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• A multimodal computational approach was used for the 3D survey of a panel painting. • Data from close-range photogrammetry and laser microprofilometry were fused. • The warping arrow before and after restoration was quantified on the 3D point cloud. • Multi-temporal monitoring of the painting is computed via Cloud-to-Cloud distance. • Micrometric roughness of paint layers is measured by laser microprofilometry. This paper reports the results obtained from the workflow designed for the 3D survey of a Renaissance panel painting using close-range photogrammetry and conoscopic laser microprofilometry, before and after its restoration. The artwork under examination is the central panel of a Renaissance polyptych realized by a late Gothic painter active in central Italy in the late 15th century. This painting has had a rather troubled conservation history following numerous adverse events that have severely damaged it structurally. In preparation for restoration work, the fusion of point cloud data derived from active and passive sensors served to create a single multi-resolution model on which to conduct advanced metrological analyses for multi-temporal monitoring of the changes undergone by the panel painting during and after structural restoration. [ABSTRACT FROM AUTHOR]

• There are few examples of field survey data being utilized to test the effectiveness of UN SDG indicator 15.3.1. • The annual mean of NDVI shows some weakness in detecting land productivity dynamics. • Remote sensing coupled with a cloud-based computing platform may improve knowledge of the land productivity index. • The trend of maximum NDVI values appears more suitable for the assessing of land degradation processes. • Analysis of finer resolution satellite datasets is the best solution for regional/local contexts. Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context. [ABSTRACT FROM AUTHOR]

• Novel prognostic method for bridge SHM based on InSAR analysis. • Applied Element Method to simulate the behaviour of bridges up to total collapse. • Combination of InSAR monitoring and numerical modelling. • Estimation of residual service life of bridges affected by slow deformation phenomena. • Application to the Albiano-Magra Bridge case study collapsed in 2020. This paper proposes an innovative approach for structural prognosis of bridges, that combines displacement information obtained by Synthetic Aperture Radar Interferometry (InSAR), applied to satellite data, with structural and collapse analysis performed through numerical modelling. The purpose of this multidisciplinary approach is to recognize potentially critical conditions and predict the time to failure of bridges affected by slow deformation phenomena, such as slow landslide-induced movements. The methodology involves advanced pre- and post-processing techniques for satellite SAR acquisitions, which allow tracking two-dimensional displacement evolution of multi-span bridges with properly defined error bounds, and uses numerical modelling to simulate damage propagation and structural behaviour up to total collapse. The effectiveness of the proposed synergy between remote displacement measurements and numerical collapse analysis is exemplified through the application to the Albiano-Magra Bridge in Italy, which collapsed on April 8th, 2020. The results have allowed to identify the triggering cause of the collapse and have provided an estimate of the residual service life of the bridge, with increasing reliability as the available satellite monitoring period gets longer. The work demonstrates that a combined analysis considering both satellite measurements and numerical modelling can improve the comprehension of the phenomena concerning the structural collapse due to slow deformations and its prevention. [ABSTRACT FROM AUTHOR]

The current paper presents the development and testing of a multi-step methodology which integrates remotely sensed and ancillary data to estimate olive (Olea europaea L.) fruit yield in Tuscany (Central Italy). The processing of very high resolution (Ikonos) and high resolution (Landsat ETM+) images provides a map of olive tree canopy cover fraction for all Tuscany olive yards, which is used to extract olive tree NDVI values from MODIS imagery. The combination of these values with standard meteorological data within a modified parametric model (C-Fix) enables the prediction of daily olive tree gross primary production (GPP) for ten years (2000–2009). These GPP estimates are then joint to the respiration estimates of a bio-geochemical model (BIOME-BGC) to simulate olive tree net primary production (NPP). The NPP accumulated over proper periods of the ten growing seasons is finally converted into olive fruit yield, whose accuracy is assessed through comparison with provincial statistics. The methodology is only partly capable of capturing spatial and temporal olive fruit yield variability at province level, but can accurately reproduce inter-year yield variation over the entire region. The paper concludes with a discussion of the results achieved and with considerations on the research prospects. [Copyright &y& Elsevier]

