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Understanding the spatial distributions of multiple ecosystem services (ESs), their associations, and their underlying socio-ecological contributing factors is critical for ES management. Using the city belt along the Yellow River in Ningxia, northwestern China, as a case study, this study quantified the spatial distribution of six ESs (food production, carbon sequestration, carbon storage, nutrient retention, sand fixation and recreational opportunity), analyzed the synergy and trade-off relations among them through correlation analysis, classified ES bundles through a self-organizing map method (SOM), explored the impacts of socio-ecological factors on the ESs through Ordinary Least Square regression (OLS) and Geo-detector analysis, delineated socio-ecological clusters using the SOM, and characterized the relationship between ES bundles and driver clusters through overlap analysis. The results suggest that spatial associations among ESs can be predicted by their driving mechanisms. Synergy relations existed among crop production, carbon sequestration, carbon storage and nutrient retention, and these were impacted by similar driving mechanisms. Synergy also existed between sand fixation and recreational opportunity, but significant differences existed in their driving mechanisms. Trade-off relations were shown between ESs in these two groups at the whole region scale. Three bundles were detected among the six ESs: bundle 1, characterized by recreational opportunity of high supply and other services of limited supply, was located in the transitional region between the central plain and the fringe mountains, and mainly driven by climate and proximity factors; bundle 2, characterized by high sand fixation, medium carbon storage and limited other services, was located in the northwestern and southern mountains and driven by climate and geography factors; bundle 3, characterized by high food production, carbon sequestration, carbon storage and nutrient retention of medium supply and other two services of limited supply, was located in the central plain and driven by vegetation coverage and proximity factors. Human activities can partly overcome the limitations of ecological conditions, thus specific strategies for different regions are proposed to maintain and improve ESs under global climate change. • Spatial correlations among six ecosystem services were assessed. • Their spatial co-variation were delineated and mapped using self-organizing map. • Their socio-ecological influencing background were detected. • Spatial associations among ecosystem services can be predicted by their driving mechanisms. [ABSTRACT FROM AUTHOR]

Many countries have been constructing modern ground transportation projects. This raises questions about the impacts of such projects on development of impervious surfaces, yet there have been few attempts to systematically analyze these impacts. This paper attempts to narrow this information gap using the Hangzhou Bay Bridge project, China, as an exploratory case study. Using remotely sensed data, we developed a framework based on statistical techniques, wavelet multi-resolution analysis and Theil-Sen slope analysis to measure the changes in impervious surfaces. The derived changes were then linked to the bridge project with respect to socio-economic factors and land use development activities. The findings highlight that the analytical framework could reliably quantify the area, pattern and form of new urban area and urban intensification. Change detection analysis showed that urban area, GDP and the length of highways increased moderately in the pre-Hangzhou Bay Bridge period (1995–2002) while all of these variables increased more substantially during (2002–2009) and after (2009–2013) the bridge construction. The results indicate that the development of impervious surfaces due to new urban area came at the expense of permeable surfaces in the urban fringe and within rural regions, while urban intensification occurred mainly in the form of the redevelopment of older structures to modern high-rise buildings within existing urban regions. In the context of improved transportation infrastructure, our findings suggest that new urban area and urban intensification can be attributed to consecutive events which act like a chain reaction: construction of improved transportation projects, their impacts on land use development policies, effects of both systems on socio-economic variables, and finally all these changes influence new urban area and urban intensification. However, more research is needed to better understand this sequential process and to examine the broader applicability of the concept in other developing regions. [ABSTRACT FROM AUTHOR]

Abstract: China''s “building a new countryside” strategy for coordinating urban and rural development and gearing up national economic growth brings challenges to the country''s farmland protection. The objective of this study is to evaluate potential impacts of implementing the strategy on farmland and to provide scientific guidelines and decision support for decision makers in northeast China. We analyzed three “building a new countryside” implementation scenarios (Historical Trend, Intensive Development, and Extensive Development) using the SLEUTH urban growth and land cover change model in combination with remote sensing and GIS analysis. The results indicated that farmland loss was inevitable, but revealed large differences in landscape patterns and the amount of farmland loss among the three BNC implementation scenarios. The Extensive Development scenario showed the largest increase in urban and rural residential land, the highest level of landscape fragmentation, and the largest loss of farmland. Farmland loss under the Intensive Development scenario is higher than that under the Historical Trend scenario; however, urban and rural sprawl and the fragmentation of landscape under the Intensive Development scenario were lower than those under the Historical Trend scenario. Consequently, the Intensive Development scenario was recommended for actual “building a new countryside” implementation in the study area. Potential rural sprawl under the Intensive Development scenario was also discussed, which provided useful information for guiding scientific-based decision support and policy making. While most studies of sprawl prediction involve urban centers only, our study presents a case of predicting urban and rural sprawl simultaneously. [Copyright &y& Elsevier]

