Increasing population, migration, and rural-to-urban transitions are accelerating urbanization globally, permanently transforming natural systems to impervious built environments. These land change processes have fueled discussion on how best to minimize the ecological impact of expanding cities with both land sparing and land sharing strategies being debated [1
]. Land sharing is an urban development strategy that allows sprawling development and results in a mix of land uses, while land sparing policy encourages infill or development near existing urban areas resulting in a separation of land uses. The differing impact of these land-pattern arrangements on agricultural- and resource-based land systems, wildlife habitat and outdoor recreation is still largely unknown [5
]. Despite the important role of urban planning strategies in shaping patterns of development, land change models rarely examine the differential impacts of development patterns at high resolutions [6
], which would enable measuring trade-offs at the scale of ecological (e.g., movement of wildlife) and social functioning (e.g., sufficient lands for local food system). With urban population expected to reach 66% of the global total by 2050 (United Nations projections [9
]), foresight into urban land transitions comparing sprawl and infill strategies over large scales is important for formulating sustainable urban growth strategies in the coming decades.
Spatially-explicit land change simulations offer the ability to estimate future land changes given assumptions of economic trends, population growth, and governance systems. Numerous land change models of varying sophistication have been developed and proven helpful in projecting urban growth (e.g., [10
] SLEUTH, CLUE). A particularly beneficial feature of land change models, especially in the decision support context, is their ability to help explore different scenarios of land management and better understand trade-offs between alternative futures based on different planning interventions [11
]. While land change models have enhanced our understanding of where and when land systems change, their ability to accurately project spatial patterns of change varies [14
]. Challenges exist for representing the socio-ecological interactions and local, context-specific characteristics necessary for approximating complex, fine-scale patterns that are generalizable to larger extents [15
]. In the US, for example, urbanization often occurs in unexpected locations [16
] influenced by land ownership characteristics, tenure relationships, incomes, transportation networks, and policy context [17
]. To date, models have been largely based on optimization algorithms that allocate new urban cells to locations with the highest environmental suitability, (e.g., distance or travel time to central business district (CBD), topography, etc.) [8
]. While agent-based modeling (ABM) frameworks incorporate social factors that may more closely approximate local conditions and resulting patterns of change, their implementation is rarely possible at large, regional to continental extents [18
] due to empirical and computational requirements of incorporating behavioral drivers of land change [20
Successfully capturing local variation also depends on the granularity or resolution (e.g., 1 km cell) at which land change is simulated. While simulation at small extents can capture local-scale developments and patterns by offering high-resolution projections [21
], regional and larger scale analyses often require coarser resolution data due to computational limitations [22
]. Coarser resolution simulations result in crude mixing of land cover/use classes due to cell aggregation schemes (e.g., majority urban, minority grassland classed as urban) that often miss the important ecological patterns and potential neighborhood feedback relevant to land change processes (i.e., spatial feedback). Alternatively, multiscale analyses of land change capture specific location factors that drive the magnitude and pattern of urban development for a given analysis sub-unit [15
Capturing land change at the scale of ecological and social functioning is an important development towards measuring land use and ecosystem services (ES) trade-offs. Fine grained cross-jurisdictional analysis at regional to continental scales can improve the spatial specificity and understanding of these trade-offs [8
]. Patterns of agricultural land fragmentation, for example, influence farming systems by taking highly productive land out of production [13
], and by changing field structure that reduces efficiency [24
]. Different development patterns also influence the movement of wildlife through fragmentation and disturbances [25
]. Aesthetic value is also impacted by patterns of urban development by influencing exposure to proximate natural landscapes [27
In this paper, we simulate urbanization and landscape change at high resolution across the South Atlantic States (SAS) region of the United States and evaluate the loss of productive lands and ecologically significant areas under Status Quo (SQ) and Infill urban growth scenarios using the FUTURES model [21
]. FUTURES (FUTure Urban-Regional Environment Simulation) is a new user-friendly, open source geospatial computer model for simulating urbanization and landscape fragmentation [28
]. It simulates landscape change based on demand for development, local development suitability factors, and a stochastic patch-growing algorithm (PGA). Recent advances to the model leverage parallel computing to produce multi-level projections over large spatial extents at fine granularity. We apply FUTURES to investigate the SAS, a region experiencing rapid population growth and increasing urbanization. We evaluate changes in land use, farmland, and ecologically significant areas that provide valuable wildlife corridors and habitat. Our overarching goal is to demonstrate a framework for simulating alternative futures of urban development at high resolution over a large extent to better understand the cumulative land change impacts of different urban growth strategies.