Abstract: The problem of severe thunderstorm risk in the Lombardy region (Italy) is serious. In fact during the warm season many thunderstorms (TS) occur in high density populated area located between the river Po and the Alps. In the year 2003, about 90 TS caused damage to people, houses, cars, agriculture and electrical lines. About 30 municipalities undergo damage by tornadoes. The 2003 summer was not particularly anomalous with respect to others for TS activity. In this region storms are well detected by some C-band radars and the Meteosat satellites, but the study of the correlation between these variables and the TS severity needs the collection of many met-data at the ground. Unfortunately the lack of a fine mesh met-station network forces the use of local press news or subjective reports to identify the impact of TS. Since 2006 ERSE has been collaborating with the Lombardy Region – Civil Protection Service/Office – in developing and testing a system to detect and nowcast severe thunderstorms, STAF (Storm Track Alert and Forecast). STAF is a nowcasting tool based on Radar and MSG (Meteosat Second Generation) data that selects only severe TS, tracks them and produces alert messages to users. In order to evaluate the severity of a TS, a crucial issue for STAF is the correlation between variables detected by the remote-sensing instruments and the effects at the ground. The paper describes a method to classify the severity of a TS by computing an index named “probability of damage” (PD). The index has been carried out by means of a storm archive, where radar and satellite data are stored together with damages reports from newspapers, all collected in 2003 summer. The index has been verified during the 2009 summer, when STAF was applied in a field test involving a group of Civil Protection observers and users. The results of this test are reported in the paper. The test has been also an occasion for verifying the effectiveness of information provided by STAF to selected people locally responsible for public alert and rescue in the case of a severe weather event. [Copyright &y& Elsevier]

On 5 September 2015 a violent hailstorm hit the Gulf and the city of Naples in Italy. The storm originated over the Tyrrhenian Sea dropping 7–10 cm diameter hailstones along its path. During its mature phase, at 08:47 UTC, the hailstorm was captured by one overpass of the Global Precipitation Measurement mission Core Observatory (GPM-CO) embarking the GPM Microwave Imager (GMI) and the Ka/Ku-band Dual-frequency Precipitation Radar (DPR). In this paper, observations by both GMI and DPR are thoroughly analyzed in conjunction with other spaceborne and ground-based measurements, to show how the GPM-CO integrates established observational tools in monitoring, understanding, and characterizing severe weather. Rapid-scan MSG SEVIRI images show an extremely rapid development, with 10.8 μm cloud-top temperatures dropping by 65 K in 40 min down to 198 K. The LIghtning NETwork registered over 37,000 strokes in 5 h, with intracloud positive stroke fraction increasing during the regeneration phases, when ground-based polarimetric radar and DPR support the presence of large graupel/hail particles. DPR Ku 40 dBZ and 20 dBZ echo top heights at 14 km and 16 km, respectively, indicate strong updraft and deep overshooting. GMI extremely low brightness temperatures (TBs) in correspondence of the convective core (158, 97, 67, and 87 K at 18.7, 36.5, 89 and 166 GHz) are compatible with the presence of massive ice particles. In two years of GPM global observations the storm ranks as fourth and first in terms of minimum 36.5 and 18.7 GHz (V-pol) TBs, respectively. This study illustrates GPM-CO sensing capabilities for characterizing the structure of such severe hailstorm, while providing observational evidence of its intensity and rarity, both globally and over the Mediterranean area. [ABSTRACT FROM AUTHOR]

Urban agriculture (UA) has been acknowledged for several positive effects such as access to fresh food, human activities moderation, agro-biodiversity and social and cultural relationships. In the city of Rome due to social and economic trends this kind of activity is spreading, but so far very few attempts have been addressed to inventorying the UA areas (e.g., community gardens, residential gardens, school gardens, illegal vegetable gardens, urban farms, etc.). Some mapping experiences have been carried out but all lack of a well-established methodology to be applied for a complete inventory. In this paper we propose a conceptual framework and methodology for mapping the UA in Rome through Earth Observation techniques by using the concepts of photointerpretation and the features of the most used web-mapping services. Google Earth has been used as a basic tool for visual interpretation and polygon digitalization, while other services have been used for the availability of additional features (e.g., the oblique view of Microsoft Bing Maps). The integration of the web-mapping services combined with ancillary data improved the UA site detection, allowing to define both the land use and the UA typologies. We argue that the integration of the web-mapping services for building urban agricultural land use datasets is cost-effective compared to processing commercial remote sensing images. The inventory can enable administrators to perform analysis about the interactions between agricultural activities and the urban environment, and to enact informed policies for green and vacant urban spaces management. [ABSTRACT FROM AUTHOR]

