FUTURES-AMR: Towards an Adaptive Mesh Refinement Framework for Geosimulations

Adaptive Mesh Refinement (AMR) is a computational technique used to reduce the amount of computation and memory required in scientific simulations. Geosimulations are scientific simulations using geographic data, routinely used to predict outcomes of urbanization in urban studies. However, the lack of support for AMR techniques with geosimulations limits exploring prediction outcomes at multiple resolutions. In this paper, we propose an adaptive mesh refinement framework FUTURES-AMR, based on static user-defined policies to enable multi-resolution geosimulations. We develop a prototype for the cellular automaton based urban growth simulation FUTURES by exploiting static and dynamic mesh refinement techniques in conjunction with the Patch Growing Algorithm (PGA). While, the static refinement technique supports a statically defined fixed resolution mesh simulation at a location, the dynamic refinement technique supports dynamically refining the resolution based on simulation outcomes at runtime. Further, we develop two approaches - asynchronous AMR and synchronous AMR, suitable for parallel execution in a distributed computing environment with varying support for solution integration of the multi-resolution results. Finally, using the FUTURES-AMR framework with different policies in an urban study, we demonstrate reduced execution time, and low memory overhead for a multi-resolution simulation

Payments for Carbon Sequestration to Alleviate Development Pressure in a Rapidly Urbanizing Region

The purpose of this study was to determine individuals’ willingness to enroll in voluntary payments for carbon sequestration programs through the use of a discrete choice experiment delivered to forest owners living in the rapidly urbanizing region surrounding Charlotte, North Carolina. We examined forest owners’ willingness to enroll in payments for carbon sequestration policies under different levels of financial incentives (annual revenue), different contract lengths and different program administrators (e.g., private companies versus a state or federal agency). We also examined the influence forest owners’ sense of place had on their willingness to enroll in hypothetical programs. Our results showed a high level of ambivalence towards participating in payments for carbon sequestration programs. However, both financial incentives and contract lengths significantly influenced forest owners’ intent to enroll. Neither program administration nor forest owners’ sense of place influenced intent to enroll. While our analyses indicated payments from carbon sequestration programs are not currently competitive with the monetary returns expected from timber harvest or property sales, certain forest owners might see payments for carbon sequestration programs as a viable option for offsetting increasing tax costs as development encroaches and property values rise

Creating spatially complete zoning maps using machine learning

Zoning regulates land use and intensity of urban development at the county and municipal level in the United States, promoting economic growth, community health, and environmental preservation. However, limited availability of zoning data at scale hinders regional assessments of regulations and coordinated resilience planning efforts. In this study, we developed an open-source, replicable, and transferable framework to predict spatially complete zoning in areas where zoning information is publicly unavailable. We applied a Hierarchical Random Forest algorithm to predict multilevel zoning districts, including three core districts (residential, non-residential, mixed use) and 13 sub-districts. To mimic real-world data accessibility challenges, we evaluated two models: one filling gaps within a county (within-county) and the other extrapolating for counties with no available data (between-county). We tested our models statewide in North Carolina (NC), USA, and developed the State's first comprehensive zoning map. We found strong predictive performance for our within-county model (∼99% accuracy; macro averaged F1 score of ∼0.97) irrespective of district breakdown (i.e., core and sub). However, our between-county model performance was lower and varied depending on the training counties sampled and the district breakdown considered (19–90% accuracy; macro averaged F1 score of 0.105–0.451). Our framework provides spatially complete zoning maps for previously inaccessible locations, enabling researchers and planners to conduct large-scale comprehensive zoning assessments

Landscape Epidemiology and Control of Pathogens with Cryptic and Long-Distance Dispersal: Sudden Oak Death in Northern Californian Forests

Exotic pathogens and pests threaten ecosystem service, biodiversity, and crop security globally. If an invasive agent can disperse asymptomatically over long distances, multiple spatial and temporal scales interplay, making identification of effective strategies to regulate, monitor, and control disease extremely difficult. The management of outbreaks is also challenged by limited data on the actual area infested and the dynamics of spatial spread, due to financial, technological, or social constraints. We examine principles of landscape epidemiology important in designing policy to prevent or slow invasion by such organisms, and use Phytophthora ramorum, the cause of sudden oak death, to illustrate how shortfalls in their understanding can render management applications inappropriate. This pathogen has invaded forests in coastal California, USA, and an isolated but fast-growing epidemic focus in northern California (Humboldt County) has the potential for extensive spread. The risk of spread is enhanced by the pathogen's generalist nature and survival. Additionally, the extent of cryptic infection is unknown due to limited surveying resources and access to private land. Here, we use an epidemiological model for transmission in heterogeneous landscapes and Bayesian Markov-chain-Monte-Carlo inference to estimate dispersal and life-cycle parameters of P. ramorum and forecast the distribution of infection and speed of the epidemic front in Humboldt County. We assess the viability of management options for containing the pathogen's northern spread and local impacts. Implementing a stand-alone host-free “barrier” had limited efficacy due to long-distance dispersal, but combining curative with preventive treatments ahead of the front reduced local damage and contained spread. While the large size of this focus makes effective control expensive, early synchronous treatment in newly-identified disease foci should be more cost-effective. We show how the successful management of forest ecosystems depends on estimating the spatial scales of invasion and treatment of pathogens and pests with cryptic long-distance dispersal

