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Mar Piccolo is a semi-enclosed sea of about 21 Km2, consisting of two basins to the north of the city of Taranto. It is subjected to multiple anthropogenic pressures, such as industrial and waste pollution, harbour activities, aquaculture and commercial fishing that affect the environmental quality and can favour the arrival and establishment of alien species. The two basins are very different in terms of both abiotic and biotic features. The western basin is a proper marine coastal area, while the eastern one is more similar to a lagoon with very reduced water circulation and low ecological diversity. Throughout the years, the anthropogenic activities increased their impactsonthisecosystemleadingtothespreadofvariousmacro-algalspecies(e.g. Hypnea cornuta and Caulerpa prolifera) and recurrent noxious episodes of phytoplankton blooms. During several sea truth campaigns carried out in 2013 and 2014, many data concerning distribution, density, biomass and chlorophyll a and b, related to the populations of benthic macroalgae and phytoplankton, were acquired at different sampling stations located in the two basins of the Mar Piccolo. The sea truth data collected during these campaigns were integrated with those provided by the new family of multispectral HR satellite sensors, namely Landsat 8 OLI and Sentinel 2 MSI, to preliminarily test their improved capability for seaweed and phytoplankton detailed mapping. Different image based approaches were applied for the essential atmospheric preprocessing focusing on the AOD (Aerosol Optical Depth) and adjacency effects noise contributions removal, taking into account the optical complexity of these shallow waters (case II water). The spectral responses detected by OLI sensor in the form of various blue-green and additional ratios, once atmospherically corrected, were satisfyingly tested for mapping the distribution of phytoplankton communities, through regressive statistical and bio-optical models. The most reliable multivariate models were those obtained for surface and sub-surface distributions of nano-phytoplankton and pico-phytoplankton, respectively. The PLS (Partial Least Square regression) models demonstrated higher robustness for assessing the distribution of all the phytoplankton and Chl a distributions, except for those related to sub-surface micro-phytoplankton, as did the regressive ones. The preliminary distributions obtained via a bio-optical approach, including the OC3 algorithm and full inversion with default settings, showed a general agreement with the previous ones produced by statistic methods. The OLI corrected data were then exploited for assessing the distribution of Caulerpa and Hypnea populations, using a ML (Maximum Likelihood) algorithm through standard supervised classification schema and related station point spectral signatures. Overall, the implemented methodology, based on the HR satellite sensors, allowed us to suitably map the variability at detailed scale of both submerged vegetation and water column concentration of chlorophyll and different phytoplankton populations, in the Mar Piccolo of Taranto. Thus, innovative monitoring methods, integrated by the most recent RS techniques, are needed for the sustainable management of this environment, to prevent, control and mitigate the impact of anthropogenic pressures on the environmental quality, human health and activities such as tourism and aquaculture, economically relevant in this context.

The multispectral data provided by the older TM5 and ETM Landsat polar sensors, despite their suitable ground resolution weren't widely applied for mapping the submerged vegetation and phytoplankton in the coastal and internal optically complexes shallow waters (typically case II waters), due mainly to their low SNR (Signal to noise Ratio) and consequent poor radiometric resolution (8 bit/pixel) which often didn't allow to obtain the required sensitivity. In the summer 2013 the new Landsat 8 NASA polar satellite carrying on board the OLI (Operational Land Imager) sensor was launched. This sensor respect to the previous ones of the Landsat family has improved acquisition bands in the visible, NIR, SWIR, TIR ranges and radiometry that offer a more reliable opportunity for operative monitoring of coastal shallow waters and ecosystems previously unavailable. In addition to the optical (RGB, NIR, SWIR, TIR) OLI spectral bands repositioning to improve their SNR, the new "coastal" deep blue channel was introduced to provide a more effective and systematic ocean color retrieving capability at higher spatial resolution in coastal and inland waters, typically recognized as case II waters. Therefore in order to preliminarily test the capabilities of the new OLI sensor for coastal monitoring, some multispectral images recorded in 2013 and 2014 on the lagoon of the Mar Piccolo of Taranto (Southern Italy) were exploited.

