Integrated Indicators for the Estimation of Vulnerability to Land Degradation

In this chapter we approach the assessment of the vulnerability to land degradation of a typical Mediterranean environment using a modified version of the ESA model. This approach combines analyses of the socio-economic component with analyses of the vegetation trends. According to the standard ESA strategy, different indicators representing the impact of agricultural and grazing activities are used. The main feature of these indicators is that they are census-based and consequently suitable only for the analysis at municipal scale. Therefore we have also elaborated a mechanization index (proxy for soil compaction induced by agricultural machineries) that uses land cover and morphological data [36], enabling high spatial resolution and faster rate of update. The indicators related to the anthropic impact are integrated into an overall Land Management Index (LMI) and in each area it is possible to enhance the main contributing factors to highlight the prevailing forces that drive human-induced degradation processes. In order to include vegetation in the vulnerability map we analyze satellite vegetation index NDVI (Normalized Difference Vegetation Index) which is recognized as ideal tool for monitoring long term trends of degradation phenomena and assessing different values of severity of the concerned processes [37,38]. The final result of our analyses is an integrated vulnerability map of the investigated region, accounting for management and vegetation factors, which allows us to identify priority sites where restoration/rehabilitation interventions are urgent. The adopted procedure can be easily applied to geographic contexts characterized by high complexity in terms of land cover type and economic vocation (intensive agriculture, grazing, industrial activities) thus enabling an early detection of the areas most vulnerable to land degradation

Investigating climate variability and long-term vegetation activity across heterogeneous Basilicata agroecosystems

The Basilicata region summarizes many basic features of the biogeographic complexity characterizing Mediterranean countries. The intricate geomorphology and the long history of human management generated the current landscapes, which include both high-value ecosystems and areas prone to desertification. Preserving goods and services provided by such composite land cover mosaics poses many problems due to the interference/overlap of diverse natural and anthropic factors which make the correct selection of relevant parameters and the interpretation of observational data rather difficult. Here, we study interconnections between local climate and vegetation activity by correlating parameters characterizing the interannual statistics of the NDVI (Normalized Difference Vegetation Index), derived from satellite data, with a recently devised multivariate statistical index of meteoclimatic variability. We used a 15-year sequence of remote images concerning a set of plots located around meteorological ground stations of the central-eastern part of the region to pick up spatial structures in the vegetation–climate relationships. Our analyses were able to correlate spatial heterogeneity to variations in water exchanges between vegetation and atmosphere. This study represents a first step to improve the description of relevant processes to protect natural habitats and quality agriculture, therefore combating land degradation and climate change detrimental effects

A preliminary studyof the site-dependence of the multifractalfeatures of geoelectric measurements

Multifractal analysis was performed to characterize the fluctuations in dynamics of the hourly time variability of self-potential signals measured from January 2001 to September 2002 by three stations installed in the Basilicata region (Southern Italy). Two stations (Giuliano and Tito) are located in a seismic area, and one (Laterza) in an aseismic area. Multifractal formalism leads to the identification of a set of parameters derived from the shape of the multifractal spectrum (the maximum a0, the asymmetry B and the width W) and measuring the «complexity» of the signals. Furthermore, the multifractal parameters seem to discriminate self-potential signals measured in seismic areas from those recorded in aseismic areas

Wavelet analysis as a tool to characteriseand remove environmental noisefrom self-potential time series

Multiresolution wavelet analysis of self-potential signals and rainfall levels is performed for extracting fluctuations in electrical signals, which might be addressed to meteorological variability. In the time-scale domain of the wavelet transform, rain data are used as markers to single out those wavelet coefficients of the electric signal which can be considered relevant to the environmental disturbance. Then these coefficients are filtered out and the signal is recovered by anti-transforming the retained coefficients. Such methodological approach might be applied to characterise unwanted environmental noise. It also can be considered as a practical technique to remove noise that can hamper the correct assessment and use of electrical techniques for the monitoring of geophysical phenomena

Multifractal Fluctuations in Seismic Interspike Series

Multifractal fluctuations in the time dynamics of seismicity data have been analyzed. We investigated the interspike intervals (times between successive earthquakes) of one of the most seismically active areas of central Italy by using the Multifractal Detrended Fluctuation Analysis (MF-DFA). Analyzing the time evolution of the multifractality degree of the series, a loss of multifractality during the aftershocks is revealed. This study aims to suggest another approach to investigate the complex dynamics of earthquakes

Heavy Metal Concentrations in Dairy Products from Sheep Milk Collected in Two Regions of Southern Italy

The aim of this work was to detect the concentrations of some heavy metals in milk collected from ewes in 8 farms located in Calabria and Campania and to evaluate to what extent these metals may be present in dairy products for human consumption. The analysis of chromium, cadmium, lead and mercury was performed in a atomic absorption spectrophotometer equipped with a graphite furnace. The determination of Hg content in dry samples was carried out by means of an automatic Mercury analyser. Chromium was the metal detected at highest levels in milk and lead was highest in fresh, mature cheese and in ricotta. In Italy, human consumption of sheep milk is very limited and addressed to milk products. In our study the levels of some metals were higher than those reported in literature. However, the results indicate that sheep milk and milk products from the two regions of Italy investigated in this study are safe for consumers