• Open space and urban land are interactively attractive in the simulation. • Simulated OS is evaluated with walking accessibility and population coverage rate. • Different mean sizes and time-lags of OS are considered in the simulation. • OS-CA is an effective tool for assessing the policies for creating new OS. Open spaces (OSs) in urban areas play a vital role in providing space for habitat conservation and recreation, improving the mental health of urbanites. OSs consist of complicated elements, such as parks, green landscapes and public squares. Few studies have simulated the dynamics of OS with a complete set of these elements. This study proposes a new Open-Space simulation model using cellular automata (OS-CA). A system dynamics model is used to generate scenarios for creating new OS with different construction time-lags. The spatial simulation of OS is allowed to interact with the urban dynamics, constrained by the policies controlling different mean sizes of new OSs. The effectiveness of creating new OS is assessed using the walking accessibility and the population coverage rate. The OS-CA was applied to Dongguan, South China, and validated with multi-source data. The OS simulation from 2015 to 2030 shows that the longer time-lag in building new open spaces, the higher the demand for open space will be in the future. We found that new open spaces are most likely to appear at places near rivers, forests, wetlands, lakes, along the roads, road intersections, or near residential areas. With the growth of OS, the increase rate of OS population coverage becomes slower than that of walking accessibility. We suggest that governments building more small OS within the central urban areas while constructing larger and fewer OS outside of the core urban areas to help improve the overall service level of OS. The OS-CA is available for download at https://github.com/HPSCIL/Open-Space-Cellular_Automata. [ABSTRACT FROM AUTHOR]

• Spatial relationships among five ecosystem services were quantified. • Random forest analysis was used to identify their socio-ecological drivers. • Topography plays an increasing role in sand fixation during 1989–2017. • Socioeconomic development alters ecosystem service relationships. • Land use pattern and vegetation coverage are critical in policy making. Understanding the dynamics and relationships among ecosystem services (ESs) and their underlying socio-ecological driving mechanisms has great importance for policy-makers to achieve sustainable development. In this study, using the City Belt along the Yellow River in Ningxia, China as the study area, we quantified and mapped five ESs for the period 1989–2017, analyzed the spatial relationships among ESs through hotspots analysis, correlation analysis and cluster delineation, and explored the dynamic impacts of socio-ecological factors on ESs through random forest analysis. The results showed that spatial distributions and relationships among ESs had significant change over the past three decades, driven by the varied impacts of socio-ecological factors. Cropland in the central plain could supply higher values of food production, carbon sequestration and nutrient retention, which were mainly impacted by land use pattern and vegetation coverage and existed significant synergies among them at regional scale. Specially, the hotspots of food production and carbon sequestration both decreased in the northern part over time, while the former one was caused by the increasing impacts of socioeconomic indicators and the latter one by climate factors. Northwestern and southern mountains could provide higher values of sand fixation, which was mainly impacted by the increasing impact of topography and decreasing impact of climate factors. Thus hotspots of sand fixation increased in the mountainous areas and decreased in other regions. Socio-economic factors had an increasing impact on recreational opportunity, resulting the transformation of its hotspots from mountains to the central Yinchuan city and transition region between mountains and central plain. Dynamic impacts of socio-ecological factors could alter the spatial relationships among ESs and impact their co-occurrence across space. Specific management strategies have been proposed for different sub-regions to improve ESs under the land-use system reform, ecological civilization and rural revitalization in China. [ABSTRACT FROM AUTHOR]

