Abstract
This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.
Highlights
Urban areas are currently the most rapidly changing types of land covers, even though they represent only a low percentage of the global land surface [32]
The remote sensing characterization of urban environments can be complicated for several reasons: (i) urban land-cover classes are not well spectrally distinct, resulting in considerable confusion between classes [36,38,39,40], (ii) the physical structure of land-use classes varies from site to site due to the different roofing and paving materials and building typology [8,21,32,34], (iii) urban areas are heterogeneous and most pixels, at the satellite spatial resolution of 30 m/pixel, appear mixed with varying proportions of different components and/or materials [33]
We have outlined the analysis results of a preliminary study aimed at verifying the efficiency of hyperspectral remote sensing data for mapping complex urban environments and for the production of accurate land cover maps
Summary
Urban areas are currently the most rapidly changing types of land covers, even though they represent only a low percentage of the global land surface [32]. Their monitoring is one of the most relevant issues concerning the evaluation of the human impact on the environment. For this purpose, remote sensing imagery can provide a timely and synoptic view of urban land covers, as well as a tool to monitor changes in urbanizing landscapes. The most common approach for characterizing urban environments using remote sensing imagery are the land-cover and land-use classifications [6,13,36]. The remote sensing characterization of urban environments can be complicated for several reasons: (i) urban land-cover classes are not well spectrally distinct, resulting in considerable confusion between classes [36,38,39,40], (ii) the physical structure of land-use classes varies from site to site due to the different roofing and paving materials and building typology [8,21,32,34], (iii) urban areas are heterogeneous and most pixels, at the satellite spatial resolution of 30 m/pixel, appear mixed with varying proportions of different components and/or materials [33].
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