Publications
2018 |
Vukomanovic, Jelena; Singh, Kunwar K; Petrasova, Anna; Meentemeyer, Ross K Not seeing the forest for the trees: Modeling exurban viewscapes with LiDAR Journal Article Landscape and Urban Planning, 170 , pp. 169-176, 2018. Abstract | Links | BibTeX | Tags: digital elevation model, land change, landscape aesthetics, multiscale, top-of canopy, visual quality @article{Vukomanovic2018, title = {Not seeing the forest for the trees: Modeling exurban viewscapes with LiDAR}, author = {Jelena Vukomanovic and Kunwar K. Singh and Anna Petrasova and Ross K. Meentemeyer }, url = {https://doi.org/10.1016/j.landurbplan.2017.10.010}, doi = {10.1016/j.landurbplan.2017.10.010}, year = {2018}, date = {2018-02-01}, journal = {Landscape and Urban Planning}, volume = {170}, pages = {169-176}, abstract = {Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications.}, keywords = {digital elevation model, land change, landscape aesthetics, multiscale, top-of canopy, visual quality}, pubstate = {published}, tppubtype = {article} } Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications. |
2017 |
Davis, Amy J; Thill, Jean-Claude; Meentemeyer, Ross K Multi-temporal trajectories of landscape change explain forest biodiversity in urbanizing ecosystems Journal Article Landscape Ecology, 32 (9), pp. 1789-1803, 2017. Abstract | Links | BibTeX | Tags: deforestation, forest biodiversity, forest cover, land change, multitemporal @article{Davis2017, title = {Multi-temporal trajectories of landscape change explain forest biodiversity in urbanizing ecosystems}, author = {Amy J. Davis and Jean-Claude Thill and Ross K. Meentemeyer}, url = {https://link.springer.com/article/10.1007/s10980-017-0541-8}, doi = {10.1007/s10980-017-0541-8}, year = {2017}, date = {2017-09-01}, journal = {Landscape Ecology}, volume = {32}, number = {9}, pages = {1789-1803}, abstract = {Context Forest loss and fragmentation negatively affect biodiversity. However, disturbances in forest canopy resulting from repeated deforestation and reforestation are also likely important drivers of biodiversity, but are overlooked when forest cover change is assessed using a single time interval. Objectives We investigated two questions at the nexus of plant diversity and forest cover change dynamics: (1) Do multitemporal forest cover change trajectories explain patterns of plant diversity better than a simple measure of overall forest change? (2) Are specific types of forest cover change trajectories associated with significantly higher or lower levels of diversity? Methods We sampled plant biodiversity in forests spanning the Charlotte, NC, region. We derived forest cover change trajectories occurring within nested spatial extents per sample site using a time series of aerial photos from 1938 to 2009, then classified trajectories by spatio-temporal patterns of change. While accounting for landscape and environmental covariates, we assessed the effects of the trajectory classes as compared to net forest cover change on native plant diversity. Results Our results indicated that forest stand diversity is best explained by forest change trajectories, while the herb layer is better explained by net forest cover change. Three distinct forest change trajectory classes were found to influence the forest stand and herb layer. Conclusions The influence of forest dynamics on biodiversity can be overlooked in analyses that use only net forest cover change. Our results illustrate the utility of assessing how specific trajectories of past land cover change influence biodiversity patterns in the present.}, keywords = {deforestation, forest biodiversity, forest cover, land change, multitemporal}, pubstate = {published}, tppubtype = {article} } Context Forest loss and fragmentation negatively affect biodiversity. However, disturbances in forest canopy resulting from repeated deforestation and reforestation are also likely important drivers of biodiversity, but are overlooked when forest cover change is assessed using a single time interval. Objectives We investigated two questions at the nexus of plant diversity and forest cover change dynamics: (1) Do multitemporal forest cover change trajectories explain patterns of plant diversity better than a simple measure of overall forest change? (2) Are specific types of forest cover change trajectories associated with significantly higher or lower levels of diversity? Methods We sampled plant biodiversity in forests spanning the Charlotte, NC, region. We derived forest cover change trajectories occurring within nested spatial extents per sample site using a time series of aerial photos from 1938 to 2009, then classified trajectories by spatio-temporal patterns of change. While accounting for landscape and environmental covariates, we assessed the effects of the trajectory classes as compared to net forest cover change on native plant diversity. Results Our results indicated that forest stand diversity is best explained by forest change trajectories, while the herb layer is better explained by net forest cover change. Three distinct forest change trajectory classes were found to influence the forest stand and herb layer. Conclusions The influence of forest dynamics on biodiversity can be overlooked in analyses that use only net forest cover change. Our results illustrate the utility of assessing how specific trajectories of past land cover change influence biodiversity patterns in the present. |
Pickard, Brian R; Gray, Joshua; Meentemeyer, Ross K Comparing quantity, allocation and configuration accuracy of multiple land change models Journal Article Land, 6 (3), pp. 52, 2017. Abstract | Links | BibTeX | Tags: accuracy, land change, modeling, urbanization @article{Pickard2017b, title = {Comparing quantity, allocation and configuration accuracy of multiple land change models}, author = {Brian R. Pickard and Joshua Gray and Ross K. Meentemeyer}, url = {http://www.mdpi.com/2073-445X/6/3/52/htm}, doi = {10.3390/land6030052}, year = {2017}, date = {2017-08-15}, journal = {Land}, volume = {6}, number = {3}, pages = {52}, abstract = {The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher’s discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model’s projections.}, keywords = {accuracy, land change, modeling, urbanization}, pubstate = {published}, tppubtype = {article} } The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher’s discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model’s projections. |
