute the ULUE of 39 cities. Additionally, the internal driving elements of the ULUE are examined using the Global Malmquist-Luenberger (GML) index. This study discovered that Northwest China had a low ULUE, with a modest upward trend seen between 2004 and 2018. The overall shape of the region was similar to an 'N' curve, with high efficiency values concentrated in the southeast of the region in 2018. When considering the agglomeration impact of urban agglomeration, these results are dependable. Moreover, the efficiency loss model discovered that pollution and wasteful resource usage were the main reasons behind the drop in ULUE in Northwest China. Finally, the GML index and its constituents revealed that the main force behind ULUE's growth in Northwest China is technological developments. This research provides important policy implications for reducing the gap between eastern and western regions, supporting high-quality development in northwest China, and protecting and intensively using urban land. [ABSTRACT FROM AUTHOR]

Land use efficiency is an indicator that measures the level of regional land resource allocation and optimization. The influence of policies tends to lead to the restructuring of regional land use and allocation in urban agglomerations, and the degree of optimization in land use efficiency has become a critical component in evaluating policy performance. In light of this, it is particularly necessary to clarify the theoretical relationship between the establishment of urban agglomerations and land use efficiency and to understand their underlying mechanisms. Additionally, it is particularly important to summarize the spatial differentiation of their effects. This study constructed a theoretical analysis framework to investigate the impact of urban agglomeration establishment on land use efficiency, deriving from the policy pathways of establishing urban agglomerations. It also explored the mediating roles of public service supply, industrial agglomeration, and technological innovation, as well as the moderating effects of geographical, institutional, and economic disparities. Building upon this foundation, the approval and implementation of national-level urban agglomeration planning were treated as a quasinatural experiment for further investigation. Based on the panel data of 244 prefecture-level and above cities in China from 2009 to 2020, this study used a staggered difference-in-differences model to empirically test the impact of establishing urban agglomerations on land use efficiency. In addition, the mediating effects were tested based on three types of pathways, and heterogeneity effects were examined using a 'center-periphery' approach with multiple types of spatial distances to analyze the mechanisms of influence. The study findings were as follows: (1) The establishment of urban agglomerations had a significant and positive impact on land use efficiency, and this effect continued and was strengthened after the establishment of national-level urban agglomerations. (2) The impact of establishing national-level urban agglomerations on land use efficiency was mediated by three factors: public service provision, industrial agglomeration, and technological innovation. However, after adjusting for policy timing, only the mediating effects of industrial agglomeration and technological innovation remained, and their effects were consistent with the establishment of national-level urban agglomerations. (3) The promotion of land use efficiency by national-level urban agglomerations exhibited spatial heterogeneity, with the effect being slightly suppressed as geographic and institutional distances increased. After adjusting for policy timing, this promoting effect was shown to exhibit spatial heterogeneity, being slightly suppressed only with increasing economic distances. Based on these findings, policy recommendations are made to further strengthen regional public investment through the allocation of resources in urban clusters, align industrial agglomeration and technological innovation with rational industry layout and division of labor, and enhance regional institutional reforms and integration to achieve regional balance. [ABSTRACT FROM AUTHOR]

The historical topographic map preserves rich geographic information and can provide direct assistance for the reconstruction of various geographic elements. Based on the historical data of cities throughout the Qing Dynasty, the land use scale data of cities across the country was obtained using GIS and urban perimeter conversion models. This study combines city information and city circumference records from the historical maps and archives of the late Qing Dynasty to quantitatively reconstruct the use patterns of ancient China's urban land at a spatial resolution of 1° × 1°. Uncertainty analysis of the reconstruction results was conducted using modern remote sensing image data as the validation data set. The results showed the following. (1) During the late Qing Dynasty, the total area of urban land in the various provinces and regions was 1456.015 km2. The maximum value was 208.691 km2 in Beijing-Tianjin-Hebei, the minimum value was 1.713 km2 in Qinghai, and the average value was 56.001 km2. (2) The results of grid reconstruction show that among the 398 grids with urban land distribution, the maximum value is 64.099 km2/grid, the minimum value is 0.013 km2/grid, and the average value is 3.658 km2/grid. (3) Of all the grids with urban land, the urban land grid to the west of the Hu Line accounts for 12.5% and the east to 87.5%. (4) During the late Qing Dynasty, urban land use in China was primarily concentrated in agriculturally developed areas such as the North China Plain, the Central Plains, Jiangnan, and the Sichuan-Chongqing region. (6) The results of a kernel density estimation showed that there were obviously three core areas of urban land agglomeration in China during the late Qing Dynasty: the North China Plain-Central Plains, the Jiangsu-Shanghai-Zhejiang-Anhui area, and the Sichuan-Chongqing urban core area. This study provides basic data for urban land use during historical periods and provides a basis for the quantitative reconstruction of relevant urban land data for historical archives. [ABSTRACT FROM AUTHOR]

Patterns of change in urban surfaces have implications for equity, health, and environmental quality in future cities and for their adaptability to climate change. Relatively little detailed research has been done on the role of land use policies—particularly the recent generation of infill and climate adaptation policies—in shaping urban surfaces and related environmental systems. I used a novel approach integrating three methods—land use change models, urban form assessments, and policy-based scenarios—to evaluate patterns of change. I applied these methods to a case study simulation of future development in Denver (CO) and used a stormwater management example to evaluate the capacity of the local management system to treat expansion of the city's hardscape. I found that the proportion of impervious cover in the city is projected to increase substantially, possibly to levels discussed in the literature as severely affected. A major social commitment to adaptation policies such as green infrastructure would be necessary to mitigate projected hardscape increases. Dispersed development strategies generate substantially more hardscape than geographically targeted development, which suggests that there is also untapped potential for climate adaptation in general land use policy. I share common limitations of scenario-based methods. Changes in the composition of urban surfaces in the next 2 decades could have significant environmental effects. In this research, I developed and tested methods that bridge infrastructure and land use planning practices—the public works and community planning silos in local governments—to evaluate these processes and effects. These methods should be useful in many cities. [ABSTRACT FROM AUTHOR]

Driven by the goal of achieving sustainable development and carbon neutrality. Addressing environmental pollution and remediating land damage have become critical challenges in resource-based cities and regions with low land use efficiency. As a response, this study focuses on the 23 provinces where China's coal resource-based cities are situated. Utilizing data from 2014 to 2020, this research employs the SBM-Undesirable model, which considers undesirable outputs in efficiency calculations, and the Tobit regression test. It aims to explore the spatio-temporal variations in industrial transformation within resource-based cities and its impact on the efficiency of green space utilization. Furthermore, it analyzes the characteristics and the extent of the influence of factors such as industrial structure adjustments on urban land use efficiency, maximizing the output of land as a factor of production. The results show that: (1) Over the 7-year period studied, China consistently made nationwide adjustments to land area and land use structure to meet the needs of urban development (2) The regression test results show that the industrial transformation of resource-based cities can promote the improvement of green space utilization efficiency. The positive influence coefficient is 0.064 and is significant at a 1% level. (3) Environmental regulation, government expenditure, international trade, and green cover play a positive role in promoting green land use. The study provides valuable insights for policymakers and urban planners seeking to foster sustainable development in resource-based cities. [ABSTRACT FROM AUTHOR]

