Jarlath O'Neil-Dunne

Data and Methods Comparing Social Structure and Vegetation Structure of Urban Neighborhoods in Baltimore, Maryland

ABSTRACT Recent advances in remote sensing and the adoption of geographic information systems (GIS) have greatly increased the availability of high-resolution spatial and attribute data for examining the relationship between social and vegetation structure in urban areas. There are several motivations for understanding this relationship. First, the United States has experienced a significant increase in the extent of urbanized land. Second, urban foresters increasingly recognize their need for data about urban forestry types, owners and property regimes, and associated social goods, benefits, and services. Third, previous research has focused primarily on the distribution of vegetation cover or diversity. However, little is known about (1) whether vegetation structure varies among urban neighborhoods and (2) whether the motivations, pathways, and capacities for vegetation management vary among households and communities. In this article, we describe novel data and methods from Baltimore, MD, and the Baltimore Ecosystem Study (BES) to address these two questions.

Ecological homogenization of urban USA

A visually apparent but scientifically untested outcome of land-use change is homogenization across urban areas, where neighborhoods in different parts of the country have similar patterns of roads, residential lots, commercial areas, and aquatic features. We hypothesize that this homogenization extends to ecological structure and also to ecosystem functions such as carbon dynamics and microclimate, with continental-scale implications. Further, we suggest that understanding urban homogenization will provide the basis for understanding the impacts of urban land-use change from local to continental scales. Here, we show how multi-scale, multi-disciplinary datasets from six metropolitan areas that cover the major climatic regions of the US (Phoenix, AZ; Miami, FL; Baltimore, MD; Boston, MA; Minneapolis–St Paul, MN; and Los Angeles, CA) can be used to determine how household and neighborhood characteristics correlate with land-management practices, land-cover composition, and landscape structure and ecosystem functions at local, regional, and continental scales.

An object-based system for LiDAR data fusion and feature extraction

In urbanized areas of the developed world, light detection and ranging (LiDAR) exists alongside a wealth of other geospatial information. Despite this bounty, high-resolution land cover is still lacking in many urban areas. This can be attributed to the complexity of many landscapes, the volume of available data and the challenges associated with combining data that were acquired over differing time periods using inconsistent standards. Object-based approaches are ideal for overcoming these limitations. We describe the design, development and deployment of an object-based system that incorporated LiDAR, imagery and vector data sets to develop a comprehensive, multibillion-pixel land-cover data set for the City of Philadelphia. A novel approach using parallel processing allowed us to distribute the feature extraction load to multiple cores, providing massive gains in efficiency and permitting continual modification of the expert system until the accuracy goals of the project were achieved.

Assessing the homogenization of urban land management with an application to US residential lawn care

Significance This paper offers conceptual and empirical contributions to sustainability science in general and urban-ecological studies in particular. We present a new analytical framework for classifying socioecological measures along a homogenization–differentiation spectrum. This simple 2 × 2 matrix highlights the multiscale nature of the processes and outcomes of interest. Our application of the conceptual framework produces needed empirical insights into the extent to which land management appears to be homogenizing in differing biophysical settings. Results suggest that US lawn care behaviors are more differentiated in practice than in theory. Thus even if the biophysical outcomes of urbanization are homogenizing, managing the associated sustainability implications may require a multiscale, differentiated approach. Changes in land use, land cover, and land management present some of the greatest potential global environmental challenges of the 21st century. Urbanization, one of the principal drivers of these transformations, is commonly thought to be generating land changes that are increasingly similar. An implication of this multiscale homogenization hypothesis is that the ecosystem structure and function and human behaviors associated with urbanization should be more similar in certain kinds of urbanized locations across biogeophysical gradients than across urbanization gradients in places with similar biogeophysical characteristics. This paper introduces an analytical framework for testing this hypothesis, and applies the framework to the case of residential lawn care. This set of land management behaviors are often assumed—not demonstrated—to exhibit homogeneity. Multivariate analyses are conducted on telephone survey responses from a geographically stratified random sample of homeowners (n = 9,480), equally distributed across six US metropolitan areas. Two behaviors are examined: lawn fertilizing and irrigating. Limited support for strong homogenization is found at two scales (i.e., multi- and single-city; 2 of 36 cases), but significant support is found for homogenization at only one scale (22 cases) or at neither scale (12 cases). These results suggest that US lawn care behaviors are more differentiated in practice than in theory. Thus, even if the biophysical outcomes of urbanization are homogenizing, managing the associated sustainability implications may require a multiscale, differentiated approach because the underlying social practices appear relatively varied. The analytical approach introduced here should also be productive for other facets of urban-ecological homogenization.

Exchanges across Land‐Water‐Scape Boundaries in Urban Systems

Conservation in urban areas typically focuses on biodiversity and large green spaces. However, opportunities exist throughout urban areas to enhance ecological functions. An important function of urban landscapes is retaining nitrogen thereby reducing nitrate pollution to streams and coastal waters. Control of nonpoint nitrate pollution in urban areas was originally based on the documented importance of riparian zones in agricultural and forested ecosystems. The watershed and boundary frameworks have been used to guide stream research and a riparian conservation strategy to reduce nitrate pollution in urban streams. But is stream restoration and riparian‐zone conservation enough? Data from the Baltimore Ecosystem Study and other urban stream research indicate that urban riparian zones do not necessarily prevent nitrate from entering, nor remove nitrate from, streams. Based on this insight, policy makers in Baltimore extended the conservation strategy throughout larger watersheds, attempting to restore functions that no longer took place in riparian boundaries. Two urban revitalization projects are presented as examples aimed at reducing nitrate pollution to stormwater, streams, and the Chesapeake Bay. An adaptive cycle of ecological urban design synthesizes the insights from the watershed and boundary frameworks, from new data, and from the conservation concerns of agencies and local communities. This urban example of conservation based on ameliorating nitrate water pollution extends the initial watershed‐boundary approach along three dimensions: 1) from riparian to urban land‐water‐scapes; 2) from discrete engineering solutions to ecological design approaches; and 3) from structural solutions to inclusion of individual, household, and institutional behavior