Smith, Jordan; Smart, Lindsey S; Dorning, Monica A; Dupéy, Lauren Nicole; Méley, Andréanne; Meentemeyer, Ross K Bayesian methods to estimate urban growth potential Journal Article Landscape and Urban Planning, 163 , pp. 1-16, 2017. Abstract | Links | BibTeX | Tags: Bayesian model, land change, stated preference, urbanization @article{Smith2017b, title = {Bayesian methods to estimate urban growth potential}, author = {Jordan Smith and Lindsey S. Smart and Monica A. Dorning and Lauren Nicole Dupéy and Andréanne Méley and Ross K. Meentemeyer}, url = {https://doi.org/10.1016/j.landurbplan.2017.03.004}, doi = {10.1016/j.landurbplan.2017.03.004}, year = {2017}, date = {2017-07-01}, journal = {Landscape and Urban Planning}, volume = {163}, pages = {1-16}, abstract = {Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.}, keywords = {Bayesian model, land change, stated preference, urbanization}, pubstate = {published}, tppubtype = {article} } Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth. |
Pickard, Brian R; Berkel, Derek Van; Petrasova, Anna; Meentemeyer, Ross K Forecasts of urbanization scenarios reveal trade-offs between landscape change and ecosystem services Journal Article Landscape Ecology, 32 (3), pp. 617-634, 2017. Abstract | Links | BibTeX | Tags: ecosystem services, geospatial analytics, land change, trade-offs @article{Pickard2017, title = {Forecasts of urbanization scenarios reveal trade-offs between landscape change and ecosystem services}, author = {Brian R. Pickard and Derek Van Berkel and Anna Petrasova and Ross K. Meentemeyer}, url = {https://link.springer.com/article/10.1007/s10980-016-0465-8}, doi = {10.1007/s10980-016-0465-8}, year = {2017}, date = {2017-03-01}, journal = {Landscape Ecology}, volume = {32}, number = {3}, pages = {617-634}, abstract = {Context Expansion of urban settlements has caused observed declines in ecosystem services (ES) globally, further stressing the need for informed urban development and policies. Incorporating ES concepts into the decision making process has been shown to support resilient and functional ecosystems. Coupling land change and ES models allows for insights into the impacts and anticipated trade-offs of specific policy decisions. The spatial configuration of urbanization likely influences the delivery and production of ES. Objective When considering multiple ES simultaneously, improving the production of one ecosystem service often results in the decrease in the provision of other ES, giving rise to trade-offs. We examine the impact of three urban growth scenarios on several ES to determine the degree to which spatial configuration of urbanization and the development of natural land cover impacts these services over 25 years. Methods We couple land change and ES models to examine impacts to carbon sequestration, surface water-run off, nitrogen and phosphorus export, organic farming and camping site suitability, to determine trade-offs among the six ES associated with each spatial configuration for western North Carolina. Results Consequences of urban configurations are dramatic, with degraded ES across all scenarios and substantial variation depending on urban pattern, revealing trade-offs. Counter-intuitive trade-offs between carbon sequestration and lands available for organic farming and camping were observed, suggesting that no configurations result in mutual benefits for all ES. Conclusions By understanding trade-offs associated with urban configurations, decision makers can identify ES critical to an area and promote configurations that enhance those.}, keywords = {ecosystem services, geospatial analytics, land change, trade-offs}, pubstate = {published}, tppubtype = {article} } Context Expansion of urban settlements has caused observed declines in ecosystem services (ES) globally, further stressing the need for informed urban development and policies. Incorporating ES concepts into the decision making process has been shown to support resilient and functional ecosystems. Coupling land change and ES models allows for insights into the impacts and anticipated trade-offs of specific policy decisions. The spatial configuration of urbanization likely influences the delivery and production of ES. Objective When considering multiple ES simultaneously, improving the production of one ecosystem service often results in the decrease in the provision of other ES, giving rise to trade-offs. We examine the impact of three urban growth scenarios on several ES to determine the degree to which spatial configuration of urbanization and the development of natural land cover impacts these services over 25 years. Methods We couple land change and ES models to examine impacts to carbon sequestration, surface water-run off, nitrogen and phosphorus export, organic farming and camping site suitability, to determine trade-offs among the six ES associated with each spatial configuration for western North Carolina. Results Consequences of urban configurations are dramatic, with degraded ES across all scenarios and substantial variation depending on urban pattern, revealing trade-offs. Counter-intuitive trade-offs between carbon sequestration and lands available for organic farming and camping were observed, suggesting that no configurations result in mutual benefits for all ES. Conclusions By understanding trade-offs associated with urban configurations, decision makers can identify ES critical to an area and promote configurations that enhance those. |
2015 |
Dorning, Monica A; Koch, Jennifer; Shoemaker, Douglas A; Meentemeyer, Ross K Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies Journal Article Landscape and Urban Planning, 136 , pp. 28-39, 2015. Abstract | Links | BibTeX | Tags: conservation, land change, urbanization @article{Dorning2015, title = {Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies}, author = {Monica A. Dorning and Jennifer Koch and Douglas A. Shoemaker and Ross K. Meentemeyer}, url = {http://www.sciencedirect.com/science/article/pii/S0169204614002710}, doi = {10.1016/j.landurbplan.2014.11.011}, year = {2015}, date = {2015-04-01}, journal = {Landscape and Urban Planning}, volume = {136}, pages = {28-39}, abstract = {Land that is of great value for conservation can also be highly suitable for human use, resulting in competition between urban development and the protection of natural resources. To assess the effectiveness of proposed regional land conservation strategies in the context of rapid urbanization, we