The impact of flooding on cities is becoming increasingly significant in the context of climate change and rapid urbanization. Based on the analysis of the land use changes and rainstorm waterlogging inundation scenarios of Jiangqiao Town from 1980 to 2020, a scenario analysis was conducted to simulate and assess the rainstorm waterlogging disaster risk in 2040 under three land use scenarios (a natural development scenario, Scenario ND; an economic growth scenario, Scenario EG; and an ecological development priority scenario, Scenario EP) and three rainstorm scenarios with return periods of 10, 50, and 100 years. The following results were found: (1) Land use change is a significant factor in the risk of urban rainstorm waterlogging disaster caused by surface runoff and inundation depth change. In particular, the resultant increase in impermeable surfaces such as residential land and industrial land and the decrease in farmland during urbanization would lead to an increase in urban rainstorm waterlogging disaster risk. (2) Under three rainstorm scenarios, the future land use exposure elements and losses are consistent in terms of spatial distribution; from 10-year to 100-year return periods, they manifest as an expansion from the south to the surroundings, especially to the central region of the study area. The locations at risk are mainly distributed in the central and southern regions of Jiangqiao Town. (3) The economic losses are different in different land use scenarios and rainstorm scenarios. In the context of rainstorm scenarios with return periods of 10, 50, and 100 years, the total losses in land use scenario ND are CNY 560 million, CNY 890 million, and CNY 1.07 billion; those in land use scenario EG are CNY 630 million, CNY 980 million, and CNY 1.19 billion; and those in land use scenario EP are CNY 480 million, CNY 750 million, and CNY 910 million. The total losses of land use EP are the lowest by comparison. So, the influence of land use change on the rainstorm waterlogging disaster risk shows obvious differences among different rainstorm scenarios. This study has important reference value for decision making on land use management and flood disaster risk management in rapidly urbanizing areas. [ABSTRACT FROM AUTHOR]

Points-of-interests (POIs) have been proven to be indicative for sensing urban land use in numerous studies. However, recent progress mainly relies on spatial co-occurrence patterns among POI categories, which falls short in utilizing the rich semantic information embodied in POI hierarchical categories and in sensing the spatial distribution patterns of POIs at an individual zonal scale. In this context, we present a spatial and adversarial representation learning approach (SARL) for predicting land use of urban zones with POIs. SARL deeply mines the information from POIs from both spatial and categorical perspectives. Specifically, we first utilize a convolutional neural network to sense the spatial distribution patterns of POIs in each urban zone. We then leverage an autoencoder and an adversarial learning strategy to mine the POI categorical information in all hierarchical levels, which emphasizes the prominent and definitive POIs while preserves the overall POI hierarchical structures in each zone. Finally, we fuse these information from the two perspectives via a Wide & Deep network and carry out land use prediction with the fused embeddings. We conduct comprehensive experiments to validate the effectiveness of SARL in four European cities with real-world data. The results demonstrate that SARL substantially outperforms several competitive baselines. [ABSTRACT FROM AUTHOR]

Soils in residential areas of cities are heavily degraded, and the environmentally protective and formative functions are instead realized by the soils in recreational areas (city parks, etc.). The study aimed to analyse the influence of functional land use and the level of anthropogenic impact on the properties of anthropogenic horizons (Au and At) in the city of Volgograd, Russia. In this study, we analysed 50 soil samples from the city's recreational and residential functional areas under field and laboratory conditions. The study evaluated the morphological aspects (thickness, colour, structure, and presence of artefacts), physical properties (bulk density, texture), and chemical properties (pHwater, salt content, CaCO3, Corg, SOCstoc) of the soils. The anthropogenic Au horizons in residential areas exhibited a clumpy structure, numerous artefacts, and significant compaction. Conversely, the soils in recreational zones contained fewer anthropogenic artefacts, with the At horizons characterized by a lumpy structure. The anthropogenic horizons' median and mean property values in the functional zones showed significant differences. The acid-alkaline properties of the studied horizons were weakly alkaline in recreational areas and alkaline in residential areas. A common feature of all anthropogenic horizons was the variability in chemical, physical, and morphological properties depending on the functional zone and level of anthropogenic load. The indicators of a specific level of anthropogenic impact on urban landscapes included horizon thickness, Corg content, colour, and structure. [ABSTRACT FROM AUTHOR]

For high-density cities, it is necessary for city managers to achieve precise regulation of carbon emissions and sequestration. For reference, taking Dongguan as example, this study proposed a complex framework to identify weak areas of urban land use carbon metabolism in high-density city. On the basic of defining the urban land use carbon metabolism units, LEAP, Markove-PLUS, and LANDIS model were applied to spatialize land use carbon emissions and carbon sequestration. Finally, the weak areas of urban land use carbon metabolism were clearly indicated through overlapping the spatial pattern of land use carbon emissions and sequestration. Accordingly, carbon emissions limit regions can be delimited, and its carbon emissions are recommended to be metabolize through connecting the limit regions to green spaces with various ecological corridors. The results will serve as a foundation to plan and control carbon emissions in high-density cities that are similar to Dongguan in international communities. [ABSTRACT FROM AUTHOR]

Cocoa cultivation is one of the sectors that most contributes to Ecuador's GDP. The aim of this study is to determine the Land-Use Conflict (UC) for cocoa in 71 cities on the coast of Ecuador. Photointerpretation has been applied to locate the crop and geospatial modeling has been used to analyze Land Use Capacity (LUC) criteria, agroecological requirements and LUC at 1:5,000. Of the total study surface area, 4.65% is occupied by cocoa crops and of this value 66.03% is in adequate use (AU) and 33.97% in LUC, and the category of UC over lightly used is the most frequent with 17.25% of the total surface in LUC. In the coastal region, the province of Guayas has the lowest LUC in contrast to Los Rios; the predominant LUC for the crop is Class III 34.32%. The findings show that the LUC of cocoa has a low concordance with the national Agroecological Zoning (AZ), since this model considers the crop in natural conditions, the main limiting factor is the vegetative wet period and does not take into account whether the crop is present in the territory. The use of national AZ information to establish crops could limit their production, since it has been demonstrated that there are cultivated areas that are not in conflict and that are exploited by farmers. This study is replicable to other crops and scalable to any area. [ABSTRACT FROM AUTHOR]

Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation models. By solving the aforementioned issues, a deeply fused super resolution guided semantic segmentation network using 30 m Landsat images is proposed. A large-scale dataset comprising 10 m Sentinel-2, 30 m Landsat-8 images, and 10 m European Space Agency (ESA) Land Cover Product is introduced, facilitating model training and evaluation across diverse real-world scenarios. The proposed Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN) combines a Super Resolution Module (SRResNet) and a Semantic Segmentation Module (CRFFNet). SRResNet enhances spatial resolution, while CRFFNet leverages super-resolution information for finer-grained land cover classification. Experimental results demonstrate the superior performance of the proposed method in five different testing datasets, achieving 68.17–83.29% and 39.55–75.92% for overall accuracy and kappa, respectively. When compared to ResUnet with up-sampling block, increases of 2.16–34.27% and 8.32–43.97% were observed for overall accuracy and kappa, respectively. Moreover, we proposed a relative drop rate of accuracy metrics to evaluate the transferability. The model exhibits improved spatial transferability, demonstrating its effectiveness in generating accurate land cover maps for different cities. Multi-temporal analysis reveals the potential of the proposed method for studying land cover and land use changes over time. In addition, a comparison of the state-of-the-art full semantic segmentation models indicates that spatial details are fully exploited and presented in semantic segmentation results by the proposed method. [ABSTRACT FROM AUTHOR]

