Event landside inventory maps can be prepared using conventional or new mapping methods. Conventional methods, including field mapping and the visual interpretation of stereoscopic aerial photographs, are time consuming and resource intensive, restricting the ability to prepare event inventory maps rapidly, repeatedly, and for large and very large areas. This is a significant drawback for regional landslide studies and post event remedial efforts. Investigators are currently experimenting new methods for preparing landslide event inventories exploiting remotely sensed data, including qualitative (visual) and quantitative (numerical) analysis of very-high resolution (VHR) digital elevation models obtained chiefly through LiDAR surveys, and the interpretation and analysis of satellite images, including panchromatic, multispectral, and synthetic aperture radar images. We devised a stepwise, semi-automatic approach to detect, map, and classify internally rainfall-induced shallow landslides exploiting multispectral satellite images taken shortly after a landslide-triggering event, and information on the topographic signature of landslides obtained from a pre-event digital elevation model. In a Bayesian framework, the approach combines a standard image classification obtained by a supervised classifier (e.g., the Mahalanobis Distance classifier) applied to a post-event image, with information on the morphometric landslide signature measured by statistics of terrain slope and cross section convexity in landslide and stable areas. The semi-automatic approach is applied in two steps. First, the rainfall-induced landslides are detected and mapped, separating them from the stable areas. Next, the mapped landslides are classified internally, separating the source from the run out areas. We have applied the approach in a 117 km2 study area in Taiwan, where shallow landslides triggered by high intensity rainfall brought by typhoon Morakot in august 2009 were abundant. Comparison in a GIS of the event landslide inventory produced by the proposed semi-automatic method with a similar event inventory obtained through the visual interpretation of post-event ortho-photographs reveals a degree of matching > 90%. The new approach is flexible, it can exploit different satellite imagery and terrain elevation data, and can be used to map and classify landslides caused by various triggers in different physiographical environments. We expect our new method to contribute to the production of event landslide inventory maps

The exploitation of a multi-temporal stack of SAR intensity images seems to provide satisfactory results in flood detection problems when different spectral signature in presence of inundation are observed. Moreover, the use of interferometric coherence information can further help in the discrimination process. Besides the remote sensing data, additional information can be used to improve flood detection. We propose a data fusion approach, based on Bayesian Networks (BNs) , to analyze an inundation event, involving the Bradano river in the Basilicata region, Italy. Time series of COSMO-SkyMed stripmap SAR images are available over the area. The following random variables have been considered in the BN scheme: F, that is a discrete variable, consisting of two states: flood and no flood; the n-dimensional i variable, obtained by the SAR intensity imagery; the m-dimensional ? variable, obtained by the InSAR coherence imagery; the shortest distance d of each pixel from river course. The proposed BN approach allows to independently evaluate the conditional probabilities P(iF), P(?F) and P(Fd), and then to join them to infer the value P(F = floodi, ?, d), obtaining the probabilistic flood maps (PFMs). We evaluate these PFMs through comparisons with reference flood maps, obtaining overall accuracies higher than 90%.

We apply a Bayesian Network (BN) paradigm to the problem of monitoring flood events through synthetic aperture radar (SAR) and interferometric SAR (InSAR) data. BNs are well-founded statistical tools which help formalizing the information coming from heterogeneous sources, such as remotely sensed images, LiDAR data, and topography. The approach is tested on the fluvial floodplains of the Basilicata region (southern Italy), which have been subject to recurrent flooding events in the last years. Results show maps efficiently representing the different scattering/coherence classes with high accuracy, and also allowing separating the multitemporal dimension of the data, where available. The BN approach proves thus helpful to gain insight into the complex phenomena related to floods, possibly also with respect to comparisons with modeling data.

Accurate flood mapping is important for both planning activities during emergencies and as a support for the successive assessment of damaged areas. A valuable information source for such a procedure can be remote sensing synthetic aperture radar (SAR) imagery. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground. For this reason, a data fusion approach of remote sensing data with ancillary information can be particularly useful. In this paper, a Bayesian network is proposed to integrate remotely sensed data, such as multitemporal SAR intensity images and interferometric-SAR coherence data, with geomorphic and other ground information. The methodology is tested on a case study regarding a flood that occurred in the Basilicata region (Italy) on December 2013, monitored using a time series of COSMO-SkyMed data. It is shown that the synergetic use of different information layers can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to data obtained independently from the analysis of optical images; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, in spite of their heterogeneous temporal SAR/InSAR signatures, reaching accuracies of up to 89%.