measured the impacts of simulated development patterns on two distinct conservation goals: protecting priority natural resources and limiting landscape fragmentation. Using a stochastic, patch-based land change model (FUTURES) we projected urbanization in the North Carolina Piedmont according to status quo trends and several conservation-planning strategies, including constraints on the spatial distribution of development, encouraging infill, and increasing development density. This approach allows simulation of population-driven land consumption without excluding the possibility of development, even in areas of high conservation value. We found that if current trends continue, new development will consume 11% of priority resource lands, 21% of forested land, and 14% of farmlands regionally by 2032. We also found that no single conservation strategy was optimal for achieving both conservation goals. For example, strategies that excluded development from priority areas caused increased fragmentation of forests and farmlands, while infill strategies increased loss of priority resources proximal to urban areas. Exploration of these land change scenarios not only confirmed that a failure to act is likely to result in irreconcilable losses to a conservation network, but that all conservation plans are not equivalent in effect, highlighting the importance of analyzing tradeoffs between alternative conservation planning approaches.}, keywords = {conservation, land change, urbanization}, pubstate = {published}, tppubtype = {article} } Land that is of great value for conservation can also be highly suitable for human use, resulting in competition between urban development and the protection of natural resources. To assess the effectiveness of proposed regional land conservation strategies in the context of rapid urbanization, we measured the impacts of simulated development patterns on two distinct conservation goals: protecting priority natural resources and limiting landscape fragmentation. Using a stochastic, patch-based land change model (FUTURES) we projected urbanization in the North Carolina Piedmont according to status quo trends and several conservation-planning strategies, including constraints on the spatial distribution of development, encouraging infill, and increasing development density. This approach allows simulation of population-driven land consumption without excluding the possibility of development, even in areas of high conservation value. We found that if current trends continue, new development will consume 11% of priority resource lands, 21% of forested land, and 14% of farmlands regionally by 2032. We also found that no single conservation strategy was optimal for achieving both conservation goals. For example, strategies that excluded development from priority areas caused increased fragmentation of forests and farmlands, while infill strategies increased loss of priority resources proximal to urban areas. Exploration of these land change scenarios not only confirmed that a failure to act is likely to result in irreconcilable losses to a conservation network, but that all conservation plans are not equivalent in effect, highlighting the importance of analyzing tradeoffs between alternative conservation planning approaches. |
2014 |
BenDor, Todd; Shoemaker, Douglas A; Thill, Jean-Claude; Dorning, Monica A; Meentemeyer, Ross K A mixed-methods analysis of social-ecological feedbacks between urbanization and forest persistence Journal Article Ecology and Society, 19 (3), pp. 3, 2014. Abstract | Links | BibTeX | Tags: forest persistence, land change, social-ecological feedbacks, tax policy, urban forests, urbanization @article{BenDor2014, title = {A mixed-methods analysis of social-ecological feedbacks between urbanization and forest persistence}, author = {Todd BenDor and Douglas A. Shoemaker and Jean-Claude Thill and Monica A. Dorning and Ross K. Meentemeyer}, url = {http://dx.doi.org/10.5751/ES-06508-190303}, doi = {10.5751/ES-06508-190303}, year = {2014}, date = {2014-09-01}, journal = {Ecology and Society}, volume = {19}, number = {3}, pages = {3}, abstract = {We examined how social-ecological factors in the land-change decision-making process influenced neighboring decisions and trajectories of alternative landscape ecologies. We decomposed individual landowner decisions to conserve or develop forests in the rapidly growing Charlotte, North Carolina, U.S. region, exposing and quantifying the effects of forest quality, and social and cultural dynamics. We tested the hypothesis that the intrinsic value of forest resources, e.g., cultural attachment to land, influence woodland owners’ propensity to sell. Data were collected from a sample of urban, nonindustrial private forest (U-NIPF) owners using an individualized survey design that spatially matched land-owner responses to the ecological and timber values of their forest stands. Cluster analysis (n = 126) revealed four woodland owner typologies with widely ranging views on the ecosystem, cultural, and historical values of their forests. Classification tree analysis revealed woodland owners’ willingness to sell was characterized by nonlinear, interactive factors, including sense of place values regarding the retention of native vegetation, the size of forest holdings, their connectedness to nature, ‘pressure’ from surrounding development, and behavioral patterns, such as how often landowners visit their land. Several ecological values and economic factors were not found to figure in the decision to retain forests. Our study design is unique in that we address metropolitan forest persistence across urban-rural and population gradients using a unique individualized survey design that richly contextualizes survey responses. Understanding the interplay between policies and landowner behavior can also help resource managers to better manage and promote forest persistence. Given the region’s paucity of policy tools to manage the type and amount of development, the mosaic of land cover the region currently enjoys is far from stable.