The Application of Remote Sensing to Identify and Measure Sealed Areas in Urban Environments

Numerous studies have used satellite images for mapping urban land cover and land use along with modelling green spaces and surface impermeability. Recently, monitoring the percentage of sealed soils in urban environments is of great interest as a key indicator of sustainable landuse. The aim of this research is to identify an appropriate methodology to classify sealed soil and green space surfaces in urban environments with the use of satellite remotely sensed data. The study area is the city of Cambridge, UK. The percentage of sealed soils, within 18 randomly selected sample segments (250 x 250 m), was interpreted visually from the aerial photography and the Ordnance Survey (OS) MasterMap polygons attributed accordingly; the percentage was limited to a precision of 25%, i.e. 0, 25, 50, 75 and 100%. The results were compared with a maximum likelihood classification of Normalised Difference Vegetation Index (NDVI) images derived from QuickBird data integrated with the OS MasterMap and summarised using confusion matrices. The overall mapping accuracy was estimated to be approximately 75%. The low map accuracy is due to coarse precision of the aerial photo interpretation (API) and the use of pixel based classification procedures. The described methodology are the preliminary results of an on going research study. In the future, object-based classifiers (eCognition) will be investigated to provide an objective approach of the visual interpretation and improve efficiency and accuracy. eCognition is also anticipated to be used as the main classifier of the satellite image analysis.