Hypnea cornuta distribution was assessed by the high resolution multispectral satellite remote sensing technique in the Mar Piccolo of Taranto, on the basis of multi-temporal in situ data acquired during the entire growth season. Different in situ biomass values were recorded between two stations out of the four sampled in the same period. These differences allowed us to properly calibrate the remotely sensed data

The Mediterranean basin is a hot spot of climate change where the Posidonia oceanica (L.) Delile (PO) and other seagrasses are under stress due to its effect on marine coastal habitats and the rising influence of anthropogenic activities (i.e., tourism, fishery). The PO and seabed ecosystems, in the coastal environments of Pantelleria and Lampedusa, suffer additional growing impacts from tourism in synergy with specific stress factors due to increasing vessel traffic for supplying potable water and fossil fuels for electrical power generation. Earth Observation (EO) data, provided by high resolution (HR) multi/hyperspectral operative satellite sensors of the last generation (i.e., Sentinel 2 MSI and PRISMA) have been successfully tested, using innovative calibration and sea truth collecting methods, for monitoring and mapping of PO meadows under stress, in the coastal waters of these islands, located in the Sicily Channel, to better support the sustainable management of these vulnerable ecosystems. The area of interest in Pantelleria was where the first prototype of the Italian Inertial Sea Wave Energy Converter (ISWEC) for renewable energy production was installed in 2015, and sea truth campaigns on the PO meadows were conducted. The PO of Lampedusa coastal areas, impacted by ship traffic linked to the previous factors and tropicalization effects of Italy’s southernmost climate change transitional zone, was mapped through a multi/hyper spectral EO-based approach, using training/testing data provided by side scan sonar data, previously acquired. Some advanced machine learning algorithms (MLA) were successfully evaluated with different supervised regression/classification models to map seabed and PO meadow classes and related Leaf Area Index (LAI) distributions in the areas of interest, using multi/hyperspectral data atmospherically corrected via different advanced approaches.

In many Italian regions, and particularly in southern Italy, karst aquifers are the main sources of drinking water and play a crucial role in the socio-economic development of the territory. Hence, estimating the groundwater recharge of these aquifers is a fundamental task for the proper management of water resources, while also considering the impacts of climate changes. In the southern Apennines, the assessment of hydrological parameters that is needed for the estimation of groundwater recharge is a challenging issue, especially for the spatial and temporal inhomogeneity of networks of rain and air temperature stations, as well as the variable geomorphological features and land use across mountainous karst areas. In such a framework, the integration of terrestrial and remotely sensed data is a promising approach to limit these uncertainties. In this research, estimations of actual evapotranspiration and groundwater recharge using remotely sensed data gathered by the Moderate Resolution Imaging Spectrometer (MODIS) satellite in the period 2000&ndash, 2014 are shown for karst aquifers of the southern Apennines. To assess the uncertainties affecting conventional methods based on empirical formulas, the values estimated by the MODIS dataset were compared with those calculated by Coutagne, Turc, and Thornthwaite classical empirical formulas, which were based on the recordings of meteorological stations. The annual rainfall time series of 266 rain gauges and 150 air temperature stations, recorded using meteorological networks managed by public agencies in the period 2000&ndash, 2014, were considered for reconstructing the regional distributed models of actual evapotranspiration (AET) and groundwater recharge. Considering the MODIS AET, the mean annual groundwater recharge for karst aquifers was estimated to be about 448 mm∙year&minus, 1. In contrast, using the Turc, Coutagne, and Thornthwaite methods, it was estimated as being 494, 533, and 437 mm∙year&minus, 1, respectively. The obtained results open a new methodological perspective for the assessment of the groundwater recharge of karst aquifers at the regional and mean annual scales, allowing for limiting uncertainties and taking into account a spatial resolution greater than that of the existing meteorological networks. Among the most relevant results obtained via the comparison of classical approaches used for estimating evapotranspiration is the good matching of the actual evapotranspiration estimated using MODIS data with the potential evapotranspiration estimated using the Thornthwaite formula. This result was considered linked to the availability of soil moisture for the evapotranspiration demand due to the relevant precipitation in the area, the general occurrence of soils covering karst aquifers, and the dense vegetation.

The accelerating rate of the introduction of non-indigenous species (NIS) and the magnitude of shipping traffic make the Mediterranean Sea a hotspot of biological invasions. For the effective management of NIS, early detection and intensive monitoring over time and space are essential. Here, we present an overview of possible applications of citizen science and remote sensing in monitoring alien seaweeds in the Mediterranean Sea. Citizen science activities, involving the public (e.g., tourists, fishermen, divers) in the collection of data, have great potential for monitoring NIS. The innovative methodologies, based on remote sensing techniques coupled with in situ/laboratory advanced sampling/analysis methods for tracking such species, may be useful and effective tools for easily assessing NIS distribution patterns and monitoring the space/time changes in habitats in order to support the sustainable management of the ecosystems. The reported case studies highlight how these cost-effective systems can be useful complementary tools for monitoring NIS, especially in marine protected areas, which, despite their fundamental role in the conservation of marine biodiversity, are not immune to the introduction of NIS. To ensure effective and long-lasting management strategies, collaborations between researchers, policy makers and citizens are essential.