Reliable land cover mapping of agricultural areas require high resolution remotesensing and robust classification techniques. In this paper, we propose the integration of spectralinformation with spatial information using the traditional statistical supervised classifier “MaximumLikelihood” and a geostatistical tool, “Indicator Kriging” algorithm, for the developmentof land cover maps by supervised classification from remotely sensed data at medium and highspatial resolution. The proposed method showed better results in classes’ discrimination withsmoother resulting maps than the ones produced using only spectral information. Two differentsatellites imagery were analyzed: a Landsat TM5 image at medium spatial resolution acquiredduring 2006 and an Ikonos II image at higher spatial resolution acquired during 2008. The betterperformance of the “combined” approach compared to the traditional Maximum Likelihoodtechnique was confirmed by confusion matrix. The overall accuracy increases from 76.16%to 85.96% for LandsatTM image and from 71.56% to 80.25% for the IKONOS image.

Multi-sensor, multi-band and multi-temporal remote sensing data can be very useful in precise flood monitoring. In this paper, we describe DAFNE, a Matlab (R)-based, open source toolbox, to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of producing time series of output flood maps, and thus follow the evolution of single or recurrent flood events. Here, an application of the toolbox is illustrated to delineate a flood map, close to the peak of inundation occurred in April 2015 on the Strymonas river (Greece), from multi-band optical and SAR data.

We propose a semi-automatic approach to detect, map and classify rainfall-induced shallow landslides. Theapproach combines the classification of a post-event multispectral satellite image with information on themorphometric signature of landslides in a Bayesian framework. We apply the approach in two steps. First,we detect and map the rainfall-induced landslides separating the stable ground from the failed areas. Next,we classify internally the landslides separating the source from the run out areas. We obtain the prior prob-ability from the Mahalanobis discriminant function used to classify the satellite image, and the likelihoodfrom the frequency distribution of terrain slope and cross section convexity in the pre-existing shallow land-slides. We tested the approach in southern Taiwan, in a catchment where Typhoon Morakot caused abundantlandslides in August 2009. Using the semi-automatic approach, we obtained a detailed event landslide inven-tory map that we compared to an inventory obtained through the visual interpretation of post-eventortho-photographs taken a few days after the landslide triggering rainfall event. Quantitative comparisonin a Geographical Information System revealed a degree of matching between the two event inventories ex-ceeding 90%. The approach is general and flexible, and can be used with different satellite imagery and topo-graphic data. Best suited in landscapes where shallow landslides leave distinct radiometric and topographicsignatures, the approach is expected to facilitate the production of event landslide inventory maps with pos-itive consequences for geomorphological investigations, landslide hazard and risk modeling, and for postevent recovery efforts.

In the Northern part of the Apulia Region, between the Lesina Lagoon and the mouth of the Fortore River, isplaced the Pietre Nere (black stones) Point, a very interesting site and the only outcrop of magmatic rocks in thePuglia region and in the whole Adriatic coast of Italy. The excavation of a canal through this area exposed greymicro- and meso-crystalline gypsum with intercalations of black limestones and marls of Upper Triassic age,mantled by loose sandy Quaternary deposits.The gypsum bedrock shows a high density of cavities, either dissolutional conduits or voids related to gravitationalcollapse processes. Starting from about 1970, a wide new touristic settlement has been built over the area, whichin turn, starting from about 1990, began to suffer for the increasing formation of sinkholes.Several interpretations have been proposed to explain the outcrops of Triassic evaporites that occur in the PietreNere Point area, previously buried by a sequence several kilometres thick of Mesozoic rocks: diapirism; pushingupwards by compressional tectonics; both halokinesis and tectonic deformation.The presence of the sinkholes and of the karst reactivation leads to the demand of a monitoring system, for thedetection of vertical displacements over a large area.Thanks to the ability of radar systems to operate in all weather conditions, day or night, and the possibilityof accurately measuring small surface deformations (changes in altitude of a few millimeters), SAR (syntheticaperture radar) differential interferometry (DInSAR) is an ideal technique for detecting and monitoring grounddeformation phenomena (subsidence, faults, landslides, etc.) over vast areas. As an evolution of DInSAR,persistent scatterers interferometry (PSI) [1] allows to follow millimetric movements of stable objects (mainlybuilding and man-made features) present on the Earth surface through time, studying the interferometric responseof such objects along series of SAR acquisitions.We used data from the ASAR sensor onboard the European Space Agency's ENVISAT satellite, from bothascending (34 acquisitions) and descending (28 acquisitions) geometry, covering a total time interval from May2003 to December 2009.Data were processed with a combination of open-source and in-house developed software [2] in order to extractrelevant information about mean velocities of stable points located on the Lesina Marina area.Relying on the relatively smooth nature of the investigated phenomenon, and assuming negligible north-southmovements, as justified by the overall geometry of the site geomorphological units, information coming fromascending and descending geometries was interpolated over a common georeferenced grid, then combined toobtain vertical and horizontal (east-west) velocity components.We observe PS objects undergoing uplift displacements in both ascending and descending data, with uplift ratesdecreasing in a roughly W-SW direction

Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, the atmospheric artifacts, the visibility problems related to the ground elevation. Nowadays, several satellite missions are available providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit time. A new interesting opportunity is provided by Sentinel-1 mission, which has a spatial resolution comparable to previous ESA C-band missions, and revisit times reduced to up to 6 days. It is envisioned that, by offering regular, global-scale coverage, improved temporal resolution and freely available imagery, Sentinel-1 will guarantee an increasing use of MTI for ground displacement investigations. According to these different SAR space-borne missions, the present work discusses current and future opportunities of MTI applications to ground instability monitoring. Issues related to coherent target detection and mean velocity precision will be addressed through a simple theoretical model assuming backscattering mechanisms related to point scatterers. The paper also presents an example of multi-sensor ground instability investigation over the site of Marina di Lesina, Southern Italy, a village lying over a gypsum diapir, where a hydration process, involving the underlying anhydride, causes a smooth uplift pattern affecting the entire village area, and the formation of scattered sinkholes. More than 20 years of MTI SAR data have been used, coming from both legacy ERS and ENVISAT missions, and last-generation Radarsat-2, COSMO-SkyMed, and Sentinel-1A sensors

The use of remote sensed images in many applications of environmental monitoring,change detection, risks analysis, damage prevention, etc. is continuously growing.Classification of remote sensed images, exploited for the production of land cover maps,involves continuous efforts in the refinement of the employed methodologies. The pixel-wise approach, which considers the spectral information associated to each pixel in theimage, is the standard classification methodology. The continuous improving of spatialresolution in remote sensors requires the focus on what is around a single pixel with theintegration of "contextual" information. In order to produce more reliable land cover mapsfrom the classification of high resolution images, this paper analyzes the effectiveness ofthe integration of contextual information comparing two different pixel-wise techniques forits extraction: 1) the post-classification filtering with a Majority filter applied to the mapproduced by the standard Maximum Likelihood algorithm; 2) the segmentation algorithmSMAP. The results were compared. A GeoEye-1 image, exploited in the framework of theAsi-Morfeo project, was considered.

Space-borne SAR Differential Interferometry (DInSAR) techniques are attractive for landslide investigations because of their capability to provide regional scale coverage and, under favourable conditions, spatially dense information on small ground surface deformations. In particular, advanced multi-temporal InSAR techniques such as Persistent Scatterer Interferometry (PSI) allow detecting and monitoring, with millimetre precision, displacements occurring on selected radar targets (PS) exhibiting coherent radar backscattering properties. PS targets correspond mainly to man-made structures or to rock outcrops, and their spatial density depends on the ground coverage, and it is maximum over urban areas. The application of multi-temporal InSAR analysis to slope instability monitoring poses challenges related to the complex kinematics of the phenomenon, as well as to the unfavourable settings of the area affected by landslides, often occurring on sites of limited extension, characterized by steep topography and variable vegetation cover. This is the case of the Daunia region, located in the Southern Italian Apennine Mountains, which is characterised by scarce urbanisation (mainly small hill-top towns) and dense vegetation cover. The SPINUA (Stable Point INterferometry over Un-urbanised Areas) PSI multi-temporal processing technique was used in the past years to detect and measure ground displacements over this region. Both C-band medium resolution SAR data from ERS-1/2 and ENVISAT ESA satellites, and X-band high resolution SAR data from the TerraSAR-X (TSX) satellite were used. Results indicate that PSI can be profitably used to investigate slope instability, mainly over the urban and peri-urban areas, and that, on these sites, TSX data result very promising for monitoring areas where ERS/ENVISAT PS density is too low. Nevertheless, the application of PSI for slope instability monitoring still remain problematic or impossible in rural and mountainous areas. This is the case, for instance, of the Municipality of Carlantino, where PS targets detected by both C- and X-band data correspond to urban structures or peri-urban walls and guard rails, while a large landslide, extending for about 2 km from the hilltop down to the valley, is lacking stable coherent targets, due to the vegetation cover. In order to allow stability monitoring through spaceborne SAR interferometry, a network of passive reflectors was designed and deployed on the area of interest. The Corner Reflectors (CR) were designed for TerraSAR-X stripmap acquisitions, and consist of three triangular metal panels welded perpendicular to each others to form a trihedral shape which ensures that the radar signal is scattered back to the sensor. A small size is preferred to minimize the curvature of the side panels, the effect of wind, the exposition to vandalism, and to allow easier transportation and deployment in the harsh terrain setting. To design the CR network, different factors were taken

