Spatial Distribution Of Land Use Research Articles

Study on the Dynamic Change of Land Use in Megacities and Its Impact on Ecosystem Services and Modeling Prediction

Under the background of rapid urbanization, strengthening the research on the response and dynamic mechanism of ecosystem services to land use is conducive to the optimization of land space and ecological restoration and governance in megacities. Using Hefei City as a case study, we examined specific ecosystem services and analyzed how water yield, habitat quality, carbon storage, and soil conservation changed over time from 2000 to 2020. We utilized spatial information technology and the InVEST model to assess these changes. Additionally, we developed a comprehensive ecological service index (CES) and used Geodetector and regression models to investigate how ecosystem services responded to land use. In addition, we utilized the Patch-generating Land Use Simulation Model (PLUS) to simulate the spatial distribution of land use in 2030. This was performed under four different scenarios: natural development (ND), urban development (UD), cultivated land protection (CP), and ecological protection (EP). Furthermore, we assessed the effects of these land-use changes on ecosystem service functions by integrating the PLUS results with InVEST. The findings indicate the following: (1) between 2000 and 2020, farmland consistently remained the dominant land-use type in Hefei City while construction land experienced significant growth. Land-use conversion was prevalent during this period, and each ecological indicator exhibited noticeable geographic variation; (2) during the past 20 years, the comprehensive ecosystem service index (CES) exhibited clear spatial clustering patterns. The different types of land use showed significant quantitative relationships with CES. Specifically, cultivated land, forest land, grassland, and water area had positive correlations, while construction land had a negative correlation. Geodetector analysis revealed that the proportion of ecological land use had the greatest impact on the spatial differentiation of CES, followed by population density; (3) according to the PLUS simulation, the UD scenario results in a significant conversion of cultivated land and grassland into construction land, leading to the greatest decrease in CES. In the ND scenario, the areas with decreasing CES are mostly areas that have been converted from other land types to construction land. In contrast, the EP scenario shows an increase in forest land and grassland, which promotes the enhancement of multiple ecosystem service functions simultaneously. This indicates that the EP scenario is the most favorable for sustainable land-use development. The study investigates the impact of land-use changes on ecosystem services and evaluates the sustainability of regional land use. The findings have both theoretical and practical significance for effectively managing land use and regulating ecological functions in large cities.

Evolution characteristics and multi-scenario prediction of habitat quality in Yulin City based on PLUS and InVEST models

As a major energy city in China, Yulin City has faced huge challenges to the ecological environment with its rapid economic development and rapid urbanization. Therefore, it is of great significance to study the impact of land use changes on habitat quality. Based on three periods of land use data in Yulin City in 1995, 2005 and 2015, the PLUS model was used to simulate the land use changes in 2015. The measured kappa coefficient was 0.8859, which met the simulation accuracy requirements. By setting development zone boundaries and adjusting parameters, three progressive scenarios are designed to predict the spatial distribution of land use in Yulin City in 2035. The InVEST model was used to analyze the spatiotemporal evolution of Yulin City’s habitat quality in the past 20 years and evaluate the distribution of Yulin City’s habitat quality under three scenarios after 20 years. The results are as follows: (1) During the study period, construction land in Yulin City expanded rapidly, with an area increase of 380.87 km2 in 20 years, and ecological land gradually shrank. (2) The land use simulation results of Yulin City under various scenarios in 2035 show that future land use changes in Yulin City will mainly be concentrated in the central and western regions. (3) During the study period, the habitat quality of Yulin City was at a medium level and the overall habitat quality showed a downward trend. Spatially, the degree of habitat quality degradation in Yulin City showed a characteristic of gradually decreasing from West to East. (4) By 2035, under the scenario of suitable urban economic development, Yulin City’s habitat quality has been improved to a certain extent, which not only protects ecological security but also meets the demand for construction land for urban development. The results of this study help the government better understand the evolution of land use and habitat quality in Yulin City in the past 20 years, and provide theoretical support and reference for the formulation of Yulin City’s ecological environment protection policies and the implementation of ecological protection work under the current land spatial planning.