With rapid industrialization and urbanization, the contradiction between the human exploitation of land production and living functions and natural ecosystem service functions has intensified. The issues of how to coordinate the exploitation and conservation functions of land and guide the rational distribution of human activities have become important for global sustainable development, especially considering the realization that multifunctional land use is an effective way to relieve land pressure and improve land use efficiency, that land multifunction has significant spatio-temporal heterogeneity, and that there is a mutual promotion and stress relationship between multifunctional land use. However, few existing studies have discussed the decoupling relationship among land use functions. In this study, a system of 10 sub-functions and 25 indicators was established based on the production function (PDF), living function (LVF), and ecological function (ELF) for 59 cities in the Yellow River Basin (YRB). There are both subjective and objective procedures employed to determine the weights, while an exploratory spatial data analysis is used to analyze the time-based and territorial changes in various functions of land use in the study area from 2000 to 2020. The decoupling relationship between the three functions is detected utilizing the theoretical foundation of the decoupling analysis. The results show that land use is multifunctional, LUFs develop unevenly, and their spatial distribution varies substantially. The results of the decoupling analysis demonstrate that the predominant types of correlations among the land use ELF and PDF and LVF over the research period are strong decoupling and strong negative decoupling correlations, with the former being a dilemma and the latter being a sustainable type of development. [ABSTRACT FROM AUTHOR]

The establishment of the Free Trade (Pilot) Zone (FTZ) is a new attempt by China to embark on modern sustainable urban planning and governmental territorial management at the development stage of a high-quality economy. As urban lands serve as the limited resource foundation for civic production and livelihoods, enhancing land use efficiency becomes a key measure to facilitate metropolitan economies of high quality. Utilizing panel data from prefecture-level cities spanning from 2005 to 2021, this study constructs a multi-period difference-in-differences model to explore the impact of FTZs on urban land use efficiency (ULUE). The results indicate that FTZs can significantly enhance urban land use efficiency, with a more pronounced policy effect observed in central cities, inland cities, and cities with higher urbanization rates. Furthermore, the FTZs also demonstrate spatial spillover effects on urban land use efficiency. This study holds great significance for relevant government departments in formulating policies to optimize land resource allocation, promote FTZ development, and foster high-quality urban planning and territorial management. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LUCC) prediction of cities in Western China requires higher accuracy in quantitative demand and spatial layout because of complex challenges in balancing relationships between urban constructions and ecological developments. Considering city-level areas and various types of land use and land cover, existing LUCC models without constraint or with only loose demand constraints were impractical in providing evidence of high accuracy and high-resolution predictions in areas facing fierce land competition. In this study, we proposed a two-layer SD-ANN-CA model to simulate and explore the LUCC trend and layout predictions for 2018, 2028, and 2038 in Ya'an City, Western China. The two-layer structure with an upper layer of the SD model and a lower layer of the ANN-CA model, as well as the advantages of all three methods of system dynamics (SD), artificial neural network (ANN), and cellular automata (CA), have allowed us to consider the macro-level demand constraints, meso-level driving factors constraints, and the micro-level spatial constraints into a unified model framework. The simulation results of the year 2018 have shown significant improvement in the accuracy of the ANN-CA model constructed in our earlier work, especially in types of forest land (error-accuracy: 0.08%), grassland (error-accuracy: 0.23%), and construction land (error-accuracy: 0.18%). The layout predictions of all six types of land use in 2028 and 2038 are then carried out to provide visual evidence support, which may improve the efficiency of planning and policy-making processes. Our work may also provide insights into new ways to combine quantitative methods into spatial methods in constructing city-level or even regional-level LUCC models with high resolution. [ABSTRACT FROM AUTHOR]

In recent years, numerous multitemporal global land use and land cover products have been published acting as valuable source for training spatially explicit geosimulation models forecasting urban growth. However, there is a notable gap in research that specifically addresses the sensitivity of models traing with those data sets when it comes to regional modeling purposes. Accordingly, the objectives of this study were to calibrate, validate, and employ global urban input datasets for the regional simulation of urban growth by the year 2030. The SLEUTH urban growth model, focused on the metropolitan area of the Ruhr, Germany, was calibrated using the Global Human Settlement Layer, World Settlement Footprint Evolution, historical OpenStreetMap data, and a Digital Land Cover Model for Germany. The goal was to compare the results in terms of accuracy, certainty, quantity, and allocation, particularly in urban areas susceptible to floods and heat. While all models achieved high accuracy levels concerning quantity and allocation, the extent of new settlements varied from 40.77 km² to 477.91 km². The models based on World Settlement Footprint and OpenStreetMap exhibited higher certainty and lower stochasticity. As the simulated urban growth increased, there was a corresponding rise in the likelihood of allocating new settlements in areas affected by natural hazards. While all models presented a similar relative portion of new settlement areas impacted by floods, variations emerged in terms of areas affected by unfavorable thermal conditions. This study underscored the potential use of historical OpenStreetMap data in training cellular automation for geosimulating future settlement growth. Furthermore, it highlighted the applicability of global Earth observation-based urban datasets for regional geosimulation and explored the impacts of diverse input data on the accuracy, certainty, quantity, and allocation performances in simulating future conditions. [ABSTRACT FROM AUTHOR]

Policies to increase density and diversity in Australian cities have been introduced to remedy the issues caused by the separation and homogenisation of land uses characterising urban development in the post-war period. Generally, the practical implementation of such policies is through rezoning. Here, we analyse zoning and land use data of 10,519 parcels in Brisbane to demonstrate that, despite policy aimed at land use diversification, practice results in large swathes of homogenous uses. Our research highlights how zoning can maintain its current aim of land use separation while mitigating the emergence of "blandscapes" by protecting and enhancing land use diversity. [ABSTRACT FROM AUTHOR]

Introduction: With the rapid advancement of industrialization and urbanization in the Yellow River Basin, the number of resource-based cities is constantly increasing, leading to an imbalance in land use structure and the gradual worsening of adverse effects such as damage to the ecological environment. Hence, improving the efficiency of green land use in resource-based cities has extremely crucial ecological and practical meaning for sustainable development. Methods: This study comprehensively considers interdisciplinary theories such as ecology and management, and combines the actual situation of resource-based cities in the Yellow River Basin to construct a new type of urban field green utilization efficiency assessment index system. To promote the coordinated development of different dimensions, the corresponding spatiotemporal evolution process and related influencing factors were also analyzed. Results and discussion: The research results indicate that the overall spatial evolution of the Yellow River Basin has active correlation. During the period from 2006 to 2022, the number of high value agglomeration cities in upstream resource-based cities increased by nearly 25%; Nearly 3% of resourcecities exhibit low-value heterogeneity, while the number of resource cities with low value agglomeration has decreased to 0. In addition, population density and scientific education expenditure level are key factors affecting the ratio of green land use in resource-based cities, while the impact of economic indicators is gradually decreasing. In summary, the assessment indicator system for urban land green utilization ratio proposed in the study is more comprehensive, and the spatial distribution results of influencing factors can provide a solid data foundation for decision-makers. [ABSTRACT FROM AUTHOR]

The development and application of urban digital infrastructure can alter land use patterns and facilitate the aggregation of factors such as labor and capital, thereby influencing the land use efficiency in cities. Based on statistical data from 279 cities in China spanning from 2004 to 2019, this study employs fixed-effects and mediation models to analyze the impact of urban digital infrastructure on land use efficiency. The findings reveal that the construction of urban digital infrastructure significantly promotes the enhancement of land use efficiency, with technological innovation levels and industrial structural transformation serving as mediators between urban digital infrastructure and land use efficiency. The impact of urban digital infrastructure on land use efficiency exhibits heterogeneity across different city scales, urban tiers, geographic locations, and policy implementation batches. Its effects are more pronounced in larger-scale cities, higher-tier cities, those located in the central and western regions, and the first two batches of pilot cities. The research findings contribute to providing theoretical references and a decision-making basis for enhancing land use efficiency, advocating for increased investment in urban digital infrastructure construction, encouraging technological innovation levels, and facilitating the upgrading of industrial structural transformation. [ABSTRACT FROM AUTHOR]

