Tiziana Simoniello

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.

Watershed influence on fluvial ecosystems: an integrated methodology for river water quality management

The EU Water Framework Directive 2000/60 (Integrated River Basin Management for Europe) establishes the importance of preserving water quality through policies applied at watershed level given the strong links existing among ecological, hydrological, and hydrogeological systems. Therefore, monitoring campaigns of river water quality should be planned with multidisciplinary approaches starting from a landscape perspective. In this paper, the effects of the basin hydrology on the river water quality and, in particular, the impacts caused by the runoff production coming from agricultural areas are investigated. The fluvial segments receiving consistent amount of pollutant loads (due to the runoff routing over agricultural areas) are assumed more critical in terms of water quality and thus, they require more accurate controls. Starting from this perspective, to evaluate the runoff productions coming from agricultural areas, we applied a semi-distributed hydrological model that adopts satellite data, pedological and morphological information for the watershed description. Then, the river segments receiving critical amount of runoff loads from the surrounding cultivated areas were identified. Finally, in order to validate the approach, water quality for critical and non critical segment was investigated seasonally, by using river macroinvertebrates as indicators of water quality because of their effectiveness in preserving in time a memory of pollution events. Biomonitoring data showed that river water quality strongly decreases in correspondence of fluvial segments receiving critical amount of runoff coming from agricultural areas. The results highlight the usefulness of such a methodology to plan monitoring campaigns specifically devoted to non-point pollution sources and suggest the possibility to use this approach for water quality management and for planning river restoration policies.

A method for the integration of satellite vegetation activities observations and magnetic susceptibility measurements for monitoring heavy metals in soil.

We present a procedure for monitoring heavy metals in soil based on the integration of satellite and ground-based techniques, tested in an area affected by high anthropogenic pressure. High resolution multispectral satellite data were elaborated to obtain information on vegetation status. Magnetic susceptibility measurements of soils were collected as proxy variable for monitoring heavy metal presence. Chemical analyses of heavy metals were used for supporting and validating the integrated monitoring procedure. Magnetic and chemical measurements were organized in a GIS environment to be overlapped to satellite-based elaborations and to analyze the pattern distribution. Results show the presence of correlation between anomalies in vegetation activity and soil characteristics. The relationship between the distribution of normalized difference vegetation index anomalies and magnetic susceptibility values provides hints for adopting the integrated procedure as preliminary screening to minimize monitoring efforts and costs by supporting the planning activities of field campaigns.

Landscape-scale characterization of vegetation phenology using AVHRR-NDVI and Landsat-TM data

The strict link between intra-annual vegetation dynamics (phenology) and Earths climate makes phenological information fundamental to improve understanding and models of inter-annual variability in terrestrial carbon exchange and climate-biosphere interactions. In order to monitor phenology in a landscape characterized by heterogeneous features rapidly changing over the territory, we performed multitemporal classifications of NDVI-AVHRR data and interfaced them with Landsat-TM data and orography. The sample area is the Vulture basin (Southern Italy), where cultivated and densely vegetated areas coexist with urban and recently built industrial areas. These land cover patterns rapidly change over the territory at very small spatial scales; it is a complex zone very interesting for studying the use of remote sensing techniques in the integrated monitoring context. Clusters having homogeneous NDVI time behaviors were identified. In spite of its spatial resolution, AVHRR NDVI effectively picks up the characteristic phenology for different covers and altitudes. Moreover, some pixels having particular microclimate were clustered and their characterization was only possible by using orography and TM classification information. The comparison of two intra-annual classifications (1996 and 1998) showed that the proposed approach can be very useful for studying change in pattern of vegetation dynamics.

Pollino Project Action D: a multiscale approach in the space-time domain to environmental risk monitoring

Physical parameters related to Earth surface and atmosphere show different behaviors when observed at different space-time scales by using both remote sensing or traditional ground based techniques. The main aim of this project was to investigate the information content degradation which results moving from the use of observations obtained by direct-punctual (ground-based), higher spectral/spatial resolution (airborne sensors), higher time-resolution, low cost and low spatial resolution (satellites), in the context of the activities related to natural and environmental risks monitoring in protected natural areas. Several observational techniques have been contemporary used during two fields campaigns in the Pollino National Park (Southern Italy): a) from ground by direct measurements of near surface parameters (from - 70cm of depth up to 200cm of height) as well as by radiosonde and radiometric measurements of surface and atmospheric parameters; b) using hyperspectral (MIVIS) and photographic aerial observations; c) from LANDSAT-TM, NOAAA/AVHRR and ADEOS/AVIRIS satellite sounders. Campaign data have been integrated on a GIS (including high resolution cartographic layers) and long term evolutionary trends (up to 20 years) also considered after the analysis of available historical, LANDSAT and NOAA, satellite records. This paper will present the main achievements of the project with special emphasis on the trade-off between expected performances and economical sustainability of different environmental monitoring strategies in an operational context.