A comprehensive analysis and future projection of land use and land cover dynamics in a fast-growing city: A case study of Sekondi-Takoradi metropolis, Ghana

Sekondi-Takoradi is the most urbanized metropolis in the Western region of Ghana. Over the past two decades, it has experienced rapid shifts in both population density and land cover. Unfortunately, these transformations have significantly altered the natural landscape and expanded urban coverage. Previous studies have not established enough evidence on land use and land cover changes in the metropolis. For this reason, this study examined the existence, rate, and spatial distribution of land use and land cover as well as the future changes in land cover in the metropolis. Landsat imagery spanning from 1991, 2009, 2016, to 2023 provided the basis for evaluating the spatiotemporal dynamics of land use and cover within the metropolis. The Random Forest Classification algorithm was employed to categorize the images into three distinct classes: water, vegetation, and built-up areas. Analysis revealed a rapid growth in built-up areas by 63.08 km² (32.91 %), accompanied by a decrease in vegetation and water coverage by 60.99 km² (31.82 %) and 2.08 km² (1.09 %) respectively. This highlights the accelerating urbanization trend, emphasizing the critical need for vigilant monitoring and controlled urban expansion to mitigate potential adverse effects. Projections for 2030 indicate further changes with water areas decreasing to 1.21 km² (0.63 %), vegetation diminishing to 95.31 km² (49.73 %), and built-up areas expanding to 95.14 km² (49.64 %). This signifies a significant shift towards built-up areas, altering the metropolis' land cover composition and potentially impeding the achievement of Sustainable Development Goal 11. Understanding these changes in land use and land cover holds significant value for policymakers, enabling them to effectively monitor and safeguard natural resources.

Analysis and Simulation of Land Use Changes and Their Impact on Carbon Stocks in the Haihe River Basin by Combining LSTM with the InVEST Model

The quantitative analysis and prediction of spatiotemporal patterns of land use in Haihe River Basin are of great significance for land use and ecological planning management. To reveal the changes in land use and carbon stock, the spatial–temporal pattern of land use data in the Haihe River Basin from 2000 to 2020 was studied via Mann–Kendall (MK) trend analysis, the transfer matrix, and land use dynamic attitude. Through integrating the models of the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) and the Long Short-Term Memory (LSTM), the results of the spatial distribution of land use and carbon stock were obtained and compared with Cellular Automation (CA-Markov), and then applied to predict the spatial distribution in 2025. The results show the following: (1) The land use and land cover (LULC) changes in the Haihe River Basin primarily involve an exchange between cultivated land, forest, and grassland, as well as the conversion of cultivated land to built-up land. This transformation contributes to the overall decrease in carbon storage in the basin, which declined by approximately 1.20% from 2000 to 2020. (2) The LULC prediction accuracy of LSTM is nearly 2.00% higher than that of CA-Markov, reaching 95.01%. (3) In 2025, the area of grassland in Haihe River Basin will increase the most, while the area of cultivated land will decrease the most. The spatial distribution of carbon stocks is higher in the northwest and lower in the southeast, and the changing areas are scattered throughout the study area. However, due to the substantial growth of grassland and forest, the carbon stocks in the Haihe River Basin in 2025 will increase by about 10 times compared with 2020. The research results can provide a theoretical basis and reference for watershed land use planning, ecological restoration, and management.