A mixed-methods analysis of social-ecological feedbacks between urbanization and forest persistence

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

A mixed-methods analysis of social-ecological feedbacks between urbanization and forest persistence

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

Modeling Landowner Interactions and Development Patterns at the Urban Fringe

Population growth and unrestricted development policies are driving low-density urbanization and fragmentation of peri-urban landscapes across North America. While private individuals own most undeveloped land, little is known about how their decision-making processes shape landscape-scale patterns of urbanization over time. We introduce a hybrid agent-based modeling (ABM) – cellular automata (CA) modeling approach, developed for analyzing dynamic feedbacks between landowners’ decisions to sell their land for development, and resulting patterns of landscape fragmentation. Our modeling approach builds on existing conceptual frameworks in land systems modeling by integrating an ABM into an established grid-based land-change model – FUTURES. The decision-making process within the ABM involves landowner agents whose decision to sell their land to developers is a function of heterogeneous preferences and peer-influences (i.e., spatial neighborhood relationships). Simulating landowners’ decision to sell allows an operational link between the ABM and the CA module. To test our hybrid ABM-CA approach, we used empirical data for a rapidly growing region in North Carolina for parameterization. We conducted a sensitivity analysis focusing on the two most relevant parameters—spatial actor distribution and peer-influence intensity—and evaluated the dynamic behavior of the model simulations. The simulation results indicate different peer-influence intensities lead to variable landscape fragmentation patterns, suggesting patterns of spatial interaction among landowners indirectly affect landscape-scale patterns of urbanization and the fragmentation of undeveloped forest and farmland

Geographic clustering of elevated blood heavy metal levels in pregnant women

Abstract Background Cadmium (Cd), lead (Pb), mercury (Hg), and arsenic (As) exposure is ubiquitous and has been associated with higher risk of growth restriction and cardiometabolic and neurodevelopmental disorders. However, cost-efficient strategies to identify at-risk populations and potential sources of exposure to inform mitigation efforts are limited. The objective of this study was to describe the spatial distribution and identify factors associated with Cd, Pb, Hg, and As concentrations in peripheral blood of pregnant women. Methods Heavy metals were measured in whole peripheral blood of 310 pregnant women obtained at gestational age ~12 weeks. Prenatal residential addresses were geocoded and geospatial analysis (Getis-Ord Gi* statistics) was used to determine if elevated blood concentrations were geographically clustered. Logistic regression models were used to identify factors associated with elevated blood metal levels and cluster membership. Results Geospatial clusters for Cd and Pb were identified with high confidence (p-value for Gi* statistic <0.01). The Cd and Pb clusters comprised 10.5 and 9.2 % of Durham County residents, respectively. Medians and interquartile ranges of blood concentrations (μg/dL) for all participants were Cd 0.02 (0.01–0.04), Hg 0.03 (0.01–0.07), Pb 0.34 (0.16–0.83), and As 0.04 (0.04–0.05). In the Cd cluster, medians and interquartile ranges of blood concentrations (μg/dL) were Cd 0.06 (0.02–0.16), Hg 0.02 (0.00–0.05), Pb 0.54 (0.23–1.23), and As 0.05 (0.04–0.05). In the Pb cluster, medians and interquartile ranges of blood concentrations (μg/dL) were Cd 0.03 (0.02–0.15), Hg 0.01 (0.01–0.05), Pb 0.39 (0.24–0.74), and As 0.04 (0.04–0.05). Co-exposure with Pb and Cd was also clustered, the p-values for the Gi* statistic for Pb and Cd was <0.01. Cluster membership was associated with lower education levels and higher pre-pregnancy BMI. Conclusions Our data support that elevated blood concentrations of Cd and Pb are spatially clustered in this urban environment compared to the surrounding areas. Spatial analysis of metals concentrations in peripheral blood or urine obtained routinely during prenatal care can be useful in surveillance of heavy metal exposure

Modeling of multi-strata forest fire severity using Landsat TM Data

a b s t r a c t Most of fire severity studies use field measures of composite burn index (CBI) to represent forest fire severity and fit the relationships between CBI and Landsat imagery derived differenced normalized burn ratio (dNBR) to predict and map fire severity at unsampled locations. However, less attention has been paid on the multi-strata forest fire severity, which represents fire activities and ecological responses at different forest layers. In this study, using field measured fire severity across five forest strata of dominant tree, intermediate-sized tree, shrub, herb, substrate layers, and the aggregated measure of CBI as response variables, we fit statistical models with predictors of Landsat TM bands, Landsat derived NBR or dNBR, image differencing, and image ratioing data. We model multi-strata forest fire in the historical recorded largest wildfire in California, the Big Sur Basin Complex fire. We explore the potential contributions of the post-fire Landsat bands, image differencing, image ratioing to fire severity modeling and compare with the widely used NBR and dNBR. Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing, and image ratioing. We predict and map multi-strata forest fire severity across the whole Big Sur fire areas, and find that the overall measure CBI is not optimal to represent multi-strata forest fire severity