With the continuous development of big data technology, semantic-rich multi-source big data provides broader prospects for the research of urban land use function recognition. This study relied on POI data and OSM data to select the central urban areas of five new first-tier cities as the study areas. The TF-IDF algorithm was used to identify the land use functional layout of the study area and establish a confusion matrix for accuracy verification. The results show that: (1) The common feature of these five cities is that the total number and area of land parcels for residential land, commercial service land, public management and service land, and green space and open space land all account for over 90%. (2) The Kappa coefficients were all in the range [0.61, 0.80], indicating a high consistency of accuracy evaluation. (3) Chengdu and Tianjin have the highest land use function mixing degree, followed by Xi'an, Nanjing, and Hangzhou. (4) Among the five new first-tier cities, Hangzhou and Nanjing have the highest similarity in land use function structure layout. This study attempts to reveal the current land use situation of five cities, which will provide a reference for urban development planning and management. [ABSTRACT FROM AUTHOR]

Mountainous cities face various land use challenges, including complex topography, low land use efficiency, and the insufficient control of land use in small-scale areas at the urban fringe. Considering population changes, environmental conservation, and urban planning, this study first established three scenarios: economic priority (Econ. Prior.), ecological priority (Ecol. Prior.), and balanced development (BD), and then used the Multi-Objective Planning (MOP) model to calculate the optimal land use structure. Finally, it carried out land use spatial layout optimization based on the Patch-generating Land Use Simulation (PLUS) model in 2035, Caijia Cluster, Chongqing, China. This approach, known as MOP-PLUS modeling, aimed to optimize land use. Meanwhile, the applicability of the PLUS model in simulating land use changes was discussed in small-scale mountainous areas. The results show the following: (1) The "quantity + space" approach in the MOP-PLUS model demonstrated the feasibility of the PLUS model in simulating land use change in small-scale mountainous areas. The overall accuracy (OA) of land use change simulation reached 81.60%, with a Kappa value of 0.73 and a Figure of Merit (FoM) coefficient of 0.263. (2) Land use optimization: Under the Econ. Prior. scenario, economic benefits peaked at 4.06 × 1010 CNY. Urban expansion was the largest, leading to increased patch fragmentation. The Ecol. Prior. scenario yielded the highest ecological benefits, reaching 7.46 × 107 CNY. The urban development pattern exhibited inward contraction, accompanied by urban retrogression. In the BD scenario, economic benefits totaled 3.89 × 1010 CNY, and ecological benefits amounted to 7.16 × 107 CNY. Construction land tended to concentrate spatially, leading to relatively optimal land use efficiency. Therefore, based on a comprehensive consideration of the regional land use constraint policies and spatial layout, we believe that a balance point for land use demands can be found in the BD scenario. It can ensure economic growth without compromising the ecological environment. [ABSTRACT FROM AUTHOR]

Contemporary humanity is confronted with the development dilemma, especially in developing countries, of limited environmental capacity and increased pressure to protect the environment. Based on the concept of the Leading Functional Space Partition, this study identifies land-use conflicts in the urban fringes of ecologically fragile areas in Western China. The purpose is to explore the distribution characteristics of land-use conflicts in urban fringe areas and provide data references to solve a series of ecological and environmental problems inherent in the process of urbanisation. First, relevant software was applied to analyse the current situation of regional land use, and clustering was conducted in line with the goal of "production-living-ecology" to form territorial space zoning with the same function. Then, three leading function evaluation systems were constructed with natural patches of land as the unit to evaluate the current suitability. According to the evaluation results and relevant planning, areas with land-use conflicts were identified, and their spatial characteristics were analysed on this basis. The results demonstrate that the incidence of land-use conflicts in urban fringe areas is high and the spatial distribution presents variability. In the case, the incidence of land-use conflicts in urban fringe areas is very high, with the highest concentration in areas 10–30 km away from urban built-up areas. And the proportion of ecological conflicts in urban fringe areas is the highest. [ABSTRACT FROM AUTHOR]

Land use/cover change has an important impact on global climate change and carbon cycle, and it has become another major source of carbon emission after energy consumption. Therefore, this study focuses on the main line of "land use carbon emissions-spatial and temporal patterns-influencing factors," and selects 13 cities in Jiangsu Province as the research object. Based on the data of land use and energy consumption, combined with the method of land use carbon emissions and ArcGIS technology, this study conducted a quantitative analysis of the spatio-temporal distribution of carbon emissions in Jiangsu Province. The factors affecting the spatial distribution of carbon emissions from land use in Jiangsu Province were discussed by using Geographic detector. The results show that: 1) Carbon emissions in Jiangsu Province showed an overall growth trend, from 16215.44 × 104tC in 2010-23597.68 × 104tC in 2020, with an average annual growth rate of 4.55%, of which the construction land and watersheds had a greater impact on carbon sources and sinks, respectively. 2) During the period, there were significant differences in carbon emission levels among different cities in Jiangsu Province, and the land use carbon emission in Jiangsu Province showed a stable spatial pattern of "northwest--southeast." The southern part of Jiangsu is always the hot area of carbon emission, while the cold spot area is mainly distributed in the northern and central parts of Jiangsu. 3) The interaction of factors such as economic development, industrial structure, energy intensity, land use and human activities is an important reason for the spatio-temporal differences of land use carbon emissions in Jiangsu Province. Among them, the level of urbanization, population size and economic aggregate have significant effects on carbon emissions. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is critical for sustainable urban development. This study aimed to project the LST of Nanjing for 2025 and 2030 under different climate change scenarios using simulated LULC and land coverage indicators. Thermal infrared data from Landsat images were used to derive spatiotemporal patterns of LST in Nanjing from 1990 to 2020. The patch-generating land use simulation (PLUS) model was applied to simulate the LULC of Nanjing for 2025 and 2030 using historical LULC data and spatial driving factors. We simulated the corresponding land coverage indicators using simulated LULC data. We then generated LSTs for 2025 and 2030 under different climate change scenarios by applying regression relationships between LST and land coverage indicators. The results show that the LST of Nanjing has been increasing since 1990, with the mean LST increased from 23.44 °C in 1990 to 25.40 °C in 2020, and the mean LST estimated to reach 26.73 °C in 2030 (SSP585 scenario, integrated scenario of SSP5 and RCP5.8). There were significant differences in the LST under different climate scenarios, with increases in LST gradually decreasing under the SSP126 scenario (integrated scenario of SSP1 and RCP2.6). LST growth was similar to the historical trend under the SSP245 scenario (integrated scenario of SSP2 and RCP4.5), and an extreme increase in LST was observed under the SSP585 scenario. Our results suggest that the increase in impervious surface area is the main reason for the LST increase and urban heat island (UHI) effect. Overall, we proposed a method to project future LST considering land use change effects and provide reasonable LST scenarios for Nanjing, which may be useful for mitigating the UHI effect. [ABSTRACT FROM AUTHOR]