We present a methodology for assessing broad scale impacts of likely future urban growth using the FUTURES model. Traditionally, land change and urban modelling for large regions have been conducted at coarse resolution or through brute force due to computational demands and limitations [6
]. Our application illustrates the challenge of simulating land change at relatively high resolution (30 m) over a large extent (six U.S. states). Achieving high resolution projections of land change over multiple jurisdictional areas is necessary for accurately assessing the cumulative impacts of land change at resolutions that match ecological and social processes. For example, such simulations can help in local planning for recreational areas, as well as understanding how agricultural production will be impacted given different planning strategies.
Our results indicate that important ecological areas, forest and farmland are vulnerable to urban development across the SAS over the coming decades. While overall losses are similar between the Infill and SQ scenarios (Appendix B Table A1
and Table A2
), differences in development patterns and local variation in land cover/use result in different land change impacts. For example, the greater loss of productive peach growing lands in the Infill scenario is due to the high concentration of peach orchards in close proximity to growing medium-sized rural centers in Georgia and South Carolina (Appendix B Table A2
, Figure A1
and Figure A2
). The low concentration of productive farmlands in close proximity to major urban centers like Miami, Atlanta, and Birmingham, conversely, results in greater losses in the SQ scenario (Appendix B Figure A1
and Figure A2
), as development occurs further from the urban fringe. Despite losses in non-urban land use, the more compact development patterns resulting from the Infill scenario spare larger, contiguous patches of forests and agricultural land compared to the patchy, dispersed, patterns of SQ development in the SAS [3
The different patterns of urbanization and landscape change will likely influence the actual flow of ecosystem services in ways that are obscured at large regional scales. Ecologically, the dispersed patterns produced by the SQ scenario will additionally result in changes to lands between urban developments (e.g., forests, agricultural land). For example, the development of parks and greenways on these lands, that include infrastructure for residents of developed communities, will result in additional fragmentation of habitat. Farmland fragmented by the SQ growth will likewise be altered as agricultural production becomes less viable due to changes in parcel configuration. Considering trade-offs related to pattern requires fine scale and contextual information that is difficult to summarize at larger scales [5
] and likely requires improved understanding of how pattern influences the flow of ecosystem services.
4.1. Challenges Getting the Pattern Correct
Our validation, based on hindcasting simulations between 1992–2011, showed reasonable agreement with observed urban development in the 2011 reference year, comparable to other land change model accuracies [14
]. Moreover, LSI measures indicated high correspondence between simulated and observed development patterns in a majority of the sampled regions. While some areas were less well projected, this is to be expected and reflects both the inherent heterogeneity of urban development and the challenge of estimating future urbanization at large scales and fine resolution.
As approximations of complex systems, urban and land change models are simplified abstractions, not fully representing patterns of development nor projecting the specific locations of change with complete accuracy. While increasing the sophistication of model parameterization to better match local processes and improve accuracy is an important goal for land change science [45
], there are trade-offs for simulations at high resolution over large extents. Traditionally, land change for large regions has been done using cellular automata (CA) models (e.g., CLUE, SLEUTH) with simplified inputs that can be obtained for large extents and using simple geographic rules that are broadly applicable [8
]. Agent-based models that include high levels of model complexity, however, are often better at mimicking local processes by incorporating behavioral drivers [18
], resulting in increased local pattern simulation accuracy. The computational demands (local agent decisions) and data requirements (local model of behavior), however, means that it is rarely practical to use ABMs for simulating urbanization for large regions. The FUTURES model partially accounts for how local scale processes influence development by incorporating local estimates of land consumption, local urban growth models (i.e., county), and local estimates of how existing development impacts subsequent growth. Such an approach that combines both general models applicable over the entire geographic extent and local models that capture finer scale aspects of development is a promising methodology for simulations over large areas. Our parameterization of local factors influencing urban growth, embedded within a general model of urbanization, enabled us to increase computational efficiency while simulating new development with reasonable accuracy.