The application of Persistent Scatterer Interferometry (PSI) to slope instability monitoring poses challenges related to the complex kinematics of the phenomenon, as well as to the unfavourable settings of the area affected by landslides, often occurring on sites of limited extension, characterized by steep topography and variable vegetation cover. New-generation SAR sensors, such as TerraSAR-X (TSX) thanks to their higher spatial resolution, make PSI applications very promising for monitoring areas with low density man-made. Nevertheless, the application of techniques still remains problematic or impossible in rural and mountainous areas. This is the case, for instance, for the Municipality of Carlantino, in Southern Italy. Both C-band medium resolution SAR data from ESA satellites, and X-band high resolution SAR data from the TSX satellite, were processed through the PSI algorithm SPINUA. Despite the higher spatial density of PS from TSX, the landslide body is lacking coherent targets, due to vegetation and variable land cover. To allow stability monitoring, a network of six CRs was designed and deployed over the landslide test site. Twenty-six TSX stripmap images were processed by using both PSI and an ad hoc procedure based on double-difference analysis of DInSAR phase values on the CR pixels, constrained by the accurate CR height measurements provided by DGPS. Despite the residual noise due to the sub-optimal CR network and the strong atmospheric signal, displacement estimation on the CRs allows to propagate the PSI results downslope, proving the stability of the landslide area subjected to consolidation works.

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

Monitoring represents the main tool for carrying out evaluation procedures and criteria for spatial and temporallandslide forecast. The forecast of landslide behaviour depends on the possibility to identify either evidences ofactivity (displacement, velocity, volume of unstable mass, direction of displacement, and their temporal variation)or triggering parameters (rainfalls).Generally, traditional geotechnical landslide monitoring technologies permit to define, if correctly positionedand with adequate accuracy, the critical value of displacement and/or acceleration into landslide body. Inmost cases, they do not allow real time warning signs to be generated, due to environmental induced errors, andthe information is related to few points on unstable area. Remote-sensing monitoring instruments are capableof inspecting an unstable slope with high spatial and temporal frequency, but allow solely measurements ofsuperficial displacements and deformations.Among these latest technologies, the satellite Persistent Scatterer SAR Interferometry (PSInSAR) is veryuseful to investigate the unstable area both in terms of space and time. Indeed, this technique allows to analysewide areas, individuate critical unstable areas, not identifiable by means detailed in situ surveys, and study thephenomenon evolution in a long time-scale.Although this technique usually adopts, as first approximation, a linear model to describe the displacementof the detected targets, also non-linear models can be used. However, the satellite revisit time, which defines thetime sampling of the detected displacement signal, limits the maximum measurable velocity and acceleration.This makes it difficult to assess in the short time any acceleration indicating a loss of equilibrium and,therefore, a probable reactivation of the landslide.The recent Sentinel-1 mission from the European Space Agency (ESA), provides a spatial resolution comparableto the previous ESA missions, but a nominal revisit time reduced to 6 days. By offering regularglobal-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performanceof PSInSAR for ground displacement investigations.In particular, the short revisit time allows a better time series analysis by improving the temporal samplingand the chances to catch pre-failure signals characterised by high rate and non-linear behaviour signals. Moreover,it allows collecting large data stacks in a short time period, thus improving the PSInSAR performance inemergency (post-event) scenarios.In the present work, we propose to match satellite data with numerical analysis techniques appropriate toevidence unsteady kinematics and, thanks to the high resolution of satellite data and improved temporal sampling,to detect early stages of land instability phenomena.The test area is situated in a small town in the Southern Apennine, Basilicata region, affected by old andnew huge landslides, now close to a live

