Groundwater recharge estimation in karst aquifers of southern Apennines (Italy) by integration of remotely sensed data

Karst aquifers, actual evapotranspiration, groundwater recharge, remote sensing data, southern Italy

Tecniche integrate di Remote Sensing e GIS a supporto della stima del potenziale fotovoltaico su tetti in aree urbane

Le ultime linee guida approvate dal Governo Italiano per sostenere finanziariamente il fotovoltaico (PV) e lo sviluppo della produzione di energia solare (Quarto e Quinto Conto Energia, Gennaio 2012 e succ.), al fine di evitare il consumo di suolo in aree agricole o naturali, comprendono indicazioni specifiche che rendono più vantaggiosa l’installazione d’impianti su tetti o superfici di copertura di edifici. In questo contesto diventa importante, per una adeguata attività di pianificazione e monitoraggio del PV, la mappatura estensiva delle superfici a disposizione, coincidenti con i tetti e l’accurata valutazione del loro potenziale fotovoltaico. Dal momento che tali coperture si trovano soprattutto nelle aree urbane o industriali, in cui fattori come eterogeneità tridimensionale, albedo, torbidità atmosferica ed ombreggiamenti reciproci influenzano in modo significativo l’irraggiamento solare locale, è necessario tenere conto adeguatamente di questi elementi mediante una mappatura GIS tridimensionale ed avanzati strumenti di modellazione, in modo da stimare efficacemente la radianza solare disponibile a livello dei tetti. La metodologia implementata, basata su tecniche di telerilevamento e GIS, ha permesso di valutare e mappare la radiazione solare globale su tutti i tetti presenti nel territorio del Comune di Avellino. Partendo da dati di tipo LIDAR, è stato ottenuto in primo luogo il DSM di tutta l’area di interesse (~ 42 Km2), quindi il modello tridimensionale di ogni edificio da cui sono stati derivati i parametri geometrici di tutte le coperture. Per tenere conto della trasparenza atmosferica e della percentuale di radiazione solare (diffusa/diretta) sulle superfici di interesse, sono stati utilizzati i dati e gli strumenti applicativi presenti sul sito web PVGIS, sviluppato dalla UE. L’elaborazione finale, basata sull’utilizzo di strumenti GIS anche di tipo open source, ha permesso di ottenere le mappe di radianza solare e di potenziale PV per tutti i tetti presenti nell’area di studio.The last guidelines approved by Italian government to financially support the solar Photovoltaic (PV) Energy production development (Fourth and Fifth feed-in-scheme, January 2012 and later), in order to avoid soil consumption in agricultural or naturals areas, include specific indications for more advantageously funding the installations exploiting roofs or covers surfaces. In this context it becomes important, for a suitable PV planning and monitoring, the extensive mapping of the available surfaces extent, usually corresponding to covers and properly assessing their quality in term of PV potential. Since the covers are mainly located in urban or industrial areas, whose 3D heterogeneity, albedo, atmospheric turbidity and casting shadows significantly influence the local solar irradiance, it is necessary to suitably account for these distributed factors by means of GIS mapping and advanced modeling tools in order to provide realistic estimates of solar available radiance at roofs level. The implemented methodology, based on remote sensing techniques, has allowed to estimate and map the global solar radiance over all the roofs within Avellino municipality. Starting from LIDAR data, DSM of the entire area of interest (~42 Km2) has been firstly obtained; then the 3D model of each building and related cover has been derived. To account for the atmospheric transparency and the related time-dependent diffuse/direct radiation percentage on the area, data and tools from EU PVGIS web application have been also used. The final processing to obtain the solar radiance maps has been carried out using specific software modules available within commercial and open-source GIS packages

Multispectral data by the new generation of high-resolution satellite sensors for mapping phytoplankton blooms in the Mar Piccolo of Taranto (Ionian Sea, southern Italy)

The HR (High-Resolution) EO (Earth Observation) satellite systems Landsat 8 OLI and Sentinel 2 were tested for mapping the frequent phytoplankton blooms and Chl a distributions in the sea basin of the Mar Piccolo of Taranto (Ionian Sea, southern Italy), using the sea truth calibration data acquired in 2013. The data were atmospherically corrected for accounting of the aerosol load on optically complexes waters (case II). Various blue-green and additional spectral indices ratios, were then satisfyingly tested for mapping the distribution of Chl a and differently sized phytoplankton populations through PLS (Partial Least Square regression) models, regressive statistical models and bio-optical algorithms. The PLS models demonstrated higher robustness for assessing the distribution of all the phytoplankton and Chl a except for those related to sub-surface micro-phytoplankton. The distributions obtained via a bio-optical approach (OC3 algorithm and full physically based inversion) showed a general agreement with the previous ones produced by statistical methods. The reflectance signals, captured by OLI and Sentinel 2 sensors in the visible and shorter wavelengths once atmospherically corrected, were found to be useful to map the coastal variability at detailed scale of Chl a and different phytoplankton populations, in the optically complexes waters of the Mar Piccolo

Satellite image mosaic of the Terra Nova Bay area (Victoria Land, Antarctica)

This work has been carried out as part of "Programma Nazionale di Ricerche in Antartide" and was supported financially be ENEA through a joint reasearch-program on Antarctic Earth Science with the University of Siena (Italy). The geopmorphological and glaciological research, of which this work forms a part, is coordinated by Prof. Giuseppe Grombelli

Testing Evapotranspiration Estimates Based on MODIS Satellite Data in the Assessment of the Groundwater Recharge of Karst Aquifers in Southern Italy

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 2000–2014 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 2000–2014, 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−1. In contrast, using the Turc, Coutagne, and Thornthwaite methods, it was estimated as being 494, 533, and 437 mm·year−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

Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data

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’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

Satellite Multi/Hyper Spectral HR Sensors for Mapping the <i>Posidonia oceanica</i> in South Mediterranean Islands

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

Assessing the Impact of Water Salinization Stress on Biomass Yield of Cardoon Bio-Energetic Crops through Remote Sensing Techniques

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

Mapping Spatial Patterns of Posidonia oceanica Meadows by Means of Daedalus ATM Airborne Sensor in the Coastal Area of Civitavecchia (Central Tyrrhenian Sea, Italy)

The spatial distribution of sea bed covers and seagrass in coastal waters is of key importance in monitoring and managing Mediterranean shallow water environments often subject to both increasing anthropogenic impacts and climate change effects. In this context we present a methodology for effective monitoring and mapping of Posidonia oceanica (PO) meadows in turbid waters using remote sensing techniques tested by means of LAI (Leaf Area Index) point sea truth measurements. Preliminary results using Daedalus airborne sensor are reported referring to the PO meadows at Civitavecchia site (central Tyrrhenian sea) where vessel traffic due to presence of important harbors and huge power plant represent strong impact factors. This coastal area, 100 km far from Rome (Central Italy), is characterized also by significant hydrodynamic variations and other anthropogenic factors that affect the health of seagrass meadows with frequent turbidity and suspended sediments in the water column. During 2011–2012 years point measurements of several parameters related to PO meadows phenology were acquired on various stations distributed along 20 km of coast between the Civitavecchia and S. Marinella sites. The Daedalus airborne sensor multispectral data were preprocessed with the support of satellite (MERIS) derived water quality parameters to obtain here improved thematic maps of the local PO distribution. Their thematic accuracy was then evaluated as agreement (R2) with the point sea truth measurements and regressive modeling using an on purpose developd method