Mapping and classification of Liao River Delta coastal wetland based on time series and multi-source GaoFen images using stacking ensemble model

The precise mapping of coastal wetlands holds great significance for monitoring carbon sequestration and storage within coastal ecosystems, particularly in light of climate change and human-induced activities. Time series and multi-source remote sensing data offer distinct advantages in spatial and temporal land use mapping, particularly in wetland systems encompassing various vegetation types. The primary aim of this study was to delineate the spatial distribution of land use within the Liao River Delta (LRD) wetland. This was achieved by employing a stacking ensemble model that integrates time-series GaoFen-1 (GF-1) optical imagery, GaoFen-3 (GF-3) synthetic aperture radar (SAR) imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) imagery. The first step involved the application of an enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to fuse the GF-1 NDVI and MODIS NDVI datasets, producing time-series NDVI data. Subsequently, the parameters pertaining to vegetation phenology were obtained by employing the threshold method on the time-series NDVI data. We compiled feature datasets encompassing GF-1 spectral bands, indices, phenological parameters, and GF-3 SAR features. A Recursive Feature Elimination and Cross-Validation (RFECV) model was employed to identify and select significant features to mitigate data redundancy. Finally, a stacking ensemble model was constructed by combining five base models [K-Nearest Neighbors (KNN), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and a Light Gradient Boosting Machine (LightGBM)] to perform wetland classification. The findings were as follows: (1) ESTARFM was able to successfully fuse GF-1 NDVI and MODIS NDVI data, resulting in spatiotemporal fusion with a coefficient of determination (R2) of 0.85 and a root mean square error (RMSE) of 0.07. (2) the RFECV algorithm was employed to select relevant features in the wetland classification process. Specifically, 75 spectral band features, 89 spectral index features, 13 SAR features, and seven phenological parameters were identified as significant for this task. (3) A stacking ensemble model was constructed using the aforementioned multi-source features. This model exhibited a robust and consistent performance in wetland classification, achieving the highest overall accuracy of 94.33%. Notably, this accuracy improvement ranged from approximately 0.09% to 10.02% compared to the individual base models. Thus, the present study has the potential to be utilized for fine-scale wetland monitoring, thereby offering valuable assistance in the field of wetland environmental research.

Optimal allocation system of urban land use types under multi-objective constraints

Abstract This paper arranges suitable land use types according to the quality characteristics of land parcel units, obtains the objective function of optimal land use allocation, and constructs a multi-objective constraint system in the process of land use planning. The integer vector is used to encode the land use types and represent the spatial distribution of land use, combined with the particle swarm algorithm to calculate its adaptability, speed and position update based on the initial population size and construct the land use optimization allocation model based on the constraint system. The particle swarm algorithm is used to explore the geographic environment and climate resources, the economy of the natural economy of the study area, and Landsat TM remote sensing image technology is used to obtain the initial data on the land use of the study area and empirical analysis is carried out on the optimization of the allocation of urban land use in the study area. The results show that the algorithm converges faster and achieves global convergence when the parameter is set to the fourth state, and the comprehensive suitability evaluation function achieves global convergence at generation 73, with a value of 1.64E+08, which is faster than the convergence of parameter settings 1 and 2 as well as 3. The land types that increase in area include garden land, forest land, transportation land, and water and water conservancy facilities land, which increase by 0.8%, 2.84%, 0.14%, and 0.8%, respectively, which protects the arable land and restricts the expansion of the construction land in terms of the quantitative structure. This study enriches the content and methods of optimizing land use space allocation, which helps to rationalize the use of land resources.

Coupled MOP and PLUS-SA Model Research on Land Use Scenario Simulations in Zhengzhou Metropolitan Area, Central China

