ANALYZING FOREST CHANGE AND POLICY IN WASHINGTON, DC SUBURBAN COUNTIES

Geographical approaches for landscape studies have emphasized the interpretation of landscape change as a cultural phenomenon, but often have neglected modern geographic techniques, such as remote sensing observations and quantitative spatial analysis, to characterize and understand landscape change. This study attempts to bridge these gaps by integrating a socio-cultural analysis of land use policy formation and quantitative assessments of land cover change to demonstrate how policy decisions can influence forest landscape patterns in suburban areas. Historical data from Montgomery County, MD and Fairfax County, VA, two counties adjacent to the Washington, DC urban core that have different governmental structures, were assembled and analyzed. A policy database was developed and analyzed using qualitative techniques, such as grounded theory and content analysis, to address questions related to policy formation and trends. Key findings included the identification of a strong link between land use policies and the broader environmental discourse, demonstrating that dominant cultural values are institutionalized in the development of land use policy. Furthermore, many policies related to forest management and preservation, particularly in recent decades, had a strong focus on protecting riparian forests. Land cover change between the late 1930s and 1998 was studied for local case study areas using time series of aerial photographs, and between 1990 and 2000 across both counties using satellite-derived land cover maps. Using a statistical technique, weights of evidence, the processes of new development, deforestation, and forest persistence were modeled. The results highlighted the role of biophysical variables, such as steep slopes and the presence of poorly drained soils, in constraining new development and enhancing forest persistence. However, the role of land use policies was also evident in enhancing forest persistence through the establishment of protected areas and riparian protection policies. This study demonstrated the impact that land use regulations can have on the evolution of forested landscape patterns within the built environment. The links between socio-cultural values and policy formation highlighted the institutional and cultural barriers that prevent rapid shifts in policy orientation, despite social and environmental problems that arise within a rapidly changing landscape

Resource Land Loss and Forest Vulnerability in the Chesapeake Bay Watershed

The contemporary pattern of urban development in industrialized countries is increasingly taking the form of low density, decentralized residential and commercial development. In the Chesapeake Bay watershed, which is located within the mid-Atlantic region of the United States, dispersed development patterns have been linked to habitat fragmentation and declining water quality. Our objectives were to document how this urbanization process has expanded throughout the watershed and to explore how lands comprising the natural resource base, particularly forests, have been replaced by a matrix of the built environment

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented

Meeting reports: Research on Coupled Human and Natural Systems (CHANS): Approach, Challenges, and Strategies

Understanding the complexity of human–nature interactions is central to the quest for both human well-being and global sustainability. To build an understanding of these interactions, scientists, planners, resource managers, policymakers, and communities increasingly are collaborating across wide-ranging disciplines and knowledge domains. Scientists and others are generating new integrated knowledge on top of their requisite specialized knowledge to understand complex systems in order to solve pressing environmental and social problems (e.g., Carpenter et al. 2009). One approach to this sort of integration, bringing together detailed knowledge of various disciplines (e.g., social, economic, biological, and geophysical), has become known as the study of Coupled Human and Natural Systems, or CHANS (Liu et al. 2007a, b). In 2007 a formal standing program in Dynamics of Coupled Natural and Human Systems was created by the U.S. National Science Foundation. Recently, the program supported the launch of an International Network of Research on Coupled Human and Natural Systems (CHANS-Net.org). A major kick-off event of the network was a symposium on Complexity in Human–Nature Interactions across Landscapes, which brought together leading CHANS scientists at the 2009 meeting of the U.S. Regional Association of the International Association for Landscape Ecology in Snowbird, Utah. The symposium highlighted original and innovative research emphasizing reciprocal interactions between human and natural systems at multiple spatial, temporal, and organizational scales. The presentations can be found at ‹http://chans- net.org/Symposium_2009.aspx›. The symposium was accompanied by a workshop on Challenges and Opportunities in CHANS Research. This article provides an overview of the CHANS approach, outlines the primary challenges facing the CHANS research community, and discusses potential strategies to meet these challenges, based upon the presentations and discussions among participants at the Snowbird meeting

Calibrating and Validating a Simulation Model to Identify Drivers of Urban Land Cover Change in the Baltimore, MD Metropolitan Region

We build upon much of the accumulated knowledge of the widely used SLEUTH urban land change model and offer advances. First, we use SLEUTH’s exclusion/attraction layer to identify and test different urban land cover change drivers; second, we leverage SLEUTH’s self-modification capability to incorporate a demographic model; and third, we develop a validation procedure to quantify the influence of land cover change drivers and assess uncertainty. We found that, contrary to our a priori expectations, new development is not attracted to areas serviced by existing or planned water and sewer infrastructure. However, information about where population and employment growth is likely to occur did improve model performance. These findings point to the dominant role of centrifugal forces in post-industrial cities like Baltimore, MD. We successfully developed a demographic model that allowed us to constrain the SLEUTH model forecasts and address uncertainty related to the dynamic relationship between changes in population and employment and urban land use. Finally, we emphasize the importance of model validation. In this work the validation procedure played a key role in rigorously assessing the impacts of different exclusion/attraction layers and in assessing uncertainty related to population and employment forecasts