Landslide geodatabases, including inventories and thematic data, today are fundamental tools for national and/or local authorities in susceptibility, hazard and risk management. A well organized landslide geo-database contains different kinds of data such as past information (landslide inventory maps), ancillary data and updated remote sensing (space-borne and ground based) data, which can be integrated in order to produce landslide susceptibility maps, updated landslide inventory maps and hazard and risk assessment maps. Italy is strongly affected by landslide phenomena which cause victims and significant economic damage to buildings and infrastructure, loss of productive soils and pasture lands. In particular, the Messina Province (southern Italy) represents an area where landslides are recurrent and characterized by high magnitude, due to several predisposing factors (e.g. morphology, land use, lithologies) and different triggering mechanisms (meteorological conditions, seismicity, active tectonics and volcanic activity). For this area, a geodatabase was created by using different monitoring techniques, including remote sensing (e.g. SAR satellite ERS1/2, ENVISAT, RADARSAT-1, TerraSAR-X, COSMO-SkyMed) data, and in situ measurements (e.g. GBInSAR, damage assessment). In this paper a complete landslide geodatabase of the Messina Province, designed following the requirements of the local and national Civil Protection authorities, is presented. This geo-database was used to produce maps (e.g. susceptibility, ground deformation velocities, damage assessment, risk zonation) which today are constantly used by the Civil Protection authorities to manage the landslide hazard of the Messina Province. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sentinel-2 and PlanetScope used for the first time for grassland FVC estimation. • Vegetation indices enhance the band spectral separability of bright soils. • Best indices based on NIR and red for Sentinel-2, NIR and red-edge for PlanetScope. • High resolution necessary to effectively describe grassland FVC spatial variability. Grassland fractional vegetation cover (FVC) accurate mapping on a large scale is crucial, since degraded grasslands contribute less to provisioning services, carbon storage, water purification, erosion control and biodiversity conservation. The spatial and temporal resolution of Sentinel-2 (S2) and PlanetScope (PS) data has never been explored for grassland FVC estimation so far and will enable researchers and agencies to quantify and map timelier and more precisely grassland processes. In this paper we compare FVC estimation models developed from Landsat-8 (L8), S2 and PS imagery. The reference grassland FVC dataset was obtained on the Paganella ski runs (46.15°N, 11.01°E, Italy) applying unsupervised classification to nadir grassland RGB photographs taken from 1.35 m above the soil. Fractional Response Models between reference FVC and 18 vegetation indices (VIs) extracted from satellite imagery were fitted and analysed. Then, leave-one-out cross validation and spatiotemporal change analysis were also performed. Our study confirms the robustness of the commonly used VIs based on the difference between NIR and the red wavelength region (R2 = 0.91 for EVI using S2 imagery) and indicate that VIs based on the red-edge spectral region are the best performing for PS imagery (R2 = 0.89 for RECI). Only medium to high spatial resolution imagery (S2 and PS) precisely mapped spatial patterns at the study site, since grasslands FVC varies at a fine scale. Previously available imagery at medium to low spatial and temporal resolution (e.g., L8) may still be interesting for analysis requiring long time-series of data. [ABSTRACT FROM AUTHOR]

• A modelling strategy is applied to estimate net forest carbon fluxes in tuscany. • Forest NPP and NEE are predicted at high spatial resolution (23 m). • NPP is assessed versus ground measurements of GSV current annual increment. • NEE is validated versus integrated eddy covariance flux tower observations. This paper presents the application of a recently proposed modelling strategy to yield high spatial resolution estimates of net forest carbon fluxes in Tuscany (Central Italy). The simulation of forest net primary production (NPP) and net ecosystem production (NEP) is based on the combination of remotely sensed and ancillary data which describe the main characteristics of local environment and vegetation. Distinctively, the methodology is driven by a map of growing stock volume (GSV) having a pixel size of 23 × 23 m2 and can therefore yield correspondingly high spatial resolution estimates of forest NPP and NEP. An advancement of the original methodology is also proposed based on the availability of a recently produced soil organic carbon map of Tuscany informative about biomass decomposition (heterotrophic respiration). The modified modelling strategy is applied over the period 2001–2005 and the obtained estimates are assessed against: i) ground observations of GSV current annual increment (CAI) collected for over 600 plots during the last National Forest Inventory of Italy; ii) a high spatial resolution reference NEP map obtained for a Mediterranean pine forest (San Rossore) by the integration of eddy covariance flux data and local CAI observations. Considering the complexity of the simulated processes and of the examined environment, the estimation accuracy achieved is satisfactory for both NPP and NEP. This supports the possibility of applying the proposed modelling strategy to estimate net carbon fluxes at high spatial resolution in other forest environments. [ABSTRACT FROM AUTHOR]