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.

Combining Habitat Suitability Models and Fluvial Functionality Data for a Multilayer Assessment of Riverine Vulnerability

Abstract For studying river basins criticalities, it is mandatory to take into account the multilayered structure of the river/landscape system. We propose an integrated analysis that combines habitat suitability (HS) maps for a riverine key species (the endangered Eurasian otter) with data on fluvial functionality (FF) as an instrument to reveal more vulnerable fluvial portions in need of restoration. The approach was tested on two river catchments with different anthropogenic pressures, both falling within the otter core area in Italy. Results showed that the integration allows for a satisfactory prioritization of river criticalities, suggesting the method as an useful tool for river environmental management.

Time Correlation Laws Inferred from Climatic Records: Long-Range Persistence and Alternative Paradigms

Observational time series of climatic variables exhibit substantial changeability on spatial and temporal scales over many orders of magnitude. In statistical terms, this implies a continuous variance distribution involving all resolvable time scales (frequencies), starting from those comparable with the age of the Earth. A correct causal interpretation of such a variability is very difficult even in the context of a cognitive approach (e.g., von Storch, 2001) to the problem. Cognitive models are minimum complexity models aiming at the scientific understanding of the most relevant processes occurring at any given temporal and spatial scale. Although generally they cannot be useful for management decisions straightforwardly, their role is fundamental especially for understanding the internal climatic variability that cannot be passively related to external forcing factors. The concept of stochastic process is essential in this framework, since it synthesizes collective behaviours which contribute as a whole to the overall dynamics. As stochastic processes are the macroscopic result of many degrees of freedom, the characterization of their correlation properties across different scales through the analysis of observational data is a problem of statistical inference and their modelling is usually a mechanical-statistical problem. Maybe, the most famous early effort aiming to summarize the climate variance distribution among different frequencies, which is commonly referred as climate spectrum, is the ideal sketch proposed by Mitchell (1976) (see Fig. 1). All the features of this spectrum that deviate from the flat behaviour typical of white noise (pure random process) deserve dynamical interpretation in order to understand climate. Within the traditional picture of the climate dynamics, the variance distribution among different temporal scales is seen as the superposition of oscillations generated by astronomical cycles (spectral spikes), quasi-periodic or aperiodic fluctuations with a preferred scale (broad spectral peaks), and internal stochastic processes whose temporal correlation decays according to characteristic time scales. These last are responsible for all the continuous broad-band deviations of the spectrum from flatness. Within this picture, the variance accumulations that do not appear in the form of peaks and spikes, such as that we

Multi-Technique Application for Waste Material Detection and Soil Remediation Strategies: The Red Mud Dust and Fly Ash Case Studies

Increasing amounts of residues and waste materials coming from different industrial activities have become a serious problem for the future. However, over the last few years there has been a growing emphasis on the utilization of these materials in several remediation technologies in order to clean up contaminated soil. Among them, two examples of industrial residues are fly ash and red mud. Fly ash is a by-product of thermal power plants partly used in concrete and cement manufacturing. More than half of it is disposed of in landfills because it finds no other application. It is composed of minerals such as quartz, mullite, subordinately hematite and magnetite, carbon, and a prevalent phase of amorphous aluminosilicate. Red mud is a waste material formed during the production of alumina when the bauxite ore is subject to caustic leaching. It is mainly characterized by the presence of hematite, goethite, gibbsite, rutile and sodium as sodium aluminum silicates or hydro-silicates. A wide variety of organic compounds could also be found (e.g. polybasic and polyhydroxy acids, humic and fulvic acids, carbohydrates, acetic and oxalic acids, furans). The mineralogical and chemical characterization of these two waste materials is generally carried out by X-ray powder diffraction, thermal analysis, infrared spectroscopy, scanning electron microscopy and chemical methods. Imaging spectroscopy under controlled conditions in laboratory is also applied. Many research activities on the neutralization of fly ash and red mud materials as well as to solve the problems connected to their disposal are developed in the last few years. Some of these focus on their utilization in different remediation technologies to immobilize toxic elements. They are in fact used in solidification/stabilization technologies for soil remediation treatment and some studies are based on the immobilization of toxic elements in synthetic zeolites crystallized by treated fly ash. The chapter investigates these two industrial residues focusing both on their chemicalmineralogical properties and their characterization as toxic materials. Studies of remediation methods to reduce the environmental risks due to polluting metals by using red mud and fly ash are presented as well as examples of landfill monitoring and airborne hyperspectral