Land use simulations are critical in predicting the impact of land use change (LUC) on the Earth. Various assumptions and policies influence land use structure and are a key factor in decisions made by policymakers. Meanwhile, the spatial autocorrelation effect between land use types has rarely been considered in existing land use spatial simulation models, and the simulation accuracy needs to be further improved. Thus, in this study, the driving mechanisms of LUC are analyzed. The quantity demand and spatial distribution of land use are predicted under natural development (ND), economic development (ED), ecological protection (EP), and sustainability development (SD) scenarios in Zhengzhou based on the coupled Multi-Objective Programming (MOP) model and the Patch-generating Land Use Simulation model (PLUS) considering Spatial Autocorrelation (PLUS-SA). We conclude the following. (1) The land use type in Zhengzhou was mainly cultivated land, and 83.85% of the land for urban expansion was cultivated land from 2000 to 2020. The reduction in forest from 2010 to 2020 was less than that from 2000 to 2010 due to the implementation of the policy in which farmland is transformed back into forests. (2) The accuracy of PLUS-SA was better than that of the traditional PLUS and Future Land Use Simulation (FLUS) models, and its Kappa coefficient, overall accuracy, and FOM were 0.91, 0.95, and 0.29, respectively. (3) Natural factors (temperature, precipitation, and DEM) contributed significantly to the expansion of cultivated land, and the increase in forest, grass, and construction land was greatly affected by socioeconomic factors (population, GDP, and proximity to town). (4) The land use structure will be more in line with the current requirements for sustainable urban development in the SD scenario, and the economic and ecological benefits will increase by 0.75 × 104 billion CNY and 1.71 billion CNY, respectively, in 2035 compared with those in 2020. The PLUS-SA model we proposed had higher simulation accuracy in Zhengzhou Compared with the traditional PLUS and FLUS models, and our research framework can provide a basis for decision-makers to formulate sustainable land use development policies to achieve high-quality and sustainable urban development.

Characteristics of Spatiotemporal Variations and Driving Factors of Land Use for Rural Tourism in Areas That Eliminated Poverty

This study explores the spatiotemporal characteristics and driving factors of land use for rural tourism in areas that eliminated poverty from 2009 to 2021. It puts forward targeted governance measures to promote the high-quality development of rural tourism, poverty alleviation, and rural revitalization. The analysis is based on exploratory spatial analysis methods and geographical detectors. The results show that (1) the overall level of land use for rural tourism was low but grew very quickly with large regional differences. (2) There was a significant spatial agglomeration in land use for rural tourism land. The spatial distribution of land use for rural tourism landscapes was characterized by two cores and four clusters, while spatial distribution of rural tourism facilities was characterized by one cluster with multiple branches. (3) The driving factors of spatial variations in land use for rural tourism were diverse and dynamic. Dominant factors shifted from natural conditions and geographical location to socioeconomic and tourism resources and regional policy dimensions. Policy should emphasize the development of the rural tourism industry, innovate the diversified “tourism + development” model, enhance the level of land use for rural tourism, broaden avenues for farmers to increase their income, and strengthen residents’ motivation for development.

A Spatio-Temporal Analysis of the Urban Fabric of Nuremberg From the 1940s Onwards Using Historical Maps

As one of the most heavily bomb-damaged cities in Germany, with around 90% of its historic city centre destroyed, Nuremberg (<em>Nürnberg</em>) provides an excellent example to investigate the urban transformation of a post-war city. In this article, we bring together heterogeneous and under-researched data sets and archival material from the post-war period and convert urban features depicted in historic maps and scanned documents into digital geospatial data that is analyzed with a geographical information system. We combine morphological variables of townscape analysis to present three different transformations over time. First, using a damage map of Nuremberg from the Second World War, we examine the varying extent of bomb damage across the city at the detailed district level. Secondly, we focus on land-use units, comparing the pre-war spatial land-use distribution from 1940 with historical maps of land use/cover from 1956 and more recent land uses in 1969. Finally, using selected characteristics of urban form, we categorize pre-war and present-day urban block typologies to examine urban morphological change. In doing so, we contribute methodologically and substantively towards a new framework for the analysis of post-war cities. We demonstrate how geographical information systems can be utilized for historical research and the study of change in urban environments, presenting a map-based interpretation of the planning strategies to have guided post-war urban development in Nuremberg. Providing an alternative appraisal of post-war city transformation, our diachronic research offers insight into Nuremberg’s under-researched past, which is also of interest to planners and policymakers seeking to improve future cities.

The Spatio-Temporal Patterns and Driving Forces of Land Use in the Context of Urbanization in China: Evidence from Nanchang City.