Land transformation is one of the phenomena that have largely interested several areas of the World and in particular the African continent. Furthermore, during last years, due to several causes, in some of the poorest countries, like Mozambique, a very great extent of land has been acquired by private companies or by a foreign country, generally for extensive agricultural purposes. In the majority of cases, the transition from a multivariate subsistence farming and slight pastoralism (made of small land parcels, exploited for cropping to satisfy local needs) to extremely large monoculture systems causes a heavy land use transformation. Moreover, the climate change effects, which have inexorably affected the African continent since various decades, constitute a further cause in vegetation changing. All these conditions make the Mozambican territory one of the most potentially subjected to the land transformation. Using GIS methodologies and Remote Sensing (RS) data, territorial dynamics were identified, in order to investigate possible situations of land cover changes. This paper describes the approach, based on Geomatics (GIS and RS), for monitoring the above-mentioned phenomena at suitable spatial and temporal scales and for identifying areas with potential vulnerabilities.

Earthquake-induced rubble in urbanized areas must be mapped and characterized. Location, volume, weight and constituents are key information in order to support emergency activities and optimize rubble management. A procedure to work out the geometric characteristics of the rubble heaps has already been reported in a previous work, whereas here an original methodology for retrieving the rubble&rsquo, s constituents by means of active and passive remote sensing techniques, based on airborne (LiDAR and RGB aero-photogrammetric) and satellite (WorldView-3) Very High Resolution (VHR) sensors, is presented. Due to the high spectral heterogeneity of seismic rubble, Spectral Mixture Analysis, through the Sequential Maximum Angle Convex Cone algorithm, was adopted to derive the linear mixed model distribution of remotely sensed spectral responses of pure materials (endmembers). These endmembers were then mapped on the hyperspectral signatures of various materials acquired on site, testing different machine learning classifiers in order to assess their relative abundances. The best results were provided by the C-Support Vector Machine, which allowed us to work out the characterization of the main rubble constituents with an accuracy up to 88.8% for less mixed pixels and the Random Forest, which was the only one able to detect the likely presence of asbestos.

The benthic seabeds and seagrass ecosystems, in particular the vulnerable Posidonia oceanica (PO), are increasingly threatened by climate change and other anthropogenic pressures. Along the 8000km coastline of Italy, they are often poorly mapped and monitored to properly evaluate their health status. Thus to support these monitoring needs, the improved capabilities of the Landsat 8 Operational Land Imager (OLI) Earth Observation (EO) satellite system were tested for PO mapping by coupling its atmospherically corrected multispectral data with near-synchronous sea truth information. Two different approaches for the necessary atmospheric correction were exploited focusing on the Aerosol Optical Depth (AOD) and adjacency noise effects, which typically occur at land–sea interfaces. The general achievements demonstrated the effectiveness of High Resolution (HR) spectral responses captured by OLI sensor, for monitoring seagrass and sea beds in the optically complex Tyrrhenian shallow waters, with performance level dependent on the type of applied atmospheric pre-processing. The distribution of the PO leaf area index (LAI) on different substrates has been most effectively modelled using on purpose developed spectral indices. They were based on the coastal and blue-green OLI bands, atmospherically corrected using a recently introduced method for AOD retrieval, based on the Short Wave Infrared (SWIR) reflectance. The alternative correction method including a less effective AOD assessment but the removal of adjacency effects has proven its efficacy for improving the thematic discriminability of the seabed types characterized by different PO cover–substrate combinations.

Various species of cultivated thistle, such as Cynara cardunculus L. (cardoon), exhibit interesting features for industrial biomass production as bioenergy crops, given also their advantageous adaptation capacities to typical Mediterranean climate trends, with noticeable resilience to drought and salinization stresses. The in situ hyperspectral reflectance responses of three genotypes of cardoon plants, irrigated with water at different salinity levels, have been tested for assessing the effects on their biophysical parameters, aiming at improving the biomass yield for bioenergy production, minimizing at same time the environmental impacts and the exploitation of soils and waters resources. The leaf and canopy reflectance hyperspectral signatures, acquired at three different growth stages with biometric measurements, were statistically analyzed (ANOVA, Tukey&rsquo, s test, graphs), as noise-resilient spectral indices, sensible to different plant features of interest. Their broadband versions, based on the Landsat 8 OLI and Sentinel 2 MSI satellite sensors, were also evaluated in perspective of operative and extensive remote crop monitoring from space. The results highlighted significant differences in some spectral index responses, related to different cardoon genotypes and water salt concentration. The biometric data supported by red-edge indices modelling evidenced the impact of the highest salt water concentration (200 mM/L) on the plant growth and yield.