}, keywords = {forest persistence, land change, social-ecological feedbacks, tax policy, urban forests, urbanization}, pubstate = {published}, tppubtype = {article} } We examined how social-ecological factors in the land-change decision-making process influenced neighboring decisions and trajectories of alternative landscape ecologies. We decomposed individual landowner decisions to conserve or develop forests in the rapidly growing Charlotte, North Carolina, U.S. region, exposing and quantifying the effects of forest quality, and social and cultural dynamics. We tested the hypothesis that the intrinsic value of forest resources, e.g., cultural attachment to land, influence woodland owners’ propensity to sell. Data were collected from a sample of urban, nonindustrial private forest (U-NIPF) owners using an individualized survey design that spatially matched land-owner responses to the ecological and timber values of their forest stands. Cluster analysis (n = 126) revealed four woodland owner typologies with widely ranging views on the ecosystem, cultural, and historical values of their forests. Classification tree analysis revealed woodland owners’ willingness to sell was characterized by nonlinear, interactive factors, including sense of place values regarding the retention of native vegetation, the size of forest holdings, their connectedness to nature, ‘pressure’ from surrounding development, and behavioral patterns, such as how often landowners visit their land. Several ecological values and economic factors were not found to figure in the decision to retain forests. Our study design is unique in that we address metropolitan forest persistence across urban-rural and population gradients using a unique individualized survey design that richly contextualizes survey responses. Understanding the interplay between policies and landowner behavior can also help resource managers to better manage and promote forest persistence. Given the region’s paucity of policy tools to manage the type and amount of development, the mosaic of land cover the region currently enjoys is far from stable. |
2013 |
Meentemeyer, Ross K; Tang, Wenwu; Dorning, Monica A; Vogler, John B; Cunniffe, Nik J; Shoemaker, Douglas A FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm Journal Article Annals of the Association of American Geographers, 103 (4), pp. 785-807, 2013, ISSN: 0004-5608. Abstract | Links | BibTeX | Tags: fragmentation, land change, nonstationarity, object-based, region growing algorithm @article{Meentemeyer2013, title = {FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm}, author = {Ross K. Meentemeyer and Wenwu Tang and Monica A. Dorning and John B. Vogler and Nik J. Cunniffe and Douglas A. Shoemaker}, url = {http://www.tandfonline.com/doi/abs/10.1080/00045608.2012.707591}, doi = {10.1080/00045608.2012.707591}, issn = {0004-5608}, year = {2013}, date = {2013-08-01}, journal = {Annals of the Association of American Geographers}, volume = {103}, number = {4}, pages = {785-807}, abstract = {We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes.}, keywords = {fragmentation, land change, nonstationarity, object-based, region growing algorithm}, pubstate = {published}, tppubtype = {article} } We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes. |
2012 |
Singh, Kunwar K; Vogler, John B; Shoemaker, Douglas A; Meentemeyer, Ross K LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 74 , pp. 110-121, 2012. Abstract | Links | BibTeX | Tags: fusion, land change, land cover, Landsat, large-area assessment, LiDAR, managed clearings, mapping accuracy @article{Singh2012, title = {LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy}, author = {Kunwar K. Singh and John B. Vogler and Douglas A. Shoemaker and Ross K. Meentemeyer}, url = {http://dx.doi.org/10.1016/j.isprsjprs.2012.09.009}, doi = {10.1016/j.isprsjprs.2012.09.009}, year = {2012}, date = {2012-10-23}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {74}, pages = {110-121}, abstract = {The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30 m and LiDAR data and LiDAR-TM fusions at 1 m, 5 m, 10 m, 15 m and 30 m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1 m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30 m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1 m, 5 m, and 10 m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5 m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover.}, keywords = {fusion, land change, land cover, Landsat, large-area assessment, LiDAR, managed clearings, mapping accuracy}, pubstate = {published}, tppubtype = {article} } The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30 m and LiDAR data and LiDAR-TM fusions at 1 m, 5 m, 10 m, 15 m and 30 m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1 m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30 m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1 m, 5 m, and 10 m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5 m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover. |
| 1. | Vukomanovic, Jelena; Singh, Kunwar K; Petrasova, Anna; Meentemeyer, Ross K: Not seeing the forest for the trees: Modeling exurban viewscapes with LiDAR. In: Landscape and Urban Planning, 170 , pp. 169-176, 2018. (Type: Journal Article | Abstract | Links | BibTeX) @article{Vukomanovic2018, title = {Not seeing the forest for the trees: Modeling exurban viewscapes with LiDAR}, author = {Jelena Vukomanovic and Kunwar K. Singh and Anna Petrasova and Ross K. Meentemeyer }, url = {https://doi.org/10.1016/j.landurbplan.2017.10.010}, doi = {10.1016/j.landurbplan.2017.10.010}, year = {2018}, date = {2018-02-01}, journal = {Landscape and Urban Planning}, volume = {170}, pages = {169-176}, abstract = {Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Viewscapes are the visible portions of a landscape that create a visual connection between a human observer and their 3-dimensional surroundings. However, most large area line-of-sight studies have modeled viewscapes using bare-earth digital elevation models, which exclude the 3-D elements of built and natural environments needed to comprehensively understand the scale, complexity and naturalness of an area. In this study, we compared viewscapes derived from LiDAR bare earth (BE) and top-of-canopy (ToC) surface models for 1000 exurban homes in a region of the Rocky Mountains, USA that is experiencing rapid low-density growth. We examined the extent to which the vertical structure of trees and neighboring houses in ToC models – not accounted for in BE models – affect the size and quality of each home’s viewscape. ToC models consistently produced significantly smaller viewscapes compared to BE models across five resolutions of LiDAR-derived models (1, 5, 10, 15, and 30-m). As resolution increased, both ToC and BE models produced increasingly larger, exaggerated viewscapes. Due to their exaggerated size, BE models overestimated the greenness and diversity of vegetation types in viewscapes and underestimated ruggedness of surrounding terrain compared to more realistic ToC models. Finally, ToC models also resulted in more private viewscapes, with exurban residents seeing almost three times fewer neighbors compared to BE models. These findings demonstrate that viewscape studies should consider both vertical and horizontal dimensions of built and natural environments in landscape and urban planning applications. |
