Mapping Impervious Surface Area Using High Resolution Imagery: a Comparison of Object-based and per Pixel Classification

Impervious surface area is a key indicator of environmental quality. Satellite remote sensing of impervious surface has focused on subpixel analysis via various forms of statistical estimation, subpixel classification, and spectral mixture analysis, using medium resolution Landsat TM or ETM+ data. Maps of impervious surface area from these studies provide useful inputs to planning and management activities at city to regional scales. However, for local studies, large-scale, higher resolution maps are preferred. This study investigates digital classification techniques of mapping of impervious surface area using high resolution Quickbird satellite data. Two methods – object-based and per pixel classification – are explored and compared. The results provide information for accurate impervious surface mapping and estimation in high resolution imagery.