With the rapid progress in urbanization and economic development, the impact of land use change (LUC) on ecosystem services is becoming increasingly significant. However, the accuracy of ecological risk assessment faces challenges due to the presence of uncertainty factors. Using the PLUS model, this study aims to simulate and predict land use changes (LUCs), focusing on the southern hilly regions in southeastern China as a case study, conducting an in-depth assessment of ecological risk uncertainty. Firstly, a spatiotemporal simulation of LUCs in the southern hilly region from 1990 to 2030 was conducted under multiple scenarios. Subsequently, differences in the spatial and temporal distribution of ecosystem service value (ESV) across different years and forecast scenarios in the southern hilly region were revealed, followed by a detailed analysis of the impact of LUCs on ESV. Finally, by calculating the Ecological Risk Index (ERI), the study systematically analyzed the evolution trend of ecological risk in the southern hilly region of China from 1990 to 2030. The main research findings are as follows: (1) the conversion proportions of different land use types vary significantly under different scenarios. Compared to 2020, under the 2030 National Development Scenarios (NDSs), there has been a slight decrease of around 3% in the total conversion area of farmland, forest, and grassland. However, under the Ecological Protection Scenario (EPS) and Urban Development Scenario (UDS) scenarios, there has been an increase in the area of forest and grassland, with a rise of approximately 1.5% in converted built-up land. (2) Western cities (e.g., Yueyang and Yiyang), central cities (e.g., Jiujiang), and northeastern cities (e.g., Suzhou) of China exhibit a relatively high ESV distribution, while ESV significantly decreased overall from 2010 to 2020. However, under the EPS and UDS, ESV shows a significant increasing trend, suggesting that these two scenarios may play a crucial role in ecosystem restoration. (3) The conversion of forest and water bodies to farmland has the most significant inhibitory effect on ESV, especially during the period from 1990 to 2000, providing substantial data support for relevant policy formulation. (4) From 1990 to 2030, ecological risk gradually increased in western, central, and southwestern cities of the southern hilly region, with the highest ecological risk values under the EPS scenario in northern cities (e.g., Chizhou and Tongling). Under the UDS scenario, there has been a significant decrease in ecological risk, providing valuable insights for future ecological conservation and sustainable development. However, a limitation lies in the need for further enhancement of the scenario's simulation authenticity. This study offers a new perspective for understanding the impact of LUCs on ecosystem services and the uncertainty of ecological risks, providing crucial reference points for land resource management and the formulation of ecological conservation policies. [ABSTRACT FROM AUTHOR]

Land use evaluation and capacity assessment for sustainable urban development in Ahvaz city has become crucial due to rapid urban expansion negatively impacting current and future development. Urban population growth and resource demands have led to a decline in environmental quality. Over the past decades, Ahvaz has identified potential areas for future expansion until 2030. This study assesses the city's physical expansion and spatial development patterns, aiming to identify optimistic future development scenarios. Emphasizing balanced and equitable development, the study focuses on suitable areas, environmental preservation, pollution reduction, and mitigating urban hazards. Various factors, including land use, soil type, vegetation density, landform, groundwater level, geology, and distances from railways, industries, roads, hinterland cities, water networks, faults, and the city itself, are considered. Three decision-making methods—logic, Analytical Hierarchy Process (AHP), and integrated Fuzzy-AHP—are applied to map urban physical development. Fuzzy logic, AHP models, Expert Choice, Arc GIS software, and a land fit zoning map are used for data analysis. The AHP assesses the factors' relative importance, followed by standardization based on fuzzy set theory. Criteria factors are then combined using AHP and fuzzy logic to generate a land suitability map. The study identifies five zones for Ahvaz's future development—very high, high, medium, low, and very low—based on their physical expansion potential. The third scenario, supported by maps, indicates favorable conditions for future development in the northwest and southwest zones, considering environmental characteristics, infrastructure, and land continuity. [ABSTRACT FROM AUTHOR]

Urban land is the primary location for manufacturing and services, facilitating the expansion and interconnectedness of economic activities and factor flows to shape various urban land-use performances (ULUP). Exploring these spatial linkages of urban land-use performance can play a crucial role in fostering cohesive urban development. Taking 109 prefecture-level cities as research samples, this paper explores the characteristics and influencing factors of the spatial network associated with ULUP in the Yangtze River Economic Belt through modified gravity model, network analysis and QAP analysis. The analysis shows that ULUP has shown an N-shaped trend over the past two decades. It has formed a network of correlations, with Chongqing, Shanghai, and Wuhan emerging as central nodes. Notably, the correlation predominantly occurs between geographically adjacent cities, with weaker links between the Yangtze River Economic Belt's upstream, midstream, and downstream regions. The network can be divided into four distinct types: main inflow plate, main outflow plate, agent plate, and bidirectional spillover plate. Geographic location is the most significant factor influencing network formation, followed by resource mismatch, economic development, and openness. The study provides theoretical guidance and empirical support for improving the utilisation of urban land and promoting coordinated development. [ABSTRACT FROM AUTHOR]

It is an inevitable trend for urban transportation to achieve compact development. Land elements are the carrier of high-quality development of urban transportation, so it is of practical significance to explore how the compact development of urban transportation affects the efficiency of green land use in promoting the intensive use of urban land and achieving China ’s goal of carbon neutrality. Therefore, based on the relevant data of 84 cities in eastern China from 2005 to 2019, evaluation systems of urban traffic compact‑ ness and green land use efficiency were constructed respectively. The entropy method and the unexpected output super efficiency SBM model were used to measure urban traffic compactness and green land use efficiency and to analyze their spatio-temporal evolution laws. Finally, the impact of compact traffic development on urban land green use efficiency was explored by establishing a spatial error model. This study also analyzed the different effects of different traffic factors on land green use efficiency. The research results showed that: ① From 2005 to 2019, the urban traffic compactness in the eastern region had a trend of continual increase, and the regional gap gradually narrowed. ② During the study period, the urban land green use efficiency in the eastern region had a significant positive spatial correlation, and the overall utilization rate showed an upward trend, with the average efficiency rising from 0.393 in 2005 to 0.618 in 2018. In 2019, the efficiency value affected by government environmental regulations dropped to 0.398. ③ The results of the spatial measurement showed that the compact development of urban traffic had a positive and significant impact on green land use efficiency when considering the spatial effect. Moreover, the influence of the traffic accessibility factor was the strongest, the efficiency factor of the public transport system was in the middle, and the influence of the traffic infrastructure construction factor was comparatively weak. Therefore, based on the research results of the impact of compact urban transport development on green land use efficiency, it is proposed that urban transport development should promote the construction of transport infrastructure in multiple ways, improve the efficiency of the public transport system, and promote the improvement of green land use efficiency while implementing a high standard of regional transport accessibility. At the same time, central cities should take the initiative to leverage the spatial spillover effect of green land use efficiency to respond to the national requirements for building compact cities and ecological cities. [ABSTRACT FROM AUTHOR]

• We investigate driving forces of different land-use types to BSD. • Diversified machine learnings are proposed for spatial interpolation fusion. • Hotspot detection establishes sampling points for reducing the MAUP effect. • Fusion models improve the interpolation performance: accuracy and stability. • Word clouds identifies predominant facility names in each land-use type. Land use plays a crucial role in promoting the bike-sharing demand. Traditionally, studies on bike-sharing demand (BSD) are mainly focused on its prediction through regression methods, but the influence of MAUP (modifiable areal unit problem) in modeling is ignored. This paper aims to model spatial BSD distribution and prove the driving forces of different land use types to BSD through a machine-learning-based multiple interpolation fusion method. The hotspot detection model is employed to establish sample points covering different land use types in urban areas. In order to capture the differences in adaptations among different urban regions and for different data sizes, six machine learning methods are applied and evaluated to improve BSD estimation by fusing five spatial interpolation algorithms, including Inverse Distance Weight, Spline, Kriging, Natural Neighborhood and Trend. The methodological verification of Beijing City shows that the fusion models improve the estimation performance compared with individual interpolation algorithms, and that GRNN (generalized regression neural network) method is superior to all the others. According to fitting results of all POIs based on the GRNN fusion model, we identify which types of facilities correspond to customers that will have a stronger preference for bike-sharing and demonstrate which facility names are more prominent in each land use type. The conclusions presented here enrich our understanding relationships between land-use and BSD, which provide a valuable foundation for the bike-sharing development. Compared with implementing regression in an analysis zone or a square grid, troubles caused by the MAUP are effectively solved through this method. [ABSTRACT FROM AUTHOR]