| 2. | Davis, Amy J; Thill, Jean-Claude; Meentemeyer, Ross K: Multi-temporal trajectories of landscape change explain forest biodiversity in urbanizing ecosystems. In: Landscape Ecology, 32 (9), pp. 1789-1803, 2017. (Type: Journal Article | Abstract | Links | BibTeX) @article{Davis2017, title = {Multi-temporal trajectories of landscape change explain forest biodiversity in urbanizing ecosystems}, author = {Amy J. Davis and Jean-Claude Thill and Ross K. Meentemeyer}, url = {https://link.springer.com/article/10.1007/s10980-017-0541-8}, doi = {10.1007/s10980-017-0541-8}, year = {2017}, date = {2017-09-01}, journal = {Landscape Ecology}, volume = {32}, number = {9}, pages = {1789-1803}, abstract = {Context Forest loss and fragmentation negatively affect biodiversity. However, disturbances in forest canopy resulting from repeated deforestation and reforestation are also likely important drivers of biodiversity, but are overlooked when forest cover change is assessed using a single time interval. Objectives We investigated two questions at the nexus of plant diversity and forest cover change dynamics: (1) Do multitemporal forest cover change trajectories explain patterns of plant diversity better than a simple measure of overall forest change? (2) Are specific types of forest cover change trajectories associated with significantly higher or lower levels of diversity? Methods We sampled plant biodiversity in forests spanning the Charlotte, NC, region. We derived forest cover change trajectories occurring within nested spatial extents per sample site using a time series of aerial photos from 1938 to 2009, then classified trajectories by spatio-temporal patterns of change. While accounting for landscape and environmental covariates, we assessed the effects of the trajectory classes as compared to net forest cover change on native plant diversity. Results Our results indicated that forest stand diversity is best explained by forest change trajectories, while the herb layer is better explained by net forest cover change. Three distinct forest change trajectory classes were found to influence the forest stand and herb layer. Conclusions The influence of forest dynamics on biodiversity can be overlooked in analyses that use only net forest cover change. Our results illustrate the utility of assessing how specific trajectories of past land cover change influence biodiversity patterns in the present.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Context Forest loss and fragmentation negatively affect biodiversity. However, disturbances in forest canopy resulting from repeated deforestation and reforestation are also likely important drivers of biodiversity, but are overlooked when forest cover change is assessed using a single time interval. Objectives We investigated two questions at the nexus of plant diversity and forest cover change dynamics: (1) Do multitemporal forest cover change trajectories explain patterns of plant diversity better than a simple measure of overall forest change? (2) Are specific types of forest cover change trajectories associated with significantly higher or lower levels of diversity? Methods We sampled plant biodiversity in forests spanning the Charlotte, NC, region. We derived forest cover change trajectories occurring within nested spatial extents per sample site using a time series of aerial photos from 1938 to 2009, then classified trajectories by spatio-temporal patterns of change. While accounting for landscape and environmental covariates, we assessed the effects of the trajectory classes as compared to net forest cover change on native plant diversity. Results Our results indicated that forest stand diversity is best explained by forest change trajectories, while the herb layer is better explained by net forest cover change. Three distinct forest change trajectory classes were found to influence the forest stand and herb layer. Conclusions The influence of forest dynamics on biodiversity can be overlooked in analyses that use only net forest cover change. Our results illustrate the utility of assessing how specific trajectories of past land cover change influence biodiversity patterns in the present. |
| 3. | Pickard, Brian R; Gray, Joshua; Meentemeyer, Ross K: Comparing quantity, allocation and configuration accuracy of multiple land change models. In: Land, 6 (3), pp. 52, 2017. (Type: Journal Article | Abstract | Links | BibTeX) @article{Pickard2017b, title = {Comparing quantity, allocation and configuration accuracy of multiple land change models}, author = {Brian R. Pickard and Joshua Gray and Ross K. Meentemeyer}, url = {http://www.mdpi.com/2073-445X/6/3/52/htm}, doi = {10.3390/land6030052}, year = {2017}, date = {2017-08-15}, journal = {Land}, volume = {6}, number = {3}, pages = {52}, abstract = {The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher’s discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model’s projections.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher’s discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model’s projections. |
| 4. | Smith, Jordan; Smart, Lindsey S; Dorning, Monica A; Dupéy, Lauren Nicole; Méley, Andréanne; Meentemeyer, Ross K: Bayesian methods to estimate urban growth potential. In: Landscape and Urban Planning, 163 , pp. 1-16, 2017. (Type: Journal Article | Abstract | Links | BibTeX) @article{Smith2017b, title = {Bayesian methods to estimate urban growth potential}, author = {Jordan Smith and Lindsey S. Smart and Monica A. Dorning and Lauren Nicole Dupéy and Andréanne Méley and Ross K. Meentemeyer}, url = {https://doi.org/10.1016/j.landurbplan.2017.03.004}, doi = {10.1016/j.landurbplan.2017.03.004}, year = {2017}, date = {2017-07-01}, journal = {Landscape and Urban Planning}, volume = {163}, pages = {1-16}, abstract = {Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners’ perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners in one of the United States’ most rapidly urbanizing regions − the urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region’s development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners’ intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatially-explicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region’s socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region’s historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth. |
