@article{pickard_meentemeyer_2019, title={Validating land change models based on configuration disagreement}, volume={77}, ISSN={["1873-7587"]}, DOI={10.1016/j.compenvurbsys.2019.101366}, abstractNote={Land change models are increasingly being employed to predict future landscapes and influence policy and decision-making. To ensure the highest model accuracy, validation methods have become commonplace following a land change simulation. The most common validation method employed uses quantity and allocation disagreement. However, these current measures may not account for differences in the configurations of land change, placing them in potential conflict with the principals of heterogeneity and spatial patterning of landscape ecology. We develop a new metric, termed configuration disagreement, designed to focus on the size, shape, and complexity of land change simulations. Using this metric, we demonstrate the value of including errors of configuration disagreement – in addition to quantity and allocation error – in the assessment of land change models. Four computational experiments of land change that vary only in spatial pattern are developed using the FUTURES land change model. For each experiment, configuration disagreement and the traditional validation metrics are computed simultaneously. Results indicate that models validated only with consideration of quantity and allocation error may misrepresent, or not fully account for, spatial patterns of landscape change. The research objective will ultimately guide which component, or components, of model disagreement are most critical for consideration. Yet, our work reveals why it may be more helpful to validate simulations in terms of configuration accuracy. Specifically, if a study requires accurately modeling the spatial patterns and arrangements of land cover. Configuration disagreement could add critical information with respect to a model's simulated changes in size, shape, and spatial arrangements, and possibly enhance ecologically meaningful land change science.}, journal={COMPUTERS ENVIRONMENT AND URBAN SYSTEMS}, author={Pickard, Brian R. and Meentemeyer, Ross K.}, year={2019}, month={Sep} }
 @article{pickard_gray_meentemeyer_2017, title={Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models}, volume={6}, ISSN={["2073-445X"]}, DOI={10.3390/land6030052}, abstractNote={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.}, number={3}, journal={LAND}, author={Pickard, Brian and Gray, Joshua and Meentemeyer, Ross}, year={2017}, month={Sep} }
 @article{pickard_van berkel_petrasova_meentemeyer_2017, title={Forecasts of urbanization scenarios reveal trade-offs between landscape change and ecosystem services}, volume={32}, ISSN={["1572-9761"]}, DOI={10.1007/s10980-016-0465-8}, abstractNote={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. 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. 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. 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. By understanding trade-offs associated with urban configurations, decision makers can identify ES critical to an area and promote configurations that enhance those.}, number={3}, journal={LANDSCAPE ECOLOGY}, author={Pickard, Brian R. and Van Berkel, Derek and Petrasova, Anna and Meentemeyer, Ross K.}, year={2017}, month={Mar}, pages={617–634} }
 @article{inostroza_konig_pickard_zhen_2017, title={Putting ecosystem services into practice: Trade-off assessment tools, indicators and decision support systems}, volume={26}, journal={Ecosystem Services}, author={Inostroza, L. and Konig, H. J. and Pickard, B. and Zhen, L.}, year={2017}, pages={303–305} }