Urban land-use types, such as residential and administration, can be inferred through semantic objects and their relationships. Point of interest (POI) data can serve as the semantic objects for urban land-use mapping. However, the previous POI-based approaches have rarely considered the relationships between the semantic objects in the urban land-use mapping, and three main challenges remain: 1) the lack of paired semantic object/land-use samples; 2) the lack of a unified model for semantic objects and the relationships between sematic objects and urban land use; and 3) the difficulty of automatically learning semantic object/land-use mapping relationships. In this paper, to address these issues, a graph-based urban land-use mapping framework integrating semantic object/land-use relationships (GOLR) is proposed. Based on open-source area of interest (AOI) and POI data, an urban object/land-use (UOLU) dataset covering 34 cities in China was built. To model the spatial and mapping relationships, the semantic objects and their relationships are used to jointly build an urban land-use graph. The mapping from semantic objects to urban land use can then be learned by the urban land-use graph isomorphic network (ULGIN) model. Finally, the GOLR framework was applied to obtain accurate land-use mapping results for multiple Chinese cities. [ABSTRACT FROM AUTHOR]

The availability of productive land is significantly impacted by the global phenomenon of urbanization. The amount of land available for food production and other essential activities decreases as cities grow because the urban perimeter encroaches on rural and natural areas. Conducting research on urban sprawl analysis and land use land cover (LULC) change assessment is essential in ensuring sustainable urban growth. Bengaluru, a rapidly expanding metropolitan city, has a significant impact on the area around it, making it a prime location for this kind of study. In this study, authors sought to assess how urban sprawl affected LULC in the Bengaluru rural district that surrounds the city of Bengaluru. The study evaluated changes in LULC over a two-decade period using remote sensing data and GIS tools. Five LULC classes were used to categorize the study area: settlement, waterbody, vegetation, agriculture, and barren land. The maximum likelihood technique was used to classify Landsat images from three different time periods using the supervised image classification method in the ERDAS software. Accuracy assessment was used to gauge the classified images' accuracy. The study's important findings showed how the LULC classes in the study area have been negatively impacted by the urban sprawl. The study emphasizes the significance of ongoing research in LULC change assessment and urban sprawl analysis to ensure sustainable urban growth and safeguard the availability of productive land. [ABSTRACT FROM AUTHOR]

Little is known about the long-distance telecoupling effects of urban food demands on land use changes (LUCs) in remote oases of the Southern Sahara. Using the example of two typical oasis settlements on Mont Bagzam in the southern Aϊr Mountains of Niger which are linked to regional and global markets by an unpaved road since 2015, this study aimed at analyzing time trajectories of LUCs and related changing agricultural production patterns. LUCs were quantified for 1955 to 2022 using GIS-based mapping of agriculture and natural vegetation based on historical aerial photographs, CORONA and multi-spectral satellite images, and high resolution drone-based surveys. The results show a major increment in actively used agricultural land in the 850 ha watershed of the two oases from 11 ha in 1955 to 13 ha in 2003 and 68 ha in 2022 as well as the addition of 92 irrigation wells to 16 existing ones between 2003 and 2022. LUCs and evapotranspiration calculated from climatic data of a local weather station allowed to estimate changes of irrigation water needs in the selected watershed. While annual precipitation averages only 214 mm, local reference evapotranspiration may reach 1,476 mm year-1. Therefore, the additional annual irrigation water needs for the newly established fields between 2003 and 2022 cultivated to cash crops rose by 696 million l. To detect LUC effects on soil quality, soil samples of onion and garlic fields of different ages were collected employing a false-time-series approach. Results reveal increasing soil pH and salt concentrations and falling ground water tables, which reflects a negative water balance and ground water extraction above recharge levels. Our study provides evidence that the newly established telecoupled production systems on Mont Bagzam threaten the sustainability of existing local agricultural production and related livelihoods of agro-pastoralists. [ABSTRACT FROM AUTHOR]

In the future, the pursuit of high-quality economic development and a focus on ecological environmental protection in China will inevitably result in significant conflicts between land use and ecological land use. Land use conflicts primarily occur in peri-urban areas characterized by prominent economic development and urban agglomeration. As a municipality situated in the Qin-Ba mountainous region, Ankang has been promoting ecological preservation for a span of two decades. The allocation of construction areas primarily focuses on the proximity of watersheds, while the northern and southern regions are predominantly characterized by mountainous and hilly terrain, thus exhibiting distinct regional attributes. Previous studies have mainly focused on analyzing the effects of land use on habitat quality during intense urbanization, with fewer studies focusing on integrating eco-regions with urbanization to assess changes in habitat quality. However, it is important to recognize that land pressure encompasses economic, ecological, and social aspects. Additionally, it aims to provide insights for the harmonious development of land use in eco-region-oriented cities. Using the SSPs-RCPs scenarios provided by the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper employs a system analysis method, the emerging Patch-Generating Land Use Simulation model (PLUS) model, Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and Land Use Conflict Measurement model to dynamically simulate the future habitat quality and spatial conflict patterns of land use in Ankang City. The study explores the spatial coupling effect of both factors under different scenarios. The results indicate the following: (1) The overall development trend reveals an intensification of land use conflicts and a decrease in habitat quality. The expansion rate of construction land is increasing and exhibiting aggregation, while agricultural land area is expanding, and forest land area is continuously decreasing. (2) Land use intensity exhibits a significant positive correlation with land conflict levels, while land conflict levels demonstrate a significant negative correlation with habitat quality. (3) Under different future shared socioeconomic path scenarios, land use intensity, and land conflict levels follow the order of SSP585 (high forcing scenario), SSP370 (medium to high forcing scenario), SSP245 (medium forcing scenario), and SSP126 (low forcing scenario), with intensity and conflict decreasing accordingly. These findings suggest that land use has had some impact on the ecological environment, with indications of habitat degradation. Even in Ankang, where ecological development is highly valued, the city will gradually face conflicts between ecological protection and economic development in future scenarios. The city already has the environment at heart, so it will be worse in cities where the priority is economic development. Therefore, it is crucial to allocate sufficient space for economic development while simultaneously prioritizing ecological protection. The results of the study can provide a reference for analyzing the trade-offs between land development and habitat protection in eco-mountainous cities and for eco-cities to avoid falling into the conflict dilemma of economic cities. [ABSTRACT FROM AUTHOR]

Against the backdrop of coordinated development and regional integration in the Guangdong–Hong Kong–Macao Greater Bay Area, China, the integration of Shenzhen and Dongguan has received increasing attention from both academia and the government. The cross-boundary area of the two cities has also shifted from being categorized as the development edge to becoming a key area, presenting more complex spatial and functional characteristics. This paper applies multi-source data to analyze the spatial characteristics and factors of influence in the Shenzhen–Dongguan cross-boundary region from the perspective of urban land use pattern evolution. The results showed that from 2008 to 2022, (1) the spatial structure presented a development trend from multi-core to axial, then to network, gradually breaking down the development barriers in the central region; (2) the function of regional land was transformed from a single type to a composite and mixed type, and the layout of commercial, public, green, and transportation land became more dispersed and balanced; (3) the cross-administrative boundaries of commercial land were exaggerated, and industrial land was widely transformed into composite industrial and commercial land, forming an integrated industrial belt; and (4) the overall increase in the mixing degree of land use functions indicated that the two regions were interconnected and integrated with each other, and the functional structure of the cross-boundary region was relatively more complete. [ABSTRACT FROM AUTHOR]

