The spatial distribution of urban vegetation
This topical area focuses on understanding the spatial distribution and characteristics of urban vegetation. There are two primary sub-areas of interest within this topic: 1)understanding the socio-economic, environmental and infrastructural determinants of where vegetation occurs and how it is managed and; 2) developing new methodologies for characterizing and mapping urban vegetation from remotely sensed data.
Topic 1: Understanding the socio-economic, environmental and infrastructural determinants of where vegetation occurs and how it is managed text
It has been established that urban vegetation, particularly canopy trees, delivers a wide range of benefits, from shading and cooling, to property value increases, to wildlife habitat, to stormwater mitigation. Given this, the spatial distribution of vegetation is an important equity and distributional issue, and understanding why vegetation is found where it is can help to address inequities.
Our research used high-resolution remotely-sensed data combined with parcel and building footprint GIS data to assess where vegetation is growing, adjusting for the fact that existing building footprints limit planting opportunities in some areas more than others. To account for these disparate opportunities, we generated two measures: "possible stewardship," which is the proportion of private land that does not have built structures on it and hence has the possibility of supporting vegetation, and "realized stewardship," which is the proportion of possible stewardship land upon which vegetation is growing. These measures were calculated at the parcel level and averaged by US Census block group. Realized stewardship was further defined by proportion of tree canopy and grass. Expenditures on yard supplies and services, available by block group, were used to help understand where vegetation condition appears to be the result of current activity, past legacies, or abandonment. Claritas PRIZM™ market segmentation data were tested as categorical predictors of possible and realized stewardship and yard expenditures. The segmentations we used were hierarchically clustered into 5, 15, and 62 categories, corresponding respectively to population density, socio-economic status (based on income and education and, since it is cumulative, population density), and lifestyle clusters (based cumulatively on density, socio-economics and additional factors such as family size and marriage status).
We found that the socio-economic segmentation best predicted variation in possible stewardship and the lifestyle best predicted variation in realized stewardship. These results were further analyzed by regressing each dependent variable against a set of continuous variables reflective of individual component characteristics of each of the three PRIZM groupings. Among the variables found to be significantly associated with a decrease in possible stewardship levels was: population density, % vacancy, and crime. Income was positively associated with possible stewardship. Housing age displayed a parabolic relationship with possible stewardship, showing that the amount of possible stewardship peaked for houses built in the late sixties. This confirms that possible stewardship is largely driven by population density and social stratification. Variables found to be significantly associated with a decrease in realized stewardship levels included population density and vacant houses. Those variables positively associated with realized stewardship included home value, % African American share of population, education levels, % single family home share, % married, and % protected land in the neighborhood. Realized stewardship also related parabolically to housing age, with the amount of realized stewardship peaking for homes from the early to mid-sixties. Yard
In answering the second question this study found that a combination of lot size and a remotely sensed index of lawn greenness could be used to successful predict levels of fertilizer application, as derived from a homeowner survey. However, the relationship was nonlinear, suggesting that while the amount of fertilizer applied goes up with yard size, it goes up at a diminishing rate. This means that the quantity of fertilizer applied per unit area goes down with yard size. It is hypothesized that this is due to the fact that as home lots get larger, the yards are often split into actively-managed and less actively-managed portions, meaning that less fertilizer is used per unit area for large lots. This research demonstrates that remote sensing and GIS data may allow managers to determine areas where lawn fertilization is heavy without actually surveying people.
Topic 2: Developing new methodologies for characterizing and mapping urban vegetation from remotely sensed data
This area of research has involved the development of a number of new methodologies for mapping vegetation and impervious surface at the sub-parcel level. Using object-oriented remote sensing classification methods, this methodological approach takes high-resolution aerial imagery and combines it with GIS data to segment the landscape into small polygons representing areas of homogeneous cover. Once these polygons are created, a "knowledge-base" of expert-driven rules is then used to classify them based on cover type. We then used this information to summarize land cover at the parcel level and to conduct land use change detection at a fine scale. We also were able to integrate Light Detection and Ranging (LiDAR) data into this methodology to allow us to summarize tree heights at the parcel level.
Austin Troy, J. Morgan Grove, Weiqi Zhou, Jarlath O'Neil-Dunne, Jennifer Jenkins, Mary Cadenasso, Ganlin Huang, Steward Pickett
W. Zhou and A. Troy. 2009. Development of an object-based framework for classifying and inventorying human-dominated forest ecosystems. International Journal of Remote Sensing. 30(23):6343-6360.
W. Zhou, A. Troy, J.M. Grove and J. Jenkins. 2009. Can money buy green? Demographic and socioeconomic predictors of lawncare expenditure and lawn greenness in urban residential areas. Society and Natural Resources. 22(8):744-760.
W. Zhou, G. Huang, A. Troy, and M. Cadenasso. 2009. Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: a comparison study. Remote Sensing of Environment. 113(8):1769-1777.
W. Zhou, and A. Troy. 2008. An Object-oriented Approach for analyzing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing. 29 (11):3119-3135.
W. Zhou, A. Troy and J.M Grove. 2008. Modeling residential lawn fertilization practices: Integrating high resolution remote sensing with socioeconomic data. Environmental Management. 41(5):742-752
W. Zhou, A. Troy, and J.M Grove. 2008. Object-based land cover classification and change analysis in the Baltimore Metropolitan Area using multi-temporal high resolution remote sensing data. Sensors. 8: 1613-1636.
A. Troy, J.M. Grove, J. O'Neil-Dunne, M. Cadenasso, and S. Pickett. 2007. Predicting opportunities for greening and patterns of vegetation on private urban lands. Environmental Management. 40(3): 394-412.
J. M. Grove, A. Troy, J. O'Neil-Dunne, S. Pickett and M. Cadenasso. 2006. Multi-dimensional characteristics of urban households and its implications for the vegetative structure of urban ecosystems. Ecosystems. 9(4): 578-597.
A. Troy and W. Zhou. 2007. Creating a parcel level database from high resolution imagery. American Planning Association InfoText (the quarterly newsletter of the Information Technology Division of the APA). Fall 2006: Issue 87
Weiqi Zhou. 2007. Classifying and analyzing human-dominated ecosystems : integrating high-resolution remote sensing and socioeconomic data . University of Vermont. PhD dissertation.
A. Troy. October 2011. Seeing the Pattern for the Pixels: Extracting Meaning from Massive Spatial Data Sets. TEDx Talk, University of Vermont, October 2011.
A. Troy , M. Grove, J. O'Neil-Dunne, M. Cadenasso and S.T.A. Picket. April 2007. Predicting opportunities for greening and vegetation patterns on urban private land. Association of American Geographers Annual Meeting. San Francisco, CA.
A. Troy. Spatial Dynamics of Urban Vegetation Baltimore, MD. October 2005. Ecological Complexity and Ecosystem Services: Opportunities for China-US Collaboration; 2nd international workshop. Burlington, VT.