• An operational method to map soil carbon content (SOC) is applied in Tuscany. • It is based on the combination of ground, ancillary and remote sensing datasets. • The method is tested versus all measurements and through a leave-one-out approach. • The final map reproduces the SOC observations and follows main ecosystem features. The organic carbon stored in the topsoil is an essential component of the global carbon cycle which should be quantified for a variety of purposes. The current paper proposes a new approach to map the amount of organic carbon stored in the 0.3 m topsoil (SOC), based on the statistical combination of a large number of ground observations with ancillary and remote sensing data. This approach is applied and tested in Tuscany, a region of Central Italy that is characterised by extremely diversified and heterogeneous environmental features. More than 3500 soil samples were collected and made available for the purpose, together with a soil map, meteorological data, a land use map and MODIS Normalised Difference Vegetation Index (NDVI) imagery. This information was processed by advanced statistical methods to yield a final map which describes the SOC spatial distribution in the region with a spatial resolution of 250 m. The map well reproduces the SOC variability in the region, showing higher SOC values for forests with respect to grasslands and croplands and SOC peaks related to peats and acidic soils. The accuracy assessment, carried out both versus all ground observations and by a leave-one-out cross validation strategy, testifies to the high quality of the SOC map, which has a global RMSE comprised between 17.5 and 32.3 t ha−1. The map is also accompanied by per-pixel estimates of error variance which are informative on the uncertainty of SOC prediction. [ABSTRACT FROM AUTHOR]

This article reviews the recent literature concerning airborne laser scanning for forestry purposes in Italy, and presents the current methodologies used to extract forest characteristics from discrete return ALS (Airborne Laser Scanning) data. Increasing interest in ALS data is currently being shown, especially for remote sensing-based forest inventories in Italy; the driving force for this interest is the possibility of reducing costs and providing more accurate and efficient estimation of forest characteristics. This review covers a period of approximately ten years, from the first application of laser scanning for forestry purposes in 2003 to the present day, and shows that there are numerous ongoing research activities which use these technologies for the assessment of forest attributes (e.g., number of trees, mean tree height, stem volume) and ecological issues (e.g., gap identification, fuel model detection). The basic approaches – such as single tree detection and area-based modeling – have been widely examined and commented in order to explore the trend of methods in these technologies, including their applicability and performance. Finally this paper outlines and comments some of the common problems encountered in operational use of laser scanning in Italy, offering potentially useful guidelines and solutions for other countries with similar conditions, under a rather variable environmental framework comprising Alpine, temperate and Mediterranean forest ecosystems. [ABSTRACT FROM AUTHOR]

Abstract: This work concerns the Late Quaternary evolution of the Astico Valley, with a focus on the relations between the glacial complexes hosted in the terminal valley tract and the piedmont fans. Three distinct glacial events are considered in this paper. Remote sensing, field survey, stratigraphic measurements and reconstructions, sand petrography, radiocarbon dating and pollen analyses allowed attribution of the last one to the LGM, a previous one probably to MIS 6 and the oldest to a generic glaciation of the Middle Pleistocene. Sand petrography analyses show that all these glacial deposits contain rock fragments that reached the Astico Valley through a transfluence of the Adige glacier. During LGM, this glacial stream entered the Astico Valley from the north through the Carbonare saddle (1075 m a.s.l.), as probably happened in previous major glaciations. The chronostratigraphy of two cores drilled near the towns of Vicenza and Villaverla shows that the outwash stream changed its route to the piedmont plain at the end of LGM, as a response to rapid glacial collapse. This switch led to the deactivation of the northwestern sector of the plain (Thiene fan) in favour of the southeastern one (Sandrigo fan). The lower and middle Astico Valley preserved significant evidence of even minor glacial fluctuations during the LGM in response to subtle climatic changes, namely an early glacial withdrawal at 23–24 cal ka, which may be difficult to distinguish in major Alpine glaciers. [Copyright &y& Elsevier]