Land use change has been one of the common problems in the context of urbanization in China. Social economy and land use interact with each other, and it is especially important for human society to adhere to sustainable development, and to deal with the contradictory relationship between the social-economic needs and land use change. The objectives of this study are: (1) Obtain time-series land-use classification data and its spatial distribution in Nanchang City; (2) Identify the characteristics and driving force of spatial-temporal land use changes in Nanchang City from 2000 to 2020; (3) Discuss the relationship between the urban expansion and social economy in Nanchang City. The results show that the spatial distribution of land use in Nanchang City has changed significantly from 2000 to 2020, and the largest area of land-use type in Nanchang City has been cropland. The cropland has continuously declined, and the urban area has increased significantly. A lot of cropland has been transformed into urban areas, and land use degree in Nanchang City has significantly increased. The spatial pattern of land use has greatly changed, and the city spatial pattern has become more aggregated, while the spatial distribution of cropland, forest and grassland has become more fragmented. Moreover, there has been an obvious correlation between social-economic development and the level of land use, and GDP has been the main driver of land use change. The central urban area of Nanchang city has been the main hotspot of land use change.

Spatial and Temporal Evolution of Land Use and the Response of Habitat Quality in Wusu, China.

Understanding land use change and its impact on habitat quality (HQ) is conducive to land use management and ecological protection. We used the InVEST model and the GeoDetector model to explore the land use and HQ of Wusu from 1980 to 2020. We found that the spatial distribution of land use in Wusu had the most dramatic change from 2000 to 2010, and accordingly, the habitat quality deteriorated seriously from 2000 to 2010. Via correlation analysis, the response of HQ to land use change is obvious, among which the negative effect of forest land to construction land is the largest, and the positive effect of construction land to water is the largest. However, the overall HQ had the largest negative response to the change of grassland to arable land, and the largest positive response to the change of unused land to grassland. Of the driving factors that cause land use change and thus affect HQ, the human factors are the strongest, and the negative impact on HQ is more irreversible. This study can provide a scientific basis for land use management and ecological protection in Wusu, and can help to further promote the exploration of human activities and ecological responses in arid and semi-arid areas.

Indirect interactions between climate and cropland distribution shape fire size in West African grasslands

ContextClimate and land use changes often interact, yet our ability to predict their combined effects on biodiversity is currently limited. In particular, the combined effects of climate and land use on key ecosystem dynamics, such as disturbance regimes, that shape biodiversity across large spatial scales, are poorly understood.ObjectivesWe assess how indirect climate–land use interactions influence disturbance regimes by examining the mechanistic pathways by which climate and proximity to cropland interact to shape fire size in a West African grassland ecosystem, the W-Arly-Pendjari transboundary protected area complex.MethodsWe use remotely sensed indicators of burned area, rainfall, cropland distribution, and vegetation dynamics to test two spatially explicit hypotheses about the interaction between climate and land use effects on fire dynamics.ResultsWe demonstrate that in areas where wet season grass production (which is driven by rainfall) is higher, fires are larger, but that this relationship depends on the distance to cropland. Close to cropland, environmental drivers of fire size (wet season grass production, and progressive loss of fire fuel during the fire season) have little effect on fire size, as fuel breaks induced by cropland limit fire size.ConclusionOur results suggest that the extent to which climate factors control fire dynamics in this African grassland depends on the spatial distribution of land use. More broadly, considering the relative spatial distributions of interacting stressors may be key to improving predictions of their combined impacts on ecosystem functioning.

