Rosa Coluzzi

On the LiDAR contribution for the archaeological and geomorphological study of a deserted medieval village in Southern Italy

Airborne laser scanning (ALS) is an optical measurement technique for obtaining high-precision information about the Earths surface including basic terrain mapping (digital terrain model, bathymetry, corridor mapping), vegetation cover (forest assessment and inventory) and coastal and urban areas. Recent studies examined the possibility of using ALS in archaeological investigations to identify earthworks, although the ability of ALS measurements in this context has not yet been studied in detail. This paper focuses on the potential of the latest generation of airborne ALS for the detection and the spatial characterization of micro-topographic relief linked to archaeological and geomorphological features. The investigations were carried out near Monteserico, an archaeological area in the Basilicata region (Southern Italy) which is characterized by complex topographical and morphological features. The study emphasizes that the DTM-LiDAR data are a powerful instrument for detecting surface discontinuities relevant for investigating geomorphological processes and cultural features. The LiDAR survey allowed us to identify the urban shape of a medieval village, by capturing the small differences in height produced by surface and shallow archaeological remains (the so-called shadow marks) which were not visible from ground or from optical dataset. In this way, surface reliefs and small elevation changes, linked to geomorphological and archaeological features, have been surveyed with great detail.

Early Identification of Land Degradation Hotspots in Complex Bio-Geographic Regions

The development of low-cost and relatively simple tools to identify emerging land degradation across complex regions is fundamental to plan monitoring and intervention strategies. We propose a procedure that integrates multi-spectral satellite observations and air temperature data to detect areas where the current status of local vegetation and climate shows evident departures from the mean conditions of the investigated region. Our procedure was tested in Basilicata (Italy), which is a typical bio-geographic example of vulnerable Mediterranean landscape. We grouped Landsat TM/ETM+ NDVI and air temperature (T) data by vegetation cover type to estimate the statistical distributions of the departures of NDVI and T from the respective land cover class means. The pixels characterized by contextual left tail NDVI values and right tail T values that persisted in time (2002–2006) were classified as critical to land degradation. According to our results, most of the critical areas (88.6%) corresponded to forests affected by erosion and to riparian buffers that are shaped by fragmentation, as confirmed by aerial and in-situ surveys. Our procedure enables cost-effective screenings of complex areas able to identify raising hotspots that require urgent and deeper investigations.

Urban Pattern Morphology Time Variation in Southern Italy by Using Landsat Imagery

This paper analyses the spatial characterization of urban expansion by using spatial fractal analysis applied to multidate Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) satellite imagery. The investigation was focused on four small towns in southern Italy, for which the border was extracted from NASA Landsat images acquired in 1976 (MSS), in 1991 (TM) and 1999 (ETM). The border was analyzed using the box counting method, which is a well-know technique to estimate the spatial fractal dimension, that quantifies the shape irregularity of an object. The obtained results show that the fractal dimension of the border of the investigated towns is a good indicator of the dynamics of the regular/irregular urban expansion.

Mapping Forest fuel types by using Satellite ASTER data and neural nets

A reliable mapping of fuel types is very important for computing fire hazard and risk and simulating fire growth and intensity across a landscape. Due to the complex nature of fuel characteristic a fuel map is considered one of the most difficult thematic layers to build up especially for large areas. The advent of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. In order to ascertain how well ASTER data can provide an exhaustive classification of fuel properties a sample area characterized by mixed vegetation covers was analysed. The selected sample areas has an extension at around 60 km2 and is located inside the Sila plateau in the Calabria Region (South of Italy). Fieldwork fuel type recognitions, performed before, after and during the acquisition of remote sensing ASTER data, were used as ground-truth dataset to assess the results obtained for the considered test area. Results from our analysis showed that the use ASTER data provided a valuable characterization and mapping of fuel types with a classification accuracy higher than 78%.

On the estimation of fire severity using satellite ASTER data and spatial autocorrelation statistics

What are the ecological effects of fires? The evaluation of fire-affected areas and fire severity is of primary importance to answer this question, because fire strongly affects the ecological processes, such as, productivity level, creation of altered patches, modification in vegetation structure and shifts in vegetation cover composition, as well as land surface processes (such as surface energy, water balance, carbon cycle). Traditional methods of recording fire burned areas and fire severity involve expensive and time -consuming field survey. The available remote sensing technologies may allow us to develop standardized burn-severity maps for evaluating fire effects and addressing post fire management activities. This paper is focused on preliminary results we obtained from ongoing research focused on the evaluation of spatial variability of fire effects on vegetation. For the purposes of this study satellite ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been used. Both single (post-fire) and multi-date (pre and post fire) ASTER images were processed for some test areas in Southern Italy. Spatial autocorrelation statistics, such as Morans I, Gearys C, and Getis-Ord Local Gi index (see Anselin 1995; Getis and Ord 1992), were used to measure and analyze the degree of dependency among spectral features of burned areas.

Performance evaluation of data fusion techniques for archaeological prospection based on satellite data

The application of Very High Resolution (VHR) satellite imagery to archaeological prospection can furnish useful information for the identification of archaeological features, related to ancient land use patterns, irrigation networks, paleo-hydrological systems, roads, walls and buildings. These archaeological features could be enhanced by using data fusion techniques which are able to merge the complementary characteristics of panchromatic and multispectral images. The quantitative evaluation of the quality of the fused images is one the most crucial aspects in the context of data fusion. This issue is particularly relevant in the case of the identification of archaeological features, because data fusion could enhance or lose the small spatial and spectral details which are generally linked with the presence of buried archaeological remains. This study is focused on the evaluation of data fusion algorithms applied to Quickbird images for the enhancement of archaeological features. Three different data fusion techniques, Gram-Schimdt, PCA, and wavelet, were applied to a study case located in the South of Italy. Focusing on the archaeological features, the evaluation process was performed by using two different protocols with and without a reference image. Results obtained from the two protocols showed that the best performance was obtained from the wavelet data fusion.

Satellite data and soil magnetic susceptibility measurements for heavy metals monitoring: findings from Agri Valley (Southern Italy)

Heavy metals pollution is a widespread problem in urbanized and industrial areas and there is a need of optimized and effective strategies for identifying and monitoring polluted areas. This study proposes an improved methodology based on Landsat satellite data and magnetic susceptibility measurements carried out in situ and in laboratory. Findings suggest that expeditious field surveys of soil magnetic susceptibility within stressed vegetated areas are a reliable indicator of soil contamination. Moreover, this procedure could provide a method for assessing heavy metals impacts and could be used to examine the effectiveness of emission control strategies.

Temporal Variation of Urban Pattern Morphology in Southern Italy Explored by Using Landsat Data

This paper deals with the spatial characterization of urban expansion by using spatial fractal analysis applied to multidate Multispectral Scanner (MSS) and Thematic Mapper (TM) satellite imagery. The investigation was focused on one small southern Italy town, for which the border was extracted from NASA Landsat images acquired in 1976 (MSS), in 1987 and 1998 (TM). The border was analysed using the box counting method, which is a well-know technique to estimate the spatial fractal dimension, that quantifies the shape irregularity of an object. The obtained results show that the fractal dimension of the border of the investigated town increases from 1976 to 1998, indicating a tendency toward a more irregular shape. This increase is also connected with the urban expansion and the population growth.