Abstract: This paper presents the assessment of a NDVI-based parametric model (C-Fix) and a bio-geochemical model (BIOME-BGC) for the simulation of semi-natural grassland primary productivity in Italy. The two models are first calibrated using the gross primary productivity (GPP) data of an eddy covariance flux tower placed over a Mediterranean-temperate hilly area in Central Italy. Next, they are applied to estimate the net primary productivity (NPP) of three independent areas representative of different eco-climatic zones. The first area shows a typical Alpine climate, while the other two are characterized by more or less pronounced Mediterranean features. The accuracy of the NPP estimates is assessed through comparison with destructive dry matter measurements taken in the three areas. The results obtained support the capability of the two models to predict spatial NPP differences across the various grassland sites. The greatest estimation errors are found in the mountain area, mostly due to inaccuracies in the meteorological input data. These errors affect particularly the outputs of the bio-geochemical model and are mitigated by the use of C-Fix, which exploits the remotely sensed information related to the seasonal evolution of green biomass. [Copyright &y& Elsevier]

Although the area burned by single wildfires may range over many orders of magnitude, only a handful papers has explored the factors that contribute to drive fire ignitions into large burns. While in southern Europe most fire ignitions are of human origin, fire propagation is mostly influenced by weather conditions, topography and fuel type. In this framework, the phenological status of vegetation represents the primary driver influencing fuel availability and moisture content. Therefore, any investigation on fire behavior requires the capability of capturing spatio-temporal changes in coarse-scale vegetation phenology that are descriptive of changes in fuel conditions. The aim of this study is thus to apply a habitat suitability modelling tool originally developed in wildlife science for reconstructing the phenological niche of the ignition points of wildfires from remotely-sensed multitemporal NDVI profiles in Sardinia (Italy) in the period of 2000–2008. Overall, our findings indicate that wildfires occur preferentially in remotely-sensed phenological conditions that considerably differ from the mean phenological conditions in Sardinia. When compared to the entire set of ignition points (irrespective of the resulting fire size), ignitions resulting in burned areas larger than 20ha are preferentially associated to a longer and later fire season showing also marked preference for higher fuel loads. [Copyright &y& Elsevier]

Abstract: The use of plastic covers for protecting cultivation in wide rural areas sets remarkable technical problems connected with the effects that large extensions of agricultural land covered with continuous cladding material may determine on aesthetic pollution of the rural landscape and on negative environmental impacts on water cycle, air and agricultural soil of the agro-ecosystem. In this paper, techniques based on remote sensing have been used for an analysis of the rural land use in terms of application of plastics in protected cultivation, in a study area located in Southern Italy near the coastline of the Jonian Sea, where the utilization of plastics in agriculture is widespread. The analysis has been realized using multitemporal Landsat TM images through Supervised classification, image processing, vectorialization and GIS tools. The results enabled the creation of a routine for the auto-detection of the plastic covers. The variation in the visual perception of the agricultural land was examined through a GIS software and a three-dimensional land modelling with solid extrusion and an overlap of the photographic images reproducing plastic-covered protected crops. The results of these simulations could be used for monitoring the variation of rural land use and supporting sound policies of landscape and environmental planning. Through a multi-variate approach to the analysis of environmental and landscape impacts, the concept of a “threshold” limit for the quantity of plastic-covered agricultural structures was proposed, and possible modification of texture and colour of the cladding material mitigating the aesthetic and environmental impact were evaluated. [Copyright &y& Elsevier]

Abstract: Geomorphological analysis is an essential element of many landslide monitoring and hazard assessment studies. Remote sensing techniques have long been an effective tool for such analyses. This paper presents results regarding a geomorphological investigation performed exploiting information derived from high resolution hyper-spectral airborne sensor MIVIS (Multi-spectral Infrared and Visible Imaging Spectrometer) images. The authors propose a workflow based on neural network algorithms used both for image geometric correction and classification. The case study area is the Middle Susa Valley (Italian Western-Alps). It includes the Cassas landslide, a well-known complex landslide affecting slopes on the southern side of the valley, near the town of Salbertrand. At this location several “lifelines” (railway, main roads, hydroelectric tunnels, etc.) are concentrated, and they suffer from a constant threat of landslide activity. Traditional geomorphological characterization of the landslide slope was improved by comparing field campaigns with further information extracted from remote-sensed datasets. Presented results include the identification and characterization of major elements of the present-day active unstable slope (debris-covered areas, fractured/disjointed rock walls, landslide accumulation borders), as well as of individual structural features and landforms (major faults and fractures, trenches, elongated depressions, counterslope scarps), related to long-term deep-seated gravitational slope deformation. [Copyright &y& Elsevier]