A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion

The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy maps that document baseline conditions or inform tree-planting programs, limiting effective study and management. This paper describes a GEOBIA approach to tree-canopy mapping that relies on existing public investments in LiDAR, multispectral imagery, and thematic GIS layers, thus eliminating or reducing data acquisition costs. This versatile approach accommodates datasets of varying content and quality, first using LiDAR derivatives to identify aboveground features and then a combination of LiDAR and imagery to differentiate trees from buildings and other anthropogenic structures. Initial tree canopy objects are then refined through contextual analysis, morphological smoothing, and small-gap filling. Case studies from locations in the United States and Canada show how a GEOBIA approach incorporating data fusion and enterprise processing can be used for producing high-accuracy, high-resolution maps for large geographic extents. These maps are designed specifically for practical application by planning and regulatory end users who expect not only high accuracy but also high realism and visual coherence.

Object-based high-resolution land-cover mapping

There has been a marked increase in availability of high-resolution remotely-sensed datasets over the past eight years. The ability to efficiently extract accurate and meaningful land-cover information from these datasets is crucial if the full potential of these datasets is to be harnessed. Land-cover datasets, particularly high-resolution ones, must be statistically accurate and depict a realistic representation of the landscape if they are to be used by decision makers and trusted by the general public. Furthermore, if such datasets are to be accessible and relevant, mechanisms must exist that facilitate cost-effective and timely production. Object-based image analysis (OBIA) techniques offer the greatest potential for generating accurate and meaningful land-cover datasets in an efficient manner. They overcome the limitations of traditional pixel-based classification methods by incorporating not only spectral data but also spatial and contextual information, and they offer substantial efficiency gains compared to manual interpretation. Drawing on our experience in applying OBIA techniques to high-resolution data, we believe any automated approach to land-cover mapping must: 1) effectively replicate the human image analyst; 2) incorporate datasets from multiple sources; and 3) be capable of processing large datasets. To meet this functionality, an operational OBIA system should: 1) employ expert systems; 2) support vector and raster datasets; and 3) leverage enterprise computing architecture.

Ecological homogenization of residential macrosystems

Similarities in planning, development and culture within urban areas may lead to the convergence of ecological processes on continental scales. Transdisciplinary, multi-scale research is now needed to understand and predict the impact of human-dominated landscapes on ecosystem structure and function.

Satisfaction, water and fertilizer use in the American residential macrosystem

Residential yards across the US look remarkably similar despite marked variation in climate and soil, yet the drivers of this homogenization are unknown. Telephone surveys of fertilizer and irrigation use and satisfaction with the natural environment, and measurements of inherent water and nitrogen availability in six US cities (Boston, Baltimore, Miami, Minneapolis-St. Paul, Phoenix, Los Angeles) showed that the percentage of people using irrigation at least once in a year was relatively invariant with little difference between the wettest (Miami, 85%) and driest (Phoenix, 89%) cities. The percentage of people using fertilizer at least once in a year also ranged narrowly (52%–71%), while soil nitrogen supply varied by 10x. Residents expressed similar levels of satisfaction with the natural environment in their neighborhoods. The nature and extent of this satisfaction must be understood if environmental managers hope to effect change in the establishment and maintenance of residential ecosystems.

Comparative assessment of methods for estimating tree canopy cover across a rural-to-urban gradient in the mid-Atlantic region of the USA

Tree canopy cover significantly affects human and wildlife habitats, local hydrology, carbon cycles, fire behavior, and ecosystem services of all types. In addition, changes in tree canopy cover are both indicators and consequences of a wide variety of disturbances from urban development to climate change. There is growing demand for this information nationwide and across all land uses. The extensive inventory plot system managed by the USDA Forest Service Forest Inventory and Analysis (FIA) offers a unique opportunity for acquiring unbiased tree canopy cover information across broad areas. However, the estimates it produces had not yet been examined for comparative accuracy with other sources. In this study, we compared four different methods readily available and with significant potential for application over broad areas. The first two, field-collected and photointerpreted, are currently acquired by FIA on approximately 44,000 plots annually nationwide. The third method is a stem-mapping approach that models tree canopy cover from variables regularly measured on forested plots and is efficient enough to calculate nationwide. The fourth is a Geographic-Object-Based Image Analysis (GEOBIA) approach that uses both high-resolution imagery and leaf-off LiDAR data and has reported very high accuracies and spatial detail at state-wide levels of application. Differences in the spatial and temporal resolution and coverage of these four datasets suggest that they could provide complementary information if their relationships could be better understood. Plot- and county-level estimates of tree canopy cover derived from each of the four data sources were compared for 11 counties in Maryland, Pennsylvania, and West Virginia across a range of urbanization levels. We found high levels of systematic agreement between field and photointerpreted, stem-mapped and field, photointerpreted and GEOBIA estimates. In several cases, the relationship changed with the level of tree canopy cover. GEOBIA produced the highest tree cover estimates of all the methods compared. Results are discussed with respect to known differences between the methods and ground conditions found in both forest and nonforest areas.