The recent availability of large amounts of remotely sensed data requires setting up efficient paradigms for the extraction of information from long series of multi-temporal, often multi-sensor, datasets. In this field, monitoring of terrain instabilities is currently performed through algorithms which estimate millimetric displacements of stable (coherent) objects, through analysis of stacks of SAR images acquired in interferometric mode. The result is generally a decomposition of at least part of the complete complex covariance matrix obtained from all possible pairwise combinations of the images in the stack, separating its spatially- and temporally-correlated parts.The same SAR temporal data stacks can be used to apply change detection algorithms, to reveal, over potentially huge spatial scales and with high resolution, terrain surface changes due to e.g. environmental hazards (floods, fires, earthquakes). In this case, again, the temporal covariance matrix contains in practice all the information related to the environmental changes.The covariance matrix, or its normalized version, known as coherence matrix, expresses thus all the information content related to a time series of remotely sensed, coherent data. In the case of SAR data, this kind of representation offers a unified framework for the study of phenomena linked either to the presence of "periods" of persistent scattering characteristics, or to changes of backscattering patterns, hinting to variations in the terrain characteristics.The average operation, involved in the definition of the above-mentioned covariance and coherence matrices, has to be performed necessarily over "homogeneous" pixel sets. This homogeneity criterion can be intended in various ways, including the one connected to the covariance definition itself, thus leading to a sort of recursive estimation process.Moreover, such homogeneity measures are often used as a substitute for the classical Euclidean distance in nonlocal estimate implementation frameworks, used for instance in the design of effective SAR speckle filters.The coherence matrix highlights the role of the interferometric phase. After having suitably modeled various phase contributions, due to topography, atmosphere, etc., it is possible to detect periods in which a target remains stable, and can thus be used as a benchmark for estimating ground deformations or other effects related to the variations of the signal optical path.From the above discussion, it appears that a thorough, physically based modeling of the coherence over such long times series of SAR data constitutes a priority for efficient data exploitation.We illustrate some of the inference which can be made starting from a time series of more than a hundred COSMO-SkyMed (CSK) images acquired in InSAR mode over the Haiti capital of Port-Au-Prince, spanning a period of almost 3 years with short repeat times. Such tight acquisition schedule can be obtained nowadays with latest-generation

Many applications of synthetic aperture radar differential interferometry (DInSAR) lead to a set of sparse phase measurements, e.g. in the processing of long multitemporal stacks of SAR differential interferograms through persistent scatterers interferometry (PSI) techniques. Often, sparse phase data have to be unwrapped, and then interpolated on a regular grid to be useful for subsequent processing steps. This step is necessary for instance in the reconstruction of the so-called APS (Atmospheric Phase Screen). Atmospheric artifacts superimposed on DInSAR measurements have the potential of hindering the accurate estimation of deformation signals. Indeed, sometimes the spatial frequencies of the atmospheric phase contributions can overlap those of deformation signals, so that such artifacts can be misinterpreted as deformation features.For the phase unwrapping stage, the solutions are directly dependent on the PS network density; moreover, phase aliasing, which appears when the signal sampling does not satisfy the Nyquist condition, especially in presence of noise, increases when passing from regular-grid to sparse data. This is because the phase sampling conditions get usually worse.An improvement of the APS estimation step has been proposed, by investigating from the empirical point of view an alternative procedure, which involves an interpolation of the complex field derived from the sparse phase measurements. Unlike traditional approaches, the proposed method allows to bypass the PU step and obtain a regular-grid complex field, from which a wrapped phase field can be extracted. Under general conditions, this smooth phase field can be shown to be a good approximation of the original phase without noise. Moreover, the interpolated, wrapped phase field can be fed to state of the art, regular grid PU algorithms, to obtain a smoother absolute phase field.The performances of this empirical approach are evaluated here over a real dataset, that is composed by 30 ascending SAR X-band COSMO-SkyMed images. The images cover the urban area and outskirts of the capital of Haiti, Port-au-Prince.The accuracy of the reconstructed phase fields is analyzed by the local value of the final inter-image phase coherence (?int), a quality figure related to the residual phase noise after subtraction of all modeled contributions. Its values are taken on points (PS) not used in the interpolation, using different spatial densities and random subsampling patterns in a test area characterized by a strong subsidence bowl.The obtained results may be applied into a broader context than the one specific to the PSI technique, considering the few assumptions on the initial phase field, i.e. its smoothness and good sampling conditions.