Abstract: Radar equipment based on microwave interferometry has been employed to remotely survey the dynamic behaviour of the celebrated and historical towers of Florence, Italy. The measurement technique demonstrated in this paper results both as sensitive and accurate as the conventional technique, which is based on contact accelerometers, plus allows simple and rapid monitoring of structures even at great distances. [Copyright &y& Elsevier]

This paper describes the development and testing of a procedure which combines remotely sensed and ancillary data to monitor forest productivity in Italy. The procedure is based on a straightforward parametric model (C-Fix) that uses the relationship between the fraction of photosynthetically active radiation absorbed by plant canopies (fAPAR) and relevant gross primary productivity (GPP). Estimates of forest fAPAR are derived from Spot-VGT NDVI images and are combined with spatially consistent data layers obtained by the elaboration of ground meteorological measurements. The original version of C-Fix is first applied to estimate monthly GPP of Italian forests during eight years (1999–2006). Next, a modification of the model is proposed in order to simulate the short-term effect of summer water stress more efficiently. The accuracy of the original and modified C-Fix versions is evaluated by comparison with GPP data taken at eight Italian eddy covariance flux tower sites. The experimental results confirm the capacity of C-Fix to monitor national forest GPP patterns and indicate the utility of considering the short-term effect of water stress during Mediterranean dry months. [Copyright &y& Elsevier]

Abstract: Non-invasive documentation of historic façades with fluorescence lidar techniques can provide helpful information for the cultural heritage sector, especially when large areas outdoors are to be examined. This paper presents a case study of the Coliseum, Rome, where both cleaned and heavily soiled areas of the monument were scanned and analysed with two fluorescence lidar systems. Biodeterioration processes were also addressed during the experiment, with the aim of assessing the colonisation extent on selected areas of the monument. Results show the usefulness of a mobile fluorescence lidar system for documentation and survey of large surfaces with complex conditions without limiting the public access to the monument. [Copyright &y& Elsevier]

The management of coastal lagoons is of particular interest due to their high economical importance. In spite of their great productivity, coastal lagoons are often impacted by human pressure which produces water eutrophication. The aim of this paper is to assess the trophic state of the two Mediterranean lagoons taking into account chemical–physical parameters, nutrient concentrations and biological parameters. Two Italian lagoons, Orbetello and Varano (respectively located in Tyrrhenian and Adriatic coast, Italy) were studied between May 2003 and April 2005. Both these systems receive treated urban outflows, agricultural effluents and rivers freshwater inputs. Field collected data showed that studied lagoons were characterized by different human and natural pressures. Orbetello showed the highest water eutrophication, highlighted by the trophic index values, while Varano showed lower eutrophication levels except for the summertime. The values of physical, chemical and biological parameters measured in Orbetello and Varano lagoons indicate that a wide spatial and seasonal gradient of the water characteristics was established during the study period, but in particular in winter. This gradient, typical of estuarine systems, was essentially due to the mixing of freshwater, seawater and anthropogenic inputs. Orbetello lagoon seemed much more affected by the urban impact and the fish-farming activities than Varano lagoon, but the latter showed a greater agriculture activities impact as showed by the remote sensing images. [Copyright &y& Elsevier]