The meadow froghopper, Philaenus spumarius L., is endemic in Italy and was not considered a harmful species until 2014, when the olive quick decline syndrome (OQDS) showed up in Apulia (southern Italy). It was immediately suspected and then verified as the main vector of Xylella fastidiosa, the bacterium responsible for the disease. Currently, EU Directives consider the fight against P. spumarius compulsory in member states and recommend Integrated Pest Management (IPM), both in uncultivated and cultivated infested areas, to minimise the environmental impact of chemical pesticides. This should be based on an improved knowledge of the vector with its seasonal trends and feeding habits linked to specific herbaceous species. In this context, our field study was aimed to improve the understanding of the vector nutritional behaviour, especially at its critical nymph stage, by monitoring its presence on different herbaceous target species, using its typical feeding foams as key indicator. The study area was in Lazio region (central Italy), dedicated to olive growing and still unaffected by the X. fastidiosa plague. Over twoyears, during the nymph development period, field data have been acquired over the test area and then analysed by coupling statistical (ANOVA), geographical information system (GIS) and geo-referenced field sampling approaches. Results highlighted that P. spumarius exhibits significant preferences for specific herbaceous plants, especially at its early development stages, detectable by tenuous spittle. This indicates female oviposition activity, which seems also not influenced by olive tree proximity. Furthermore, the non-host plant species identified here could be suitable for creating green barriers for limiting the vector diffusion to contiguous areas where sensible plantations are growing. In the final section, applied implications arising from the present findings for P. spumarius population management are discussed.

Knowledge on ecosystem functioning can largely contribute to promote ecosystem-based management and its application. The Mar Piccolo of Taranto is a densely populated area at a high risk of environmental crisis. Here, planktonic primary production (PP) and heterotrophic prokaryotic production (HPP) were measured as proxies of functioning in three sampling sites located in two inlets at different levels of industrial contamination, during three sampling surveys (June 2013, February and April 2014). To have a better overall view and provide some insights into the benthic-pelagic coupling, we integrated PP and HPP in the water column with those in the sediments and then discussed this with the origin of the organic matter pools based on analysis of stable isotopes. Heavy metals and polychlorobiphenyls (PCBs) were also analysed in the surface (1 cm) sediment layer and related to the overall ecosystem functioning. Multidimensional scaling (MDS) analysis, based on the main data, clearly separated the second inlet from the first one, more severely impacted by anthropogenic activities. The stable isotope mixing model suggested the prevalent terrestrial/riverine origin of the particulate organic matter pools (mean 45.5 %) in all sampling periods, whereas phytoplankton contributed up to 29 % in February. Planktonic PP and HPP rates followed the same pattern over the entire study period and seemed to respond to phytoplankton dynamics confirming this community as the main driver for the C cycling in the water column. On the contrary, benthic PP rates were almost negligible while HPP rates were lower or comparable to those in the water column indicating that although the Mar Piccolo is very shallow, the water column is much more productive than the surface sediments. The sediment resuspension is likely responsible for a pulsed input of contaminants into the water column. However, their interference with the proper functioning of the pelagic ecosystem seems to be limited to the bottom layers. © 2015, Springer-Verlag Berlin Heidelberg.

Karst aquifers, actual evapotranspiration, groundwater recharge, remote sensing data, southern Italy., Flowpath 2017, National meeting on hydrogeology 2017

This study presents a first-level spatial assessment of the susceptibility to earthquake-induced landslides in the seismic area of the Agri Valley (Basilicata Region, southern Italy), which hosts the largest onshore oilfield and oil/gas extraction and pre-treatment plant in Europe and is the starting point of the 136-km-long pipeline that transports the plant’s products to the refinery located in Taranto, on the Ionian seacoast. Two methodologies derived from the ones proposed by Newmark (Geotechnique 15(2):139–159, 1965) and Rapolla et al. (Eng Geol 114:10–25, 2010, Nat Hazards 61:115–126, 2012. doi: 10.1007/s11069-011-9790-z ), based on different modelling approaches, were implemented using the available geographic information system tools, which allowed a very effective exploitation of the two models capability for regional zoning of the earthquake-induced landslide hazard. Subsequently, the results obtained from the two models were compared by both visual evaluation of thematic products and statistical correlation analysis of quantitative indices, such as the Safety Index based on the Newmark’s approach and the Susceptibility Index from Rapolla’s model. The comparison showed a general agreement in highlighting the most critical areas. However, some slight differences between the two models’ results were observed, especially where rock materials and steep slopes are prevailing.