• A mixed-cell CA (MCCA) is proposed to simulate the structural change of land use. • The cell state, lattice, and neighborhood of CA is re-designed based on mixed cells. • Continuous and quantitative change of multiple land use components can be simulated. • MCCA enables mixed land use studies to leap from static analysis to dynamic simulation. When used for land use change modeling, Cellular Automata (CA) usually assume that each cell has only one land use type at each time step, ignoring the mixed land use structures that are often found in land units. Mixed cells, composed of cover proportions of multiple land use types, provide a new perspective for modeling the spatio-temporal dynamics of mixed land use structures. Simulating land use change with mixed cells is challenging because mixed-cell CAs are fundamentally different from conventional CAs. This study develops a mixed-cell CA (MCCA). The structure of the CA is re-designed based on the cover proportion of land uses, including the representations of cell state, lattice, and neighborhood. The transition rules are automatically constructed by random-forest regression using historical data and a competition mechanism among multiple land use types at the sub-cell scale is proposed. In addition, a mixed-cell figure of merit (mcFoM) accuracy measure is proposed to validate the MCCA. The MCCA was applied to the Wuhan metropolitan area in China, and the results show that the MCCA was able to simulate the subtle changes of land use proportions within land units. The MCCA represents a new breed of geospatial CA models for spatio-temporal dynamics of mixed land use structures, which enables mixed land use research to leap from static analysis to dynamic simulation. The software for MCCA has been made available at https://github.com/HPSCIL/Mixed_Cell_Cellullar_Automata. [ABSTRACT FROM AUTHOR]

Accurate forecasting of future urban land expansion can provide useful information for policy makers and urban planners to better plan for the impacts of future land use and land cover change (LULCC) on the ecosystem. However, most current studies do not emphasize spatial variations in the influence intensities of the various driving forces, resulting in unreliable predictions of future urban development. This study aimed to enhance the capability of the SLEUTH model, a cellular automaton model that is commonly used to measure and forecast urban growth and LULCC, by embedding an urban suitability surface from geographically weighted logistic regression (GWLR). Moreover, to examine the performance of the loosely-coupled GWLR-SLEUTH model, a layer with only water bodies excluded and a layer combining the former with an urban suitability surface from logistic regression (LR) were also used in SLEUTH in separate model calibrations. This study was applied to the largest metropolitan area in central China, the Wuhan metropolitan area (WMA). Results show that the integrated GWLR-SLEUTH model performed better than either the traditional SLEUTH model or the LR-SLEUTH model. Findings demonstrate that spatial nonstationarity existed in the drivers' impacts on the urban expansion in the study area and that terrain, transportation and socioeconomic factors were the major drivers of urban expansion in the study area. Finally, with the optimal calibrated parameter sets from the GWLR-SLEUTH model, an urban land forecast from 2017 to 2035 was conducted under three scenarios: 1) business as usual; 2) under future planning policy; and 3) ecologically sustainable growth. Findings show that future planning policy may promise a more sustainable urban development if the plan is strictly obeyed. This study recommended that spatial heterogeneity should be taken into account in the process of land change modeling and the integrated model can be applied to other areas for further validation and forecasts. • Heterogeneous factors at different scales were integrated into SLEUTH. • Taking spatial heterogeneity into consideration can improve the performance of the SLEUTH model. • Topography, transportation and economic factors are the main driving forces of urban expansion in the Wuhan Metropolitan Area. • Future urban planning policy has the potential to promise urban development that is more sustainable. [ABSTRACT FROM AUTHOR]

Abstract The agent-based modelling (ABM) is commonly used to simulate urban land growth. A key challenge of ABM for the simulation of urban land-use dynamics in support of sustainable urban management is to understand and model how human individuals make and develop their location decisions that then shape urban land-use patterns. To investigate this issue, we focus on modelling the agent learning process in residential location decision-making process, to represent individuals' personal and interpersonal experience learning during their decision-making. We have constructed an extended reinforcement learning model to represent the human agents' learning when they make location decisions. Consequently, we propose and have developed a new agent-based procedure for residential land growth simulation that incorporates an agent learning model, an agent decision-making model, a land use conversion model, and the impacts of urban land zoning and the developers' desires. The proposed procedure was first tested by using hypothetical data. Then the model was used for a simulation of the urban residential land growth in the city of Nanjing, China. By validating the model against empirical data, the results showed that adding agent learning model contributed to the representation of the agent's adaptive location decision-making and the improvement of the model's simulation power to a certain extent. The agent-based procedure with the agent learning model embedded is applicable to studying the formulation of urban development policies and testing the responses of individuals to these policies. Highlights • We construct an agent learning model for urban growth simulation • We assign a computational equation to the payoff function and extend the to-be-reinforced strategy set of RL algorithm • We develop an agent-based procedure with an agent learning model embedded for geo-simulation • The procedure is applicable to studying the formulation of urban policies and testing the responses of individuals to them • Future research should consider embedding social network into learning model and multi-stages learning models [ABSTRACT FROM AUTHOR]