Object of this study is the versatility of a remote-sensing measurement system through experimental survey of the levels of primary and secondary pollutants in urban areas. For the first time a long-range measurement of different pollutant for a long period are reported in the urban area of Rome. Over 10 years of data collection, levels of NO2, O3, SO2, benzene and toluene are measured and discussed by means of a differential optical absorption spectroscopy (DOAS) system installed at ground level. The period is interesting because it represents one of the most important life-changes in Europe and in Italy: in fact, during the 1990s some significant decrees introduced the catalytic pots and limited the amount of benzene and sulfur dioxide in the fuel. The paper will describe how the main primary and secondary pollutants are changed during all the period: particularly, benzene and SO2 are not over a big social problem for the human health whereas the secondary pollutants, ozone and nitrogen dioxide, have the same trends during the period, meaning a no contribution to their evolution from the atmospheric processes. The DOAS technique is resulted very useful and versatile: comparisons with other traditional analyzers (i.e., GC-PID for benzene and toluene investigations) demonstrate how it allows to have information over all the territory saving the qualitative trends and giving a similar quantitative result. In this respect the authors show that the difference is due to the different sampling ways (the traditional analyzers are punctual whereas the DOAS is a remote-sensing technique covering a wide area) and does not influence the atmospheric process interpretation. Finally, it should be considered that the pollutant trends have been interpreted using the concentration measurements of natural radioactivity as tracer of the dynamic properties of the atmospheric boundary layer. [Copyright &y& Elsevier]

This paper describes a systematic procedure for recognizing corrugated asbestos-cement roofing sheets and evaluating their deterioration status related to the asbestos fiber air dispersion that can cause lung cancer. To develop this procedure, we made field and laboratory measurements and gathered airborne MIVIS data covering two industrial test areas in Italy. Laboratory analyses of asbestos-cement samples representing various levels of deterioration allowed for: (a) recognizing dominant minerals using XRD and FTIR instruments, (b) identifying their optical characteristics using portable field spectrometers (ASD and μFTIR), (c) assessing the abundance of surfacing asbestos fibers using a high resolution scanner. Based on the spectral analyses, two linear regression lines were identified by relating optical asbestos-cement material characteristics (i.e. band-depth ratio of the continuum removed calculated for the two asbestos diagnostic bands at 2.32 μm and at 9.44 μm) to the relative percentage of surfacing asbestos fibers related to AC deterioration status. Suitable MIVIS spectral regions were used in a spectral classification procedure to map asbestos-cement roofs. The detected roofs were further analyzed using the obtained linear regression lines to estimate surfacing asbestos fiber abundance, using the MIVIS TIR range at 9.44 μm, selected by means of the asbestos-cement detection limit analysis. The results showed that a hyperspectral scanner with suitable operational characteristics allows for good clustering of AC roofs as a function of their deterioration status. Therefore, this technique can furnish government authorities with an efficient, rapid and repeatable environmental mapping procedure that can provide information about the location of hazardous AC roofing sheets. [Copyright &y& Elsevier]

MODVOLC is a non-interactive algorithm developed at the Hawaii Institute of Geophysics and Planetology (HIGP) that uses low spatial resolution (1-km pixel-size) infrared satellite data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) to map the global distribution of volcanic thermal anomalies in near-real-time. MODVOLC scans the Level-1B MODIS data stream, on a pixel-by-pixel basis, for evidence of pixel and sub-pixel-sized high-temperature radiators. Once a hot spot has been identified its details (location, emitted spectral radiance, time, satellite observation geometry) are written to ASCII text files and transferred via FTP to HIGP, from where the results are disseminated via the internet ). In this paper, we review the underlying principles upon which the algorithm is based before presenting some of the results and data that have been obtained since its inception. We show how MODVOLC reliably detects thermal anomalies at a large number of persistently and sporadically active volcanoes that encompass the full range of common eruptive styles including Erebus (Antarctica), Colima (México), Karymsky (Kamchatka), Popocatépetl (México), Etna (Italy), and Nyiragongo (Democratic Republic of Congo), amongst others. We also present a few cautionary notes regarding the limitations of the algorithm and interpretation of the data it provides. [Copyright &y& Elsevier]

The paper presents an overview of SARA/Digital Puglia (Synthetic Aperture Radar Atlas), a remote sensing environment that shows how grid technologies and high performance computing can be efficiently used to build dynamic earth observation systems for the management of huge quantities of data coming from space missions and for their on-demand processing and delivering to final users. SARA/Digital Puglia is a grid-enabled, high performance digital library of remote sensing images, developed in a joint research project with CACR/Caltech, ISI/USC and the Italian Space Agency. [Copyright &y& Elsevier]