Similar developed locations in both the SQ and Infill scenarios over the 50 different simulations should be considered when interpreting the results. In this implementation of FUTURES, we used parallelization that increased model speed by simultaneously simulating urbanization within individual counties [44
]. This process limited sites for possible new development (seed selection) compared to simulating change over several counties (the original design of FUTURES), and thereby, increased the chance that a specific set of highly suitable cells were repeatedly developed over the 50 simulation years. To solve this path dependent outcome, future implementations of the model will need to run experiments that identify larger regions (i.e., multiple counties) at which development is appropriate (e.g., Atlanta metro area); thereby improving the realism of the model projections, and balancing the computational requirements of simulating urban development over these larger areas.
Furthermore, while our results show promise, our choice of a 30 m resolution does increase uncertainty due to the granularity of our projections. For example, the site of a new building and its associated infrastructure, which is distinguishable at this resolution, is highly unpredictable. Our projections represent landscape-scale trends of urbanization processes rather than actual, site-specific outcomes, and this inherent spatial uncertainty should be considered when interpreting the results. Our chosen resolution matches the development of urban impervious surface over a broad range of land types (i.e., farm buildings, exurban development, new housing subdivision); however, it is unlikely to be practical for measuring the impact of some land use and ES trade-offs. For example, projecting the impacts on farmlands at this resolution is unlikely to be necessary, as conversion often occurs at coarser resolutions (i.e., parcel level). Within-parcel land change, however, will likely impact other ES that requires this fine-resolution projection (e.g., micro-ecology landscape aesthetics). Additional research into the appropriate scale and extent of study will be an important step for projecting land use and ES trade-offs over time. Promising nested modeling frameworks that simulate land change at multiple resolutions may additionally aid in projecting at appropriate scales for various ES [50
]. Such simulations can be conducted at coarser resolutions in productive landscapes (i.e., timber and farmland), and finer resolutions in recreational and biodiverse areas where this scale of analysis is appropriate (see further discussion in the next section).
4.2. Addressing Scales Relevant to Social and Ecological Function
Modelling urban growth at high resolution is an important advancement in land change science as it will enable simulations that reflect the scales at which social and ecological functioning occurs and for which policy is designed. FUTURES, for example, mimics the development of subdivisions that are often a mixture of pervious and impervious surfaces, as well as single small urban structures (Figure 3
; Appendix B Figure A1
and Figure A2
). This better reflects the different fragmentation patterns occurring around urban areas and allows for improved evaluation of these development outcomes. For example, persistent urban greenspace around and within subdivisions might serve an important social function, providing natural areas for walking paths or be of ecological benefit, creating specific habitat for certain species [3
]. Conversely, fragmentation may increase the cost of infrastructure and create a barrier for the movement of wildlife. In the context of agriculture, more contiguous patterns like those realized in an Infill scenario are likely more productive as there are fewer hindrances to the movement of large farming equipment and more efficient use of land. Such evaluations are only possible if land change models simulate different patterns of development at resolutions that match ecological and social functioning.
Similarly, large-scale outcomes of land change, modelled with high granularity, are important to consider as small changes can have large macro-scale impacts, for example, in food systems. The difference between a 3% (SQ) and a 1% (Infill) decrease in productive farmland in a highly productive agricultural area can have substantial impacts on regional and global food chains [51
]. Our model implementation allows for the rapid evaluation of microscale change impacts at the macroscale.
Representing these microscale developments and macroscale impacts will likewise increase the plausibility of simulations, creating important buy-in from stakeholders. Land change models based exclusively on site suitability often result in concentric development patterns that, in the context of the multinodel development that typifies US growth, may be viewed with much skepticism. Representing the common leapfrog development pattern, even in the abstract sense due to limited accuracy, is important to present to policymakers familiar with urban growth. Simulating more plausible development patterns can potentially increase the need for considering urban growth patterns in urban design and planning. Likewise, providing evidence of the cumulative impacts of these local scale changes may increase coordination between regions and countries to implement land sparing strategies that protect the global food supply.