| 5. | Pickard, Brian R; Berkel, Derek Van; Petrasova, Anna; Meentemeyer, Ross K: Forecasts of urbanization scenarios reveal trade-offs between landscape change and ecosystem services. In: Landscape Ecology, 32 (3), pp. 617-634, 2017. (Type: Journal Article | Abstract | Links | BibTeX) @article{Pickard2017, title = {Forecasts of urbanization scenarios reveal trade-offs between landscape change and ecosystem services}, author = {Brian R. Pickard and Derek Van Berkel and Anna Petrasova and Ross K. Meentemeyer}, url = {https://link.springer.com/article/10.1007/s10980-016-0465-8}, doi = {10.1007/s10980-016-0465-8}, year = {2017}, date = {2017-03-01}, journal = {Landscape Ecology}, volume = {32}, number = {3}, pages = {617-634}, abstract = {Context Expansion of urban settlements has caused observed declines in ecosystem services (ES) globally, further stressing the need for informed urban development and policies. Incorporating ES concepts into the decision making process has been shown to support resilient and functional ecosystems. Coupling land change and ES models allows for insights into the impacts and anticipated trade-offs of specific policy decisions. The spatial configuration of urbanization likely influences the delivery and production of ES. Objective When considering multiple ES simultaneously, improving the production of one ecosystem service often results in the decrease in the provision of other ES, giving rise to trade-offs. We examine the impact of three urban growth scenarios on several ES to determine the degree to which spatial configuration of urbanization and the development of natural land cover impacts these services over 25 years. Methods We couple land change and ES models to examine impacts to carbon sequestration, surface water-run off, nitrogen and phosphorus export, organic farming and camping site suitability, to determine trade-offs among the six ES associated with each spatial configuration for western North Carolina. Results Consequences of urban configurations are dramatic, with degraded ES across all scenarios and substantial variation depending on urban pattern, revealing trade-offs. Counter-intuitive trade-offs between carbon sequestration and lands available for organic farming and camping were observed, suggesting that no configurations result in mutual benefits for all ES. Conclusions By understanding trade-offs associated with urban configurations, decision makers can identify ES critical to an area and promote configurations that enhance those.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Context Expansion of urban settlements has caused observed declines in ecosystem services (ES) globally, further stressing the need for informed urban development and policies. Incorporating ES concepts into the decision making process has been shown to support resilient and functional ecosystems. Coupling land change and ES models allows for insights into the impacts and anticipated trade-offs of specific policy decisions. The spatial configuration of urbanization likely influences the delivery and production of ES. Objective When considering multiple ES simultaneously, improving the production of one ecosystem service often results in the decrease in the provision of other ES, giving rise to trade-offs. We examine the impact of three urban growth scenarios on several ES to determine the degree to which spatial configuration of urbanization and the development of natural land cover impacts these services over 25 years. Methods We couple land change and ES models to examine impacts to carbon sequestration, surface water-run off, nitrogen and phosphorus export, organic farming and camping site suitability, to determine trade-offs among the six ES associated with each spatial configuration for western North Carolina. Results Consequences of urban configurations are dramatic, with degraded ES across all scenarios and substantial variation depending on urban pattern, revealing trade-offs. Counter-intuitive trade-offs between carbon sequestration and lands available for organic farming and camping were observed, suggesting that no configurations result in mutual benefits for all ES. Conclusions By understanding trade-offs associated with urban configurations, decision makers can identify ES critical to an area and promote configurations that enhance those. |
| 6. | Dorning, Monica A; Koch, Jennifer; Shoemaker, Douglas A; Meentemeyer, Ross K: Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies. In: Landscape and Urban Planning, 136 , pp. 28-39, 2015. (Type: Journal Article | Abstract | Links | BibTeX) @article{Dorning2015, title = {Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies}, author = {Monica A. Dorning and Jennifer Koch and Douglas A. Shoemaker and Ross K. Meentemeyer}, url = {http://www.sciencedirect.com/science/article/pii/S0169204614002710}, doi = {10.1016/j.landurbplan.2014.11.011}, year = {2015}, date = {2015-04-01}, journal = {Landscape and Urban Planning}, volume = {136}, pages = {28-39}, abstract = {Land that is of great value for conservation can also be highly suitable for human use, resulting in competition between urban development and the protection of natural resources. To assess the effectiveness of proposed regional land conservation strategies in the context of rapid urbanization, we measured the impacts of simulated development patterns on two distinct conservation goals: protecting priority natural resources and limiting landscape fragmentation. Using a stochastic, patch-based land change model (FUTURES) we projected urbanization in the North Carolina Piedmont according to status quo trends and several conservation-planning strategies, including constraints on the spatial distribution of development, encouraging infill, and increasing development density. This approach allows simulation of population-driven land consumption without excluding the possibility of development, even in areas of high conservation value. We found that if current trends continue, new development will consume 11% of priority resource lands, 21% of forested land, and 14% of farmlands regionally by 2032. We also found that no single conservation strategy was optimal for achieving both conservation goals. For example, strategies that excluded development from priority areas caused increased fragmentation of forests and farmlands, while infill strategies increased loss of priority resources proximal to urban areas. Exploration of these land change scenarios not only confirmed that a failure to act is likely to result in irreconcilable losses to a conservation network, but that all conservation plans are not equivalent in effect, highlighting the importance of analyzing tradeoffs between alternative conservation planning approaches.