Environmental impact classification of urban land cover from satellite remote sensing data

Abstract

A sustainable development of urban areas demands adequate information both spatially and punctually. The study focuses on the assessment of the potentialities of satellite remote sensing data to study environmental impact classification of urban land cover by fuzzy logic. The evaluation of urban landscapes is based upon different sub-functions which refer to landscape features such as soil, land-use, buildings, groundwater, biotope types. Mixed pixels result when the sensor's instantaneous field-of-view includes more than one land cover class on the ground. For mixed pixels, fuzzy classifiers can be used, which assign a pixel to several land cover classes in proportion to the area of the pixel that each class covers. These fraction values can be assigned to sub-pixels, based on the assumption of spatial dependence and the application of linear optimization techniques. A newly proposed sub-pixel mapping algorithm was first applied to a set of multispectral and multitemporal satellite data for Bucharest and Constantza urban areas in Romania. This paper describes how fuzzy logic can be applied to analysis of environmental impacts for urban land cover. Based on classified Landsat MSS, TM, SPOT, ASTER, SAR and MODIS data was performed a land cover classification and subsequent environmental quality analysis. Spectral signatures of different terrain features were used to separate and classify surface units of urban and sub-urban area. A complete set of criteria to evaluate and examine the urban environmental quality, including the air pollution condition indicators, water pollution indicators, solid waste treated indicators, noise pollution indicators, urban green space have been widely used to assess the urban environmental quality.