Alessia Allegrini

Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation

The ability to classify asphalt surfaces is an important goal for the selection of suitable non-variant targets as pseudo-invariant targets during the calibration/validation of remotely-sensed images. In addition, the possibility to recognize different types of asphalt surfaces on the images can help optimize road network management. This paper presents a multi-resolution study to improve asphalt surface differentiation using field spectroradiometric data, laboratory analysis and remote sensing imagery. Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne data and multispectral images, such as Quickbird and Ikonos, were used. From scatter plots obtained by field data using λ = 460 and 740 nm, referring to MIVIS Bands 2 and 16 and Quickbird and Ikonos Bands 1 and 4, pixels corresponding to asphalt covering were identified, and the slope of their interpolation lines, assumed as asphalt lines, was calculated. These slopes, used as threshold values in the Spectral Angle Mapper (SAM) classifier, obtained an overall accuracy of 95% for Ikonos, 98% for Quickbird and 93% for MIVIS. Laboratory investigations confirm the existence of the asphalt line also for new asphalts, too.

Speedy methodology for geometric correction of MIVIS data

Abstract Since MIVIS images are generated from an airborne sensor they are affected by geometric distortion. In this paper a speedy procedure for the geometric correction of hyperspectral MIVIS images using algorithms such as the polynomial model (PM) and the model of rational functions (RFM) is presented. The aim of the work is to achieve an effective geometric correction of MIVIS images using an optimal compromise between result precision and elaboration time. This method could be also applied to large areas (thousands of square kilometers).

Anthropogenic activities monitoring by a multi-sensor approach: a case study of Rossano (CS) landfill

In this paper the landfill located to Rossano (CS) is analyzed through an integrated approach by the use of different remote sensed data from 1994 to 2010. In particular, the Multispectral Visible and Infrared Imaging Spectrometer (MIVIS) airborne sensor, orthophotos and Google Earthtm satellite image are used. The integrated multi-temporal data and downscaling analysis have identified several evidences such as the increase of land consumption. The Normalized Difference Vegetation Index (NDVI) of MIVIS image allows to observe the distribution of exposed soil and vegetated areas. At the same time, MIVIS thermal data shows some superficial thermal anomalies which highlighted these changes. Finally, this study shows how this approach can be useful to support both monitoring studies of land consumption as well as planning environmental campaigns in such areas.

Analysis of anthropic activities by optical remote sensing data at different spatial, spectral and temporal resolutions

This paper describes a remote sensing based downscaling approach for the analysis of the area affected by anthropic activities as quarrying and landfill. We selected the South-East flank of Mt. Vesuvius National Park as study area because in the last decades there have been a strong anthropic pressure with mining and municipal solid waste dumping as the main activities. The changes occurred were analysed by optical remote sensing at different spatial and spectral resolution. These activities had an environmental impact that is highlighted by integration of multi-source data.The multi- and hyper-spectral remote sensing data were adopted to study spectral indices and spatial patterns. The spectral response of targets supported the interpretation of stress conditions and other environmental anomalies in specific zones. Landsat, MIVIS, aerial photos and thematic maps were fused in a GIS for environmental analysis providing some Warning Zones defined in core and neighbouring of the anthropic area.

Metropolitan area of Rome: an integrated vegetation study by remote sensing and field data

The objective of this work is to assess the impact of environmental stresses, both natural (water stress, due to summer dryness typical of the Mediterranean climate) and anthropogenic (atmospheric pollution), on the vegetation in sites of great naturalistic value and under strong anthropic pressure in the Rome metropolitan area. The species Quercus ilex L. (holm oak), an evergreen oak, has been studied through ecophysiological measurements performed in the field and Landsat TM images relating to the Rome metropolitan area. The results obtained show a greater degree of structural and functional alteration in individual holm oaks within the urban area compared with those in the protected areas outside of the central zone. In particular, in 1993 the main stress factor for vegetation is attributable to climatic conditions, whereas in 1998 ozone atmospheric concentration caused an evident alteration of the parameters studied.