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Land that is of great value for conservation can also be highly suitable for human use, resulting in competition between urban development and the protection of natural resources. To assess the effectiveness of proposed regional land conservation strategies in the context of rapid urbanization, we measured the impacts of simulated development patterns on two distinct conservation goals: protecting priority natural resources and limiting landscape fragmentation. Using a stochastic, patch-based land change model (FUTURES) we projected urbanization in the North Carolina Piedmont according to status quo trends and several conservation-planning strategies, including constraints on the spatial distribution of development, encouraging infill, and increasing development density. This approach allows simulation of population-driven land consumption without excluding the possibility of development, even in areas of high conservation value. We found that if current trends continue, new development will consume 11% of priority resource lands, 21% of forested land, and 14% of farmlands regionally by 2032. We also found that no single conservation strategy was optimal for achieving both conservation goals. For example, strategies that excluded development from priority areas caused increased fragmentation of forests and farmlands, while infill strategies increased loss of priority resources proximal to urban areas. Exploration of these land change scenarios not only confirmed that a failure to act is likely to result in irreconcilable losses to a conservation network, but that all conservation plans are not equivalent in effect, highlighting the importance of analyzing tradeoffs between alternative conservation planning approaches. |
| 7. | BenDor, Todd; Shoemaker, Douglas A; Thill, Jean-Claude; Dorning, Monica A; Meentemeyer, Ross K: A mixed-methods analysis of social-ecological feedbacks between urbanization and forest persistence. In: Ecology and Society, 19 (3), pp. 3, 2014. (Type: Journal Article | Abstract | Links | BibTeX) @article{BenDor2014, title = {A mixed-methods analysis of social-ecological feedbacks between urbanization and forest persistence}, author = {Todd BenDor and Douglas A. Shoemaker and Jean-Claude Thill and Monica A. Dorning and Ross K. Meentemeyer}, url = {http://dx.doi.org/10.5751/ES-06508-190303}, doi = {10.5751/ES-06508-190303}, year = {2014}, date = {2014-09-01}, journal = {Ecology and Society}, volume = {19}, number = {3}, pages = {3}, abstract = {We examined how social-ecological factors in the land-change decision-making process influenced neighboring decisions and trajectories of alternative landscape ecologies. We decomposed individual landowner decisions to conserve or develop forests in the rapidly growing Charlotte, North Carolina, U.S. region, exposing and quantifying the effects of forest quality, and social and cultural dynamics. We tested the hypothesis that the intrinsic value of forest resources, e.g., cultural attachment to land, influence woodland owners’ propensity to sell. Data were collected from a sample of urban, nonindustrial private forest (U-NIPF) owners using an individualized survey design that spatially matched land-owner responses to the ecological and timber values of their forest stands. Cluster analysis (n = 126) revealed four woodland owner typologies with widely ranging views on the ecosystem, cultural, and historical values of their forests. Classification tree analysis revealed woodland owners’ willingness to sell was characterized by nonlinear, interactive factors, including sense of place values regarding the retention of native vegetation, the size of forest holdings, their connectedness to nature, ‘pressure’ from surrounding development, and behavioral patterns, such as how often landowners visit their land. Several ecological values and economic factors were not found to figure in the decision to retain forests. Our study design is unique in that we address metropolitan forest persistence across urban-rural and population gradients using a unique individualized survey design that richly contextualizes survey responses. Understanding the interplay between policies and landowner behavior can also help resource managers to better manage and promote forest persistence. Given the region’s paucity of policy tools to manage the type and amount of development, the mosaic of land cover the region currently enjoys is far from stable.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We examined how social-ecological factors in the land-change decision-making process influenced neighboring decisions and trajectories of alternative landscape ecologies. We decomposed individual landowner decisions to conserve or develop forests in the rapidly growing Charlotte, North Carolina, U.S. region, exposing and quantifying the effects of forest quality, and social and cultural dynamics. We tested the hypothesis that the intrinsic value of forest resources, e.g., cultural attachment to land, influence woodland owners’ propensity to sell. Data were collected from a sample of urban, nonindustrial private forest (U-NIPF) owners using an individualized survey design that spatially matched land-owner responses to the ecological and timber values of their forest stands. Cluster analysis (n = 126) revealed four woodland owner typologies with widely ranging views on the ecosystem, cultural, and historical values of their forests. Classification tree analysis revealed woodland owners’ willingness to sell was characterized by nonlinear, interactive factors, including sense of place values regarding the retention of native vegetation, the size of forest holdings, their connectedness to nature, ‘pressure’ from surrounding development, and behavioral patterns, such as how often landowners visit their land. Several ecological values and economic factors were not found to figure in the decision to retain forests. Our study design is unique in that we address metropolitan forest persistence across urban-rural and population gradients using a unique individualized survey design that richly contextualizes survey responses. Understanding the interplay between policies and landowner behavior can also help resource managers to better manage and promote forest persistence. Given the region’s paucity of policy tools to manage the type and amount of development, the mosaic of land cover the region currently enjoys is far from stable. |