Temporal and spatial variation analysis of habitat quality on the PLUS-InVEST model for Ebinur Lake Basin, China

The improvement of habitat quality is of great significance to biodiversity conservation. Irrational development activities lead to the degradation of habitat quality and loss of biodiversity, especially in arid inland areas where the ecological environment is fragile. In this study, the InVEST model is used to evaluate the habitat quality and degradation degree of the Ebinur Lake Basin from 1990 to 2020, while the PLUS model is used to predict the land use change in the Ebinur Lake Basin under various scenarios. Combined with the results of ecological zoning planning, the spatial distribution of land use and habitat quality under the inertia development (ID) and ecological protection (EP) scenarios in 2030 were compared and analyzed, and the optimal scenario was selected. The following four conclusions are drawn: (1) From 1990 to 2020, cultivated land, grassland, and construction land significantly increased, while forest land, water bodies, and unused land significantly decreased. (2) The habitat quality of the watershed showed an increasing trend followed by a decreasing trend. The area proportion of high habitat quality has always exceeded 50 % of the total area of the watershed with a high level of habitat quality. (3) Furthermore, combined with habitat quality and the human footprint index (HFI), ecological zoning planning for the basin was conducted. The three ecological zones were divided into key protected areas, key restoration areas, and moderate development zones, with area proportions of 53.69 %, 29.37 %, and 16.93 %, respectively. (4) By comparing habitat quality under the ID scenario and the EP scenario in 2030, we found that the area of high habitat quality increased by 2730.06 km2 under the EP scenario, which improved the overall level of habitat quality, and was more conducive to the ecological security protection of the basin. The results of this study may benefit the analysis of the temporal and spatial variation characteristics of habitat quality in the Ebinur Lake Basin and provide a reference for the improvement of habitat quality in the inland arid region.

Impact assessment of land use on water scarcity in urban areas: case of Jaffna Peninsula, Sri Lanka

Abstract Urbanization-led land use changes have significant effects on urban water problems as water scarcity is a common challenge for planning of cities today. Chunnakam aquifer in Jaffna, Sri Lanka has been significantly depleted and polluted in the past decade. This study assessed the impact of spatial distribution of land use on water quality and quantity using 44 dug wells. Water scarcity index (WSI), water quality index (WQI) and multiple linear regression (MLR) models were used to explore the spatiotemporal patterns of water quality, quantity and their relationship. Results revealed that 75% of the aquifer faced scarcity problems while 54% was polluted and not suitable for consumption. Moreover, water sources located around agricultural and residential land use were contaminated in line with the spatial distribution of crop types, fertilizer use and domestic waste disposal. Integration of water policy with urban development strategy and multi-stakeholder engagement for water conservation are important findings for ensuring the water security of cities in developing countries. This study can be further expanded by evaluating temporal effects of urbanization on water resources for sustainable spatial planning. Strong water policies must govern the land use change to ensure water security for arid regions in developing countries.

The Evolution of Ecological Space in an Urban Agglomeration Based on a Suitability Evaluation and Cellular Automata Simulation

Changing and reconstructing the ecological space of urban agglomerations is inevitable for ecological conservation and a scientific problem that needs urgent attention from geography, ecology, and urban and rural planning. Using ArcGIS and other software for data processing, this study established a spatial attribute database, constructed a land use conversion matrix of the Changsha-Zhuzhou-Xiangtan (CZX) urban agglomeration’s ecological space, and quantitatively analyzed the main changes in ecological land. Using a trained cellular automata model with predicted land use in 2035 as the threshold value, the simulation research was presented by creating two simulation scenarios for the spatial distribution of land use by 2035 in the “Green Heart” area of the CZX urban agglomeration. The simulation results were compared, and the constraining role of land use suitability evaluation on ecological space evolution was analyzed. This study found that the total area of ecological space in the Green Heart area saw a rapid reduction, and it predicted that, by 2035, the total area of the CZX Green Heart area will have decreased. Comparing the two simulation scenarios proved the hypothesis that zoning ecological space reconstruction based on a land suitability evaluation can effectively protect ecological space and ensure ecological network functions are harnessed.

