Brownfield identification: different approaches for analysing data detected by means of remote sensing

This paper aims to show different techniques for identifying brownfields and for monitoring based on remote sensing. If brownfields are associated with urban wastes such as the demolition of buildings, dangerous materials or contaminants will be involved with them. Hazard and health risks increase, so the administration policy needs to detect, at an early stage, dangerous materials or contaminants and to manage the cleaning and recovery of these land areas. A detailed classification of the land and the identification of these typologies of brownfields can be carried out by means of hyperspectral remote sensing using the airborne MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor of the Laboratory of CNR (LARA). Thanks to MIVIS, we can detect many different materials. The subsequent integration of data into GIS allows identification of brownfields through our specific software tools that determine the contours of objects and evaluate the statistical parameters of the processed images. Moreover, other GIS tools correlate data in order to evaluate the extent and derivative parameters of the same brownfields. When brownfields are associated with urban or sub-urban abandoned industrial properties or a land with neglected vegetation, i.e. with the evident characteristic of land abandoned or under-used; then the previous method is useless. These brownfields are missing specific hazardous substances or contaminants and buildings and are generally in good condition. For these last cases, an alternative process should be implemented. A higher geometric resolution than MIVIS should be used, putting in evidence small objects in the soil and so IKONOS or QuickBird satellite imagery are required. In this case, brownfield identification will be carried out by means of object-oriented algorithms and the mutual relationship between them.