Vito Imbrenda

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.

A geostatistics-assisted approach to the deterministic approximation of climate data

We propose a nonconventional application of variogram analysis to support climate data modelling with analytical functions. This geostatistical technique is applied in the theoretical domain defined by each model variable to detect the systematic behaviours buried in the fluctuations determined by other driving factors and to verify the ability of candidate fits to remove correlations from the data. The climatic average of the atmospheric temperature measured at 387 European meteorological stations has been analysed as a function of geographical parameters by a step-wise procedure. Our final model accounts for non-linearity in latitude with a local-scale residual correlation that decays in approximately ten kilometres. The variance of the residuals from the fitted model (approximately 3% of the total) is mostly determined by local heterogeneity in transitional climates and by urban islands. Our approach is user-friendly, and the support of statistical inference makes the modelling self-consistent. Variogram analysis is adapted to optimize deterministic modelling of climate data.The explanatory variables define the domain where our analysis is performed.Variograms support model identification and diagnostic checking.The analysis identifies scales where simple functions approximate complex patterns.The approach is suited when local and global scales are separable.

Integrated Indicators for the Estimation of Vulnerability to Land Degradation

The setting up of sustainable development strategies, able to balance the opposite demands of economic growth and environmental protection, is one of the fundamental challenges for the international community. Our developing world is experiencing growing pressures on its land, water, and food production systems and the role of the human society in determin‐ ing change within the Earth environment is becoming ever more central [1]. In this context, preserving the land productivity is a prior goal, especially in those areas, such as drylands, which are particularly fragile from an ecological point of view.

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.