| 8. | Meentemeyer, Ross K; Tang, Wenwu; Dorning, Monica A; Vogler, John B; Cunniffe, Nik J; Shoemaker, Douglas A: FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm. In: Annals of the Association of American Geographers, 103 (4), pp. 785-807, 2013, ISSN: 0004-5608. (Type: Journal Article | Abstract | Links | BibTeX) @article{Meentemeyer2013, title = {FUTURES: Multilevel Simulations of Emerging Urban–Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm}, author = {Ross K. Meentemeyer and Wenwu Tang and Monica A. Dorning and John B. Vogler and Nik J. Cunniffe and Douglas A. Shoemaker}, url = {http://www.tandfonline.com/doi/abs/10.1080/00045608.2012.707591}, doi = {10.1080/00045608.2012.707591}, issn = {0004-5608}, year = {2013}, date = {2013-08-01}, journal = {Annals of the Association of American Geographers}, volume = {103}, number = {4}, pages = {785-807}, abstract = {We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We present a multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions using a combination of field-based and object-based representations of land change. The FUTure Urban-Regional Environment Simulation (FUTURES) produces regional projections of landscape patterns using coupled submodels that integrate nonstationary drivers of land change: per capita demand, site suitability, and the spatial structure of conversion events. Patches of land change events are simulated as discrete spatial objects using a stochastic region-growing algorithm that aggregates cell-level transitions based on empirical estimation of parameters that control the size, shape, and dispersion of patch growth. At each time step, newly constructed patches reciprocally influence further growth, which agglomerates over time to produce patterns of urban form and landscape fragmentation. Multilevel structure in each submodel allows drivers of land change to vary in space (e.g., by jurisdiction), rather than assuming spatial stationarity across a heterogeneous region. We applied FUTURES to simulate land development dynamics in the rapidly expanding metropolitan region of Charlotte, North Carolina, between 1996 and 2030, and evaluated spatial variation in model outcomes along an urban–rural continuum, including assessments of cell- and patch-based correctness and error. Simulation experiments reveal that changes in per capita land consumption and parameters controlling the distribution of development affect the emergent spatial structure of forests and farmlands with unique and sometimes counterintuitive outcomes. |
| 9. | Singh, Kunwar K; Vogler, John B; Shoemaker, Douglas A; Meentemeyer, Ross K: LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy. In: ISPRS Journal of Photogrammetry and Remote Sensing, 74 , pp. 110-121, 2012. (Type: Journal Article | Abstract | Links | BibTeX) @article{Singh2012, title = {LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy}, author = {Kunwar K. Singh and John B. Vogler and Douglas A. Shoemaker and Ross K. Meentemeyer}, url = {http://dx.doi.org/10.1016/j.isprsjprs.2012.09.009}, doi = {10.1016/j.isprsjprs.2012.09.009}, year = {2012}, date = {2012-10-23}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, volume = {74}, pages = {110-121}, abstract = {The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30 m and LiDAR data and LiDAR-TM fusions at 1 m, 5 m, 10 m, 15 m and 30 m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1 m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30 m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1 m, 5 m, and 10 m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5 m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The structural characteristics of Light Detection and Ranging (LiDAR) data are increasingly used to classify urban environments at fine scales, but have been underutilized for distinguishing heterogeneous land covers over large urban regions due to high cost, limited spectral information, and the computational difficulties posed by inherently large data volumes. Here we explore tradeoffs between potential gains in mapping accuracy with computational costs by integrating structural and intensity surface models extracted from LiDAR data with Landsat Thematic Mapper (TM) imagery and evaluating the degree to which TM, LiDAR, and LiDAR-TM fusion data discriminated land covers in the rapidly urbanizing region of Charlotte, North Carolina, USA. Using supervised maximum likelihood (ML) and classification tree (CT) methods, we classified TM data at 30 m and LiDAR data and LiDAR-TM fusions at 1 m, 5 m, 10 m, 15 m and 30 m resolutions. We assessed the relative contributions of LiDAR structural and intensity surface models to classification map accuracy and identified optimal spatial resolution of LiDAR surface models for large-area assessments of urban land cover. ML classification of 1 m LiDAR-TM fusions using both structural and intensity surface models increased total accuracy by 32% compared to LiDAR alone and by 8% over TM at 30 m. Fusion data using all LiDAR surface models improved class discrimination of spectrally similar forest, farmland, and managed clearings and produced the highest total accuracies at 1 m, 5 m, and 10 m resolutions (87.2%, 86.3% and 85.4%, respectively). At all resolutions of fusion data and using either ML or CT classifier, the relative contribution of the LiDAR structural surface models (canopy height and normalized digital surface model) to classification accuracy is greater than the intensity surface. Our evaluation of tradeoffs between data volume and thematic map accuracy for this study system suggests that a spatial resolution of 5 m for LiDAR surface models best balances classification performance and the computational challenges posed by large-area assessments of land cover. |