[Display omitted] • Mapping and monitoring of slow-moving landslides. • Remote Sensing, statistical and ground-based measurements have been combined. • Integration of Machine Learning and InSAR techniques for landslide risk assessment. This paper describes a novel methodology where Machine Learning Algorithms (MLAs) have been integrated to assess the landslide risk for slow moving mass movements, processes whose intermittent activity makes challenging any risk analysis worldwide. MLAs has been trained on datasets including Interferometric Synthetic Aperture Radar (InSAR) and additional remote sensing datasets such as aerial stereo photographs and LiDAR and tested in the Termini-Nerano landslides system (southern Apennines, Italy). The availability of such a wealth of materials allows also an unprecedented spatio-temporal reconstruction of the volume and the kinematic of the landslides system through which we could generate and validate the hazard map. Our analysis identifies fifteen slow-moving phenomena, traceable since 1955, whose total area amounts to 4.1 × 105 m2 and volume to ~1.4 × 106 m3. InSAR results prove that seven out of the fifteen slow-moving landslides are currently active and characterized by seasonal velocity patterns. These new insights on the dynamic of the landslides system have been selected as the main independent variables to train three MLAs (Artificial Neural Network, Generalized Boosting Model and Maximum Entropy) and derive the landslide hazard for the area. Finally, official population and buildings census data have been used to assess the landslide risk whose highest values are located in the crown area, south of Termini village, and nearby Nerano. This new methodology provides a different landslide risk scenario compared to the existing official documents for the study area and overall new insights on how to develop landslide risk management strategies worldwide based on a better understanding of slope processes thanks to the latest satellite technologies available. [ABSTRACT FROM AUTHOR]

Slow-moving landslides are widespread natural hazards that can affect both social and economic activities causing damage to structures and infrastructure networks. This paper aims at providing an innovative simplified procedure to analyse road damage induced by slow-moving landslides based on the joint use of landslide inventory maps, a road damage database (collecting both the results of in-situ surveys and Google Street View images) and ground displacement measurements derived from the interferometric processing of satellite SAR images (DInSAR data). The procedure was applied to the case study of the Calabria region (southern Italy) following a two-scale approach. First, a regional-scale analysis was carried out to gather information on the level of road exposure (namely, Index of Exposed Roads) to slow-moving landslides in order to identify the most representative case studies. Then at local scale, relationships between road damage severity level and a selected parameter (i.e. differential settlement computed in two different ways) of slow-moving landslide intensity were derived for two road stretches that differ in geo-lithological and topographic conditions. The results underline the importance of the availability of both reliable landslide inventories and rich road damage databases as well as the potential of high-resolution DInSAR data for obtaining quantitative information on movement rates of roads useful to carry out in-depth studies pursing vulnerability analyses of the infrastructure. • Road damage and slow-moving landslide displacements are correlated in different geo-lithological and topographic conditions. • DInSAR data allow for the recognition of sections of the road network affected by displacements. • Very high-resolution DInSAR data are preferable for local scale analyses of road sections. • Google Street View is a potential tool for rapid road damage inspections. • The systematic application of this procedure could help quantitative analyses of landslide-induced road damage forecasting. [ABSTRACT FROM AUTHOR]

Abstract: Over the past two years the capabilities of adjoint-based inversion have been explored for applications both in geoacoustic inversion and shallow-water acoustic tomography. Starting from case studies based on the principles of optimal control theory the performance of adjoint modeling has been constantly improved to more realistic scenarios ever since. In particular, the concept of semi-automatic adjoint generation has been introduced for inversion of satellite ocean colour imagery and geoacoustic characterization of seabed properties. In the framework of a Maritime Rapid Environmental Assessment sea trial (MREA/BP07) that was conducted in the same area south of the island of Elba as the earlier Yellow Shark experiment (YS94), this paper examines the original YS94 geoacoustic data and the recent MREA07 oceanographic data to demonstrate adjoint-based acoustic monitoring of environmental parameters in Mediterranean shallow waters. First, the adjoint-generated environmental gradients are validated for an application in geoacoustic inversion where the bottom geoacoustic parameters of the layered seabed are determined from long range acoustic propagation data in the water column. Then, for the application in ocean acoustic tomography, the temporal variability of MREA/BP07 sound-speed field data is analyzed in terms of empirical orthogonal functions and the adjoint-based approach is used to track the time-varying sound speed profile of the experimental transect. [Copyright &y& Elsevier]