Analyzing the characteristics of land use distribution in typical village transects at Chinese Loess Plateau based on topographical factors

Abstract The spatial distribution of land use at different terrain gradients has been used as an important index for the land management and ecological restoration in the Loess Plateau of China. Based on the land use data surveyed in 2015 and digital elevation model data with a resolution of 30 m from typical village transects in the Loess Plateau area in Yan’an City, Shaanxi Province, this study analyzed the terrain niche index, distribution index, land utilization comprehensive index, and land use equilibrium degree for four village transects. The results demonstrated that the land use types in the four village transects were mainly grassland, forest land, and cropland. Land use types showed obvious differences in respect to topographical gradient with built-up land, traffic land, water bodies, cropland, and orchard distributed in low terrain gradient areas, forest land, and grassland distributed in medium- and high-terrain areas. As terrain gradient increased, the land utilization comprehensive index and land use equilibrium degree showed a generally decreasing trend. These findings may provide a useful reference for land use planning and land resource allocation in the Loess Plateau region.

Spatial–Temporal Characteristics and Driving Mechanisms of Land–Use Transition from the Perspective of Urban–Rural Transformation Development: A Case Study of the Yangtze River Delta

Urban–rural transformation development is the key to resolving the imbalance in the dual structure of urban and rural areas. However, the transformation of the urban–rural relationship will also affect the structure and spatial distribution of land use. This paper measured the spatial–temporal characteristics of land–use transition in the Yangtze River Delta from 1990 to 2018 by using a geo–information Tupu method and explored the driving mechanism of land–use transition under the background of urban–rural transformation development by using a spatial regression analysis method. The results showed the following: (1) The transition from cultivated land to urban construction land, from rural residential land to cultivated land, and from rural residential land to urban construction land were the three main types of land–use transition in the Yangtze River Delta during urban–rural transformation development. (2) The transition from cultivated land to urban construction land was always the most important type of land–use transition. It expanded from the central area to the surrounding cities. The transition of rural residential land to cultivated land and urban construction land began to increase significantly after the year 2010, which was the urban–rural integration development period. (3) The urban–rural land–use transition was driven by government policies, industrial restructuring, population urbanization and migration. During the urban–rural integration development period, secondary industry and tertiary industry were the main driving factors of the transition from cultivated land to urban construction land. The number of policies, the primary industry, the total population, and the urbanization rate were the main driving factors of the transition from rural residential land to cultivated land. Primary industry, secondary industry, and tertiary industry were the main driving factors of the transition from rural residential land to urban construction land. Finally, the study provided some suggestions for policy, industry, and population driving forces.

The Evolution Pattern and Simulation of Land Use in the Beijing Municipal Administrative Center (Tongzhou District)

Beijing Municipal Administrative Center (Beijing MC) in Tongzhou District has inherited the non-capital core functions of Beijing's central urban area, and its rapid construction and development urgently require a scientific understanding of the pattern of land use evolution in the region. This paper analyzes the pattern of land use evolution in Tongzhou District over the past 40 years, from 1980 to 2020. According to the historical evolutionary characteristics of land use and urban development planning goals, combined with the driving factors of cultural tourism development, the Future Land-use Simulation (FLUS) model is used to simulate the spatial distribution of land use in Beijing MC (Tongzhou District) in 2035 under three scenarios of urbanization acceleration, deceleration and sustainable development. The results show three major trends. (1) Beijing MC (Tongzhou District) is dominated by urban development and construction. During the high-speed urbanization stage from 1980 to 2010, the urban expansion pattern of “along the Sixth Ring Road and along the Grand Canal” was formed. During the low-speed urbanization stage from 2010 to 2020, the land distribution was stable, and Tongzhou District formed a pattern of urban-rural differentiation and land intensification from northwest to southeast. As a typical area of Tongzhou District's urbanization, Beijing MC has the same characteristics of the temporal and spatial evolution as Tongzhou as a whole. (2) By 2035, there are significant differences in land use among the three scenarios with respect to the magnitude of change and spatial distribution. The area and distribution of ecological land under the urban sustainable development scenario are optimal, which is conducive to the realization of sustainable urban development. In analyzing the degree of conformity with the three Beijing MC zoning plans, the prediction simulation under the sustainable development scenario is highly consistent with the land use of the “Beijing Municipal Administrative Center Regulatory Detailed Planning (Block Level) (2016–2035)” (hereinafter referred to as “Planning”) issued by the municipal government. However, there are certain deviations between the simulation predictions in the cultural tourism function area and the livable living scenery area and the corresponding “Planning” expectations. During the urban construction process, the internal ecological land area still needs to be increased. (3) Tongzhou District may lack a close connection between the urban and rural areas in the southeast. Potential risks such as the imbalance in the development of northern and southern townships require further attention in the development process. The prediction and simulation results of the model can provide certain data and methodological support for the construction of a harmonious and livable city in Beijing MC (Tongzhou District).