The urban agglomeration in the middle reaches of the Yangtze river (MYRUA) is the second largest national urban agglomeration in China, with an excellent ecological foundation. As the construction of urban agglomerations accelerated towards both extension and connotation, land prices of various cities rose, and the contradiction in the demand and utilization of land resources became acute. Therefore, exploring the spatiotemporal evolution and driving mechanisms of land green utilization efficiency within MYRUA had positive significance. This article focused on 28 prefecture‐level cities in the MYRUA and measured their urban land green use efficiency (ULGUE) from 2006 to 2018 using the super slack‐based measure model with undesirable output (Super‐SBM‐U). Then, the improved Fixed Malmquist–Luenberger index was developed to improve the accuracy of dynamic efficiency analysis. Further, the internal and external driving factors were explored using the index decomposition and the spatial error model with fixed effects. The results showed that the land use mode of the urban agglomeration in the middle reaches of the Yangtze River was relatively green, but efficiency gaps still existed. Although the green total factor productivity of the whole cluster has improved by 3.30% a year on average, the excessive pursuit of technological innovation has led to the stagnation of urban land management in various cities, and there are numerous inherent contradictions in urban development. But at the same time, feasible economic development directions, industrial upgrading, high‐quality employees, land marketization, and positive environmental behaviors are conducive to the green and sustainable development of ULGUE. This article depicted the internal land use characteristics of the MYRUA from the perspective of spatiotemporal evolution and efficiency, providing a theoretical reference for the green development and coordinated management of urban agglomeration land. Recommendations for Resource Managers: Natural features and socioeconomic heterogeneity lead to a significant discrepancy in the green efficiency of urban land use. Thus, they should be regarded as the scientific basis of policy design for an entire urban agglomeration.Improving regional connections is vital for enhancing the exemplary and radiation effects of core cities on green land use.While the importance of technology has been stressed, the management of related systems should keep pace to provide sustainable forces for the development of urban agglomerations. [ABSTRACT FROM AUTHOR]

The study of urban land use efficiency is of great significance for optimizing the spatial allocation of urban land, thereby promoting the intensive use of urban land and the transformation of economic development modes. Taking the Yangtze River Economic Belt (YREB) as the study object, we chose the undesirable Slacks-Based Measure (SBM) model to calculate the urban land use efficiency (ULUE). Then, we utilized the spatial correlation analysis and econometric methods to discuss its spatio-temporal features and influential factors. The results show the following: (1) The urban land use efficiency in the YREB steadily improved from 2010 to 2022, but the inter-regional efficiency gap evidently increased. (2) There is an efficiency value to be found in a multi-center network structure, and it forms a "core-periphery" distribution pattern. The high-efficiency areas in the downstream and upstream regions of the YREB are gradually increasing, while the efficiency value in the midstream area remains low. (3) The urban efficiency values have strong correlation, and they are mainly "High-High agglomeration" and "Low-Low agglomeration", and they show significant regional characteristics. (4) The economic level, industrial structure, and urbanization have obvious motivating effects on ULUE, and the positive spatial spillover effect is clear. The foreign direct investment and land finance hinder the boost of efficiency, and the latter has a negative spatial spillover role on the ULUE in the downstream cities. [ABSTRACT FROM AUTHOR]

The ecological impact of non‐native species arises from their establishment in local assemblages. However, the rates of non‐native spread in new regions and their determinants have not been comprehensively studied. Here, we combined global databases documenting the occurrence of non‐native species and residence of non‐native birds, mammals, and vascular plants at regional and local scales to describe how the likelihood of non‐native occurrence and their proportion in local assemblages relate with their residence time and levels of human usage in different ecosystems. Our findings reveal that local non‐native occurrence generally increases with residence time. Colonization is most rapid in croplands and urban areas, while it is slower and variable in natural or semi‐natural ecosystems. Notably, non‐native occurrence continues to rise even 200 years after introduction, especially for birds and vascular plants, and in other land‐use types rather than croplands and urban areas. The impact of residence time on non‐native proportions is significant only for mammals. We conclude that the continental exchange of biotas requires considerable time for effects to manifest at the local scale across taxa and land‐use types. The unpredictability of future impacts, implied by the slow spread of non‐native species, strengthens the call for stronger regulations on the exchange of non‐native species to reduce the long‐lasting invasion debt looming on ecosystems' future. [ABSTRACT FROM AUTHOR]

It is proven a powerful tool to reduce carbon emissions by regulating urban land use structure. To leverage this tool, land-use carbon emission intensity (LUCEI) is the critical indicator to bridge carbon emissions effects of natural and human activities to land use structure. Current studies try to established the carbon emission allocation framework of "carbon emission inventory - land use category" and use it to estimate the LUCEI. However, none of the frameworks work at multiple spatial scales: the total carbon emissions and the land use category at different territorial levels do not conform with each other, impeding the synergetic effects of the low-carbon planning at different territorial levels. This study establishes a new framework of carbon emission estimation and allocation for the three territorial levels of municipality, city proper, and district and conducts a case study on Hinggan Prefecture. With the new framework, the carbon emission intensity of the entire land use category is being systematically differentiated, enabling the clarification of the linkages between LUCEI and the characteristics of energy structure, industrial structure, and urban forms of cities. The comparison of LUCEI of residential and transportation land between cities identifies the characteristics of urban form that are apt to high carbon emissions. The biggest contribution of the framework established in this study provides a technical tool to introduce carbon emission quantification into the territorial and spatial planning system. It may help deepen the understanding about the spatial pattern of carbon emissions and support a more refined estimation and comparison of the carbon effects of land use planning schemes. [ABSTRACT FROM AUTHOR]

Residents of urban areas experience a variety of difficulties as a result of intensive urbanization, including clogged infrastructure, air pollution, and waste management. Preservation of green spaces provides a vital role in maintaining at least a minimum natural balance for residents of urban area. Maintaining and preserving the sustainable development of infrastructure is an important element of urban planning. City planners thus face a difficulty in providing urban dwellers with efficient and sustainable green spaces. Growing problems in the field of land use and landscaping of urban areas require an operational assessment of the existing situation. GIS technology offers a powerful tool for assessing these issues and developing solutions for sustainable urban development. A GIS-based land use assessment can provide valuable information on the distribution of urban land use types, which can help identify areas of high land use intensity and potential for urban green space development. The use of satellite imagery and other methods can help track changes in land-use patterns over time, which can inform urban planning and decision-making processes. Based on GIS analysis and remote sensing, an assessment was made of changes in territories and the distribution of green spaces in 4 urban districts of Bishkek. These changes were assessed using LANDSAT 4-5, LANDSAT-7, LANDSAT-8 and Sentinel2 imagery. The evaluation category included such areas as: parks, squares and street trees. Areas with a level of green space coverage for different parts of the city were identified by utilizing GIS technology to analyze the spatial structures of green spaces. The spatial analysis also made it possible to identify priority areas for the development of new green spaces. For this study, we selected all ‘green’ areas within the city of Bishkek. The southern region of the city is the most interesting from the perspective of how the fabric of the city has altered as a result of the elimination of green spaces. This territory was originally designed taking into account the zone of influence of the Issyk-Ata tectonic fault and developed to achieve and comply with certain seismological standards. Our study thus explores the characteristics of the growth of the city of Bishkek over the previous few decades by providing information on the distribution of land use types and green areas. [ABSTRACT FROM AUTHOR]

Towards the drive for environmental sustainability and sustainable urban development, geospatial technology has been deployed to study land use/land cover (LULC) dynamics in Abeokuta and environs, Southwestern Nigeria. The natural, human and social-economic interventions on the landscape have assumed prominent dimensions. The aim of this study was to evaluate the trend, rate and magnitude of changes, within the framework for effective planning and sustainable growth. Three sets of land satellite imageries (30 m × 30 m resolution) of 2001, 2008 and 2015 were harnessed for the study. Contrast enhancement and considerations were made to minimize radiometric effect due to seasonal changes. A supervised classification was conducted using maximum likelihood algorithm while a Markovian transition estimator (MTE) was applied for the prediction of future land cover change. Urban areas increased sharply from 938.82 km2 (19.18%) in 2001 to 1423.58 km2 (29.08%) in 2015. The area under shrubs cover increased at a rate of 1.08 km2 (0.07%) per annum. Forested wetland and farmland declined from 871.67 km2 (17.80%) to 795.43 km2 (16.25%) and 1445.77 km2 (29.53%) to 1022.13 km2 (20.88%) in 2001 and 2015, respectively. The net decrease indicated the anthropogenic interference into surrounding natural landscape. Effective policy planning and sustainable growth require proactive and holistic monitoring of LULC dynamics. This will facilitate informed decisions on land resources, environmental sustainability and sustainable urban development in the region. [ABSTRACT FROM AUTHOR]

