Thursday, August 6, 2009

PS 74-186: Vegetation structure and it’s relation to socioeconomic status in Columbia, Missouri

Jason T. Edwards, T. Kevin O'Donnell, and Charles H. Nilon. University of Missouri


How people manage their property can ultimately have an effect on urban biodiversity. We want to understand how people’s management decisions shape the pattern and structure of vegetation in residential neighborhoods.  This study compares neighborhoods with different socioeconomic characteristics by comparing vegetation pattern and structure at three different scales: neighborhood, block, and lot. We defined neighborhoods as Columbia, MO census tract block groups.  We used the block group data to group the 56 neighborhoods into four groups defined by income, home ownership, race, and education.  We randomly selected 2 neighborhoods from each group for our study.   We used biotope mapping, a hierarchical method of vegetation classification that combines land use and land cover, to describe neighborhood differences in vegetation types.  Biotope types were classified based on interpretation of aerial photos and ground-truthing. We used a cluster analysis to group the neighborhoods based on percent cover of each biotope type.  For the block scale analysis we surveyed five randomly selected, 100 meter transects in the residential areas of each neighborhood.  In these strips we counted the number of housing units, trees, trees over hanging the street, housing units with trees, housing units with shrubs, housing units with planted annuals and perennials, and  housing units with grass.  We will use cluster analysis to compare patterns of similarity or dissimilarity among the neighborhoods.
Results/Conclusions We mapped 180 polygons in the eight neighborhoods, and identified 46 biotope types.   Residential biotopes with lawn, trees and shrubs were the dominant vegetation types. These were divided into two subtypes, yard trees and fence rows, based on location of trees. We found that differences among these two subtypes were important in understanding patterns of similarity among the neighborhoods.  The two inner city neighborhoods formed a distinct cluster and had a higher percentage of the two subtypes.   Preliminary results from the block scale analysis show that inner city neighborhoods are distinct from other residential areas in the city.