Spatial and Temporal Variation, Simulation and Prediction of Land Use in Ecological Conservation Area of Western Beijing

Exploring land use change is crucial to planning land space scientifically in a region. Taking the ecological conservation area (ECA) in western Beijing as the study area, we employ ArcGIS 10.2, landscape pattern index and multiple mathematical statistics to explore the temporal and spatial variation of land use from 2000 to 2020. Patch-generating Land Use Simulation (PLUS), Future Land Use Simulation (FLUS) and Markov models were used to simulate and predict the current land use in 2020. The models were evaluated for accuracy, and the more accurate PLUS model was selected and used to simulate and predict the potential land use in the study area in 2030 under two management scenarios. The main findings of this research are: (1) From 2000 to 2020, the construction land increased constantly, and the area of cultivated land and grassland decreased significantly. (2) For predicting the spatial distribution of land use in the study area, the PLUS model was more accurate than the FLUS model. (3) The land-use prediction of the study area in 2030 shows that the area of grassland, forest and water is approximately equal to their corresponding value in 2020, but the construction land increased constantly by occupying the surrounding cultivated land. According to this research, the continuous decrease of cultivated land in favor of increasing construction land will cause losses to the ecological service function of the ECA, which is not beneficial to the sustainable development of the region. Relevant departments should take corresponding measures to reduce this practice and promote sustainable development, particularly in the southern and western areas of the ECA where there is less construction land.

Land use optimization of rural production–living–ecological space at different scales based on the BP–ANN and CLUE–S models

Rural production–living–ecological (PLE) space is the essential carrier of China's rural land resource planning and management. However, the pattern identification, optimal prediction, and multi–scale integration of rural PLE space lack sufficient scientific evidence and reliable quantitative analysis. To fill this gap, this paper proposes a multi-scale land use optimization method based on benefit coupling evaluation, BP–ANN and CLUE–S models. The typical hilly area of the upper reaches of the Yangtze River in southwest China were used as a case for empirical study. The results showed that there was a high correlation between land use patterns and benefits. The key to land use optimization in hilly areas is to increase the area of ecological land and improve the capacity of regional ecosystem services. Through the evaluation of the degree of comprehensive benefit coupling, 44 sample towns with an optimal land use pattern were effectively identified to construct the optimization model. At the regional scale, the BP-ANN model predicted the optimal ratio of land use structure for each township unit based on natural, social, and economic influencing factors. The optimization results reduced production land by 8.94% on average, increased ecological land by 9.2% on average, and kept living land relatively stable. The proportions of production, living, and ecological land were adjusted to 59.85%, 8.34%, and 31.81%, respectively, which can better meet the land space demand for food security and ecological protection in the future. Regarding the smaller scale, the CLUE-S model took the regional-scale optimization results as the goal to simulate the spatial distribution of land use. The proportion of production, living, and ecological land in the town of Taiping was optimized from the previous 88.17%, 6.25%, and 5.58% to 62.92%, 7.83%, and 29.25%, respectively. The optimization results not only ensure the stability of high-quality cultivated land, but also effectively improve ecological functions such as soil conservation and water purification. This novel method was proven to be effective for quantitative optimization of land use and multi-scale functional space cohesion and integration, providing scientific support for the sustainable use of rural land resources in China.