Analyzing the spatio-temporal relationship between socio-economics and land-use structure at the micro-scale is crucial for effective spatial governance in large cities. This paper focuses on Beijing, utilizing long time-series remote sensing images and multi-source data spanning 30 years. We employ spatio-temporal clustering based on kilometer grid cells and a community-scale multi-factor aggregation method to categorize the linkages and spatio-temporal matching of population, GDP, land development, and ecological protection at the community level in a problem-oriented approach. Results indicate significant changes in Beijing's population, GDP, and land use, with a 11.53% increase in land development intensity. We identify significant temporal and spatial disparities between population–GDP dynamics, population–land development trends, and GDP–land development patterns, underscoring the multifaceted challenges inherent in urban governance. Areas characterized by lagging population concentration, sluggish economic growth, rampant land development, and ecological fragility collectively encapsulate notable portions of Beijing's expansive urban terrain. Mismatches pose governance risks, with medium to high-risk communities comprising 18.08% of community units and high-risk types representing 4.27% in Beijing. These discrepancies pose formidable governance risks, with communities ranging from moderate to high-risk categories, necessitating tailored interventions to address their unique challenges. This systematic exploration of comprehensive governance issues within mega-cities promises to furnish decision-makers with invaluable insights, facilitating nuanced and strategic urban governance approaches tailored to the intricacies of urban dynamics and challenges. [ABSTRACT FROM AUTHOR]

Assessing the dynamics and patterns of Land Use and Land Cover (LULC) and its transformation is an important practice of urban planners and environmentalists for a variety of applications, including land management, urban climate modeling, and sustainability of any urban region. Monitoring changes in LULC using geospatial techniques can help to identify areas at risk for indefensible land use, low-grade environment, and especially for sustainable urban planning. This study aims to analyze the changing pattern, dynamics, and alteration of LULC using Google Earth Engine (GEE) and Machine Learning Applications for the years 1991, 2001, 2011, and 2022 in the Varanasi City Development Region (VCDR). The LULC classification was divided into seven classes using random forest classification, and Landsat-5(TM) and 9(OLI-2) satellite data were used. Saga GIS has been utilized for the detection of LULC change during the 1991-2022 period. For validation of classification results, accuracy assessment was estimated using error matrices and through user, producer, and overall accuracy estimation. The Kappa statistics were applied for the reliability of the accuracy assessment result. As a result, the built-up area increased by 507.8 percent, and other classes like agricultural, barren, fallow land, and vegetation cover rapidly declined and altered into concrete areas over the period. Water bodies and river sand classes have been slightly converted into different classes. The finding explains that 114.8 km² of fertile agricultural land, 14.81 km² barren land, and 12.93 km2 of vegetation cover transformed into impervious surface, which is unsustainable and causes various problems like food scarcity, environmental degradation, and low quality of urban life. This study can be a useful guide for urban planners, academicians, and policymakers by providing a scientific background for sustainable urban planning and management of VCDR and other cities as well. [ABSTRACT FROM AUTHOR]

In this article we propose an ecological economics orientation of peri-urban development, looking at land use planning, local food and entropy. Based on a mathematical model, we present an operational concept of minimizing negative externalities within a given population. The model applies control theory. The concept is introduced to facilitate closing cycles, conduct spatial planning and reduce costs to achieve the ecological target of improved entropy. To this end, we look at more soundly defined metropolitan areas. An emphasis is placed on optimally assuring space for urban agriculture and on enabling recycling in ever-growing cities. Our concept is grounded in the use of peri-urban agriculture and regional food provision as an integrated system, which is based on the recycling of organic matter. Firstly, we reference current unhealthy developFfigments and show how cycles were removed as growth occurred. Secondly, as market-oriented city expansions showed limited scope for peri-urban farming, we suggest entering into regional planning. Planning shall ensure a better use of space and can be based on organic matter recycling (composting, slurry, etc.). The article provides a theoretical background for the occurrence of modified land use (systems). These systems shall alleviate some external burdens of large and growing cities. The approach looks at ecological, economic and social aspects in parallel, to outline principles for more sustainable land use, including peri-urban land. Methodologically we offer land use modelling, looking at interactions of industry, residence, and farming. The functions of a city are integrated into a methodical approach of distance from centre to periphery. [ABSTRACT FROM AUTHOR]

The development and utilization of land in the main urban area have significantly impacted the stability of the regional geological environment through various means, such as increased load and subway construction, primarily manifested as rock and soil mass deformation leading to geological hazards. Therefore, it is worth exploring how to reduce the occurrence of karst ground subsidence (KGS) through reasonable land development and control measures in the main urban areas with large-scale developments of buried karst formations. This study focuses on the main urban area of Wuhan City. An evaluation model for KGS was constructed using the analytic hierarchy process (AHP) and comprehensive index evaluation method by analyzing the geological conditions that affect KGS. The susceptibility zoning of KGS was obtained with GIS spatial analysis technology. The results show that the susceptible areas can be divided into extreme, high, medium, and weak susceptibility, accounting for 4.93%, 15.30%, 33.21%, and 46.56%, respectively, which are consistent with the distribution density of past KSGs. Furthermore, by selecting the subway construction as a human activity type, it indirectly discusses the influence of land development intensity on KGS. The results show that past KSGs are mainly concentrated in areas with high engineering construction density and significant land development intensity. Based on the above, strategies for regional land development and prevention and control of KGSs are proposed. [ABSTRACT FROM AUTHOR]

Amazon streams present great biodiversity and offer several ecosystem services, but these systems are threatened by multiple land uses. The changes created by land use are expected to drive the composition of species, ultimately changing the trophic relationships of several biological groups, including zooplankton. We investigated if land use changes the composition of zooplankton functional guilds in Amazon streams and which are the local (physical-chemical) variables driving the zooplankton functional guilds in the land-use gradient. Zooplankton and physical-chemical variables were sampled in 17 water bodies in the municipality of Barcarena, Pará, Brazil in 2018 and 2019, five sampling sites were in the Pará River and 12 in streams. Forest cover (a proxy for land use) was determined through digital image processing and converted in percentage. Zooplankton species were classified into five functional guilds (filter, raptorial, scraper, suctor, and predator feeders). We recorded 98 zooplankton taxa and filters were the most abundant functional guild. The composition of zooplankton functional guilds did not change in the land use gradient. However, the distribution of zooplankton functional guilds in Amazon streams was determined by local environmental variables related to the feeding strategies. Scraper-feeders (cladocerans) were positively related to greater canopy cover, suctor-feeders and predator-feeders (both rotifers) were related to greater total phosphorus, whereas filter-feeders (rotifers, cladocerans, and copepods) and raptorial (copepods) were related to total suspended solids. This study brings new information about zooplankton in Amazon streams that are under-studied. The functional approach clarifies the patterns observed and reflects the trophic relationships in which the zooplankton community is involved in streams under a degree of land use, i.e., scraper-cladocerans can represent more preserved streams under greater canopy cover, whereas the other functional guilds were related to variables that can represent more altered streams. [ABSTRACT FROM AUTHOR]