Assessing spatiotemporal variations of greenness in the Baltimore

Assessing spatiotemporal variations of greenness in the Baltimore–Washington corridor area

Tang, Junmei / Chen, Fang / Schwartz, Stuart S.

Highlights ► The urban greenness pattern and its changes were investigated using linear spectral mixture analysis and fuzzy logic analysis. ► The sub-pixel classification offers a more detailed greenness maps. ► Urbanization and its associated socioeconomic developments are the major forces for the urban greenness change.

Abstract This paper aims to characterize and analyze the urban greenness change in the Baltimore–Washington corridor area. The general objectives for this study are: (1) to identify and analyze the urban greenness pattern and its changes, (2) to interpret and associate the change in urban greenness structure within its zoning area, (3) to characterize and discuss the influence of urbanization on the spatial distribution and temporal dynamic of urban green spaces. The linear spectral mixture analysis (LSMA) was adopted to extract the fraction maps of four endmembers (imperviousness, grassland, woodland, and soil) using Landsat Thematic Mapper (TM). The research results indicate that LSMA offers a better classified map than the traditional Maximum Likelihood Classification with an improved overall accuracy from 78.99% to 86.20%. The greenness dynamics within each census tract as well as planning zone were derived based on the each two successive fraction maps and fuzzy set concepts. The significant decrease of grassland in the business-based landscape (average green density decreased from 30% to 17%) with a slight decrease of woodland (from 63% to 60%) indicate the urbanization in the Baltimore–Washington corridor area impact its spatial morphology, structure, and ecological environment under the special socioeconomic development and reserve policies. The combination of remote sensing data and GIS zoning data provides us a comprehensive track on the pattern and process of urban greenness to deliver significant information for city planners and policy makers, as well as the unique dataset for other researcher to further evaluate the socioeconomic function of urban greenness.