RESOURCE LAND LOSS AND FOREST VULNERABILITY IN THE CHESAPEAKE BAY WATERSHED 1

ABSTRACT The contemporary pattern of urban development in industrialized countries is increasingly taking the form of low density, decentralized residential and commercial development. In the Chesapeake Bay watershed, which is located within the mid-Atlantic region of the United States, dispersed development patterns have been linked to habitat fragmentation and declining water quality. Our objectives were to document how this urbanization process has expanded throughout the watershed and to explore how lands comprising the natural resource base, particularly forests, have been replaced by a matrix of the built environment. We accomplished this by mapping impervious surface cover (houses, roads, etc.) across the ~168,000 km 2 area using a time series of satellite imagery. We calculated metrics of land use change and used these to estimate the loss of resource lands across the region. We conservatively estimate that 334 km 2 of forest, 888 km 2 of agriculture and 2 km 2 of wetlands have been converted to impervious surfaces between 1990 and 2000. We also used the time series to calibrate a spatial model of urban land use change, and forecasted future development patterns in Maryland out to 2030 under different policy scenarios. Using Maryland Department of Natural Resources' (DNR) Strategic Forest Lands Assessment (SFLA), which evaluates forest resources in terms of their economic and ecologic value, and Maryland's Green Infrastructure, which identifies ecologically valuable patches of contiguous forests and wetlands, we evaluated the vulnerability of natural resources in Maryland. Threats, associated with loss and fragmentation, were identified

Research Article Analysis of scale dependencies in an urban land-use-change model

Different processes shaping land-use patterns are observed at different scales. In land-use modelling, scale can influence the measurement and quantitative description of land-use patterns and can therefore significantly impact the behaviour of model parameters that describe land-use change processes. We present results of a rigorous sensitivity analysis of a cellular urban land-usechange model, SLEUTH, testing its performance in response to varying cell resolutions. Specifically, we examine the behaviour of each type of urban growth rule across different cell sizes, and explore the model’s ability to capture growth rates and patterns across scales. Our findings suggest that SLEUTH’s sensitivity to scale extend beyond issues of calibration. While the model was able to capture the rate of growth reliably across all cell sizes, differences in its ability to simulate growth patterns across scales were substantial. We also observed significant differences in the sensitivity of the growth rules across cell sizes, indicating that SLEUTH may perform better at certain cell sizes than at others. These findings emphasize the importance of scale considerations in land-use-change modelling research, particularly in terms of determining the relevant and appropriate scales of enquiry for the processes being simulated. 1

Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore – Washington metropolitan area

Declining water quality in the Chesapeake Bay estuary is in part the result of disruptions in the hydrological system caused by urban and suburban development throughout its 167 000 km 2 watershed. A modeling system that could provide regional assessments of future development and explore the potential impacts of different regional management scenarios would be useful for a wide range of applications relevant to the future health of the Bay and its tributaries. We describe and test a regional predictive modeling system that could be used to meet these needs. An existing cellular automaton model, SLEUTH, was applied to a 23 700 km 2 area centered on the Washington - Baltimore metropolitan region, which has experienced rapid land-use change in recent years. The model was calibrated using a historic time series of developed areas derived from remote sensing imagery, and future growth was projected out to 2030 assuming three different policy scenarios: (1) current trends, (2) managed growth, and (3) ecologically sustainable growth. The current trends scenario allowed areas on the urban fringe that are currently rural or forested to be developed, which would have implications for water quality in the Chesapeake Bay and its tributaries. The managed growth and ecologically sustainable scenarios produced growth patterns that were more constrained and which consumed less natural resource land. This application of the SLEUTH model demonstrates an ability to address a range of regional planning issues, but spatial accuracy and scale sensitivity are among the factors that must be further considered for practical application.

Land Use Dynamics in the Delaware River Basin

The Delaware River Basin is an important region that hosts more than 8.2 million residents and provides ecosystem services that support multiple commercial, industrial, recreational and residential uses. The purpose of the project is to provide useful tools to decision makers across the 43 counties that intersect the basin in Pennsylvania, New York, New Jersey, Delaware, and Maryland. The tools include: a) a high-resolution (1m) LiDAR-based land cover dataset for all 43 counties that cover, in whole or in part, the basin; b) geoprocessing tools for evaluating and visualizing alternate scenarios of future land cover changes; and c) a feasibility study that gauges the willingness and abilities of stakeholder groups to participate in a long-term monitoring program. This project is one of many funded by the William Penn Foundation to develop tools and on-the-ground projects in support of Basin-wide water quality restoration and protection. In this paper we will describe our overall project, which includes collaborations with the University of Vermont and the USGS, as well as what we have learned so far regarding land use dynamics in the Basin

Squeezed from All Sides: Urbanization, Invasive Species, and Climate Change Threaten Riparian Forest Buffers

Streamside forests of urbanizing coastal regions lie at the nexus of global changes: rising sea levels, increasing storm surge, expanding urban development, and invasive species. To understand how these combined stressors affect forest conditions, we identified forest patches adjacent to urban land, analyzed adjacent land cover, modeled forest inundation, and sampled 100 sites across the Chesapeake Bay and Delaware Bay watersheds. We found that the majority of forest patches are adjacent to urban land and projected flooding will affect 8–19% of regional forested land. We observed non-native invasive plants in 94% of forest plots. Trees were predominantly native, but over half of shrub stems were invasive species and more than 80% of plots contained invasive woody vines. Disturbance of human origin was correlated with abundance of invasive trees. Signs of deer activity were common. Richness and number of growth forms of invasive plants were related to adjacent agricultural land cover. These data reveal that streamside forests are impacted by the interacting stressors of urbanization, climate change, and invasive species spread. Our results emphasize the importance of protection and restoration of forests in urban regions and point to the need for a social-ecological systems approach to improve their condition