We present an example of integration of persistent scatterer interferometry (PSI) and in situ measurements over a landslide in the Bovino hilltop town, in Southern Italy. First, a wide-area analysis of PSI data, derived from legacy ERS and ENVISAT SAR image time series, highlighted the presence of ongoing surface displacements over the known limits of the Pianello landslide, located at the outskirts of the Bovino municipality, in the periods 1995-1999 and 2003-2008, respectively. This prompted local authorities to install borehole inclinometers on suitable locations. Ground data collected by these sensors during the following years were then compared and integrated with more recent PSI datafrom a series of Sentinel-1 images, acquired from March 2014 to October 2016. The integration allows sketching a consistent qualitative model of the landslide spatial and subsurface structure, leading to a coherent interpretation of remotely sensed and ground measurements. The results were possible thanks to the synergistic operation of local authorities and remote sensing specialists, and could represent an example for best practices in environmental management and protection at the regional scale.

Applications such as SAR interferometry [1] are increasingly used in "sparse" contexts, in which information about some geophysical parameters (e.g. millimetric terrain deformations) are only available over some of the imaged pixels, corresponding to stable objects [2]. In such cases, it is often necessary to adapt processing algorithms, developed and optimized for regular data grids, to work on sparse samples. One of such algorithms, at the basis of several InSAR processing chains, is the so-called phase unwrapping (PU), consisting of obtaining absolute phase values (i.e. defined over the whole real interval) from the corresponding principal values, i.e. limited to the interval [??, ?[.Recently, a method to reduce the unwrapping problem of a sparse-grid field to one corresponding to a regular grid, has been proposed [3], based on a preliminary nearest-neighbor interpolation step. The solution to the sparse problem is shown to be mathematically equivalent to that of a corresponding regular grid problem, properly derived from the former. The approach allows to employ existing algorithms for regular-grid PU, such as those based on network theory (e.g. the so-called Minimum Cost Flow, or MCF).In this work, stemming from an analysis of the above-mentioned methodology, giving as a solution an absolute phase significant only over the sampled pixels, we propose an alternate procedure, in which the principal phase interpolation step is based on algorithms more advanced than the simple nearest-neighbor scheme. Such interpolation can be performed over the unit-magnitude complex field obtained from the wrapped phase. In this way, the obtained wrapped phase field results more similar to the original, "physical" regular field from which the sparse samples have been obtained.In the case in which this latter field can be assumed to satisfy general conditions of smoothness and homogeneity [4], this allows to exploit at best such characteristics, and to have finally an absolute phase regular matrix more representative of the real data, and then more effective to use in the subsequent processing steps [5].In the paper, several interpolators are considered, such as radial basis functions (RBF), as well as, more generally, Kriging [6], and their performances and application limits are evaluated in simulation, as a function of both the regularity conditions of the original sampled surface, and the sampling density.

We apply persistent scatterer interferometry (PSI) techniques to synthetic aperture radar (SAR) data from ERS and ENVISAT satellites on the Lesina Marina area, a coastal tourist village in Apulia, Southern Italy, where the excavation of a canal exposed grey micro- and meso-crystalline gypsum which is now showing a high density of cavities and sinkholes due to gravitational collapse processes. We observe PS objects undergoing displacements, along the sensor line of sight, forming the same relatively smooth pattern in all the processed data stacks. Vertical displacement rates, derived through integration of ascending and descending geometries, reach about 4 mm/year on locations adjacent to the canal, gently decreasing towards the western end of the built-up area. High-precision leveling measurements, performed in 1999 and 2010, reveal a substantial agreement with the ENVISAT PSI data, taking into account a small bias due to the choice of the leveling reference point. The dataset, thus validated, suggests the presence of an uplift phenomenon going on steadily for the entire timespan covered by the SAR observations (1992--2009). These observations, supported by petrographic data and in situ investigations, seem only in part compatible with a residual diapirism, and hint instead to more complex processes, such as a combination of diapirism and the hydration of the residual anhydrite in the core of the gypsum mass. These results confirm the importance of the integration between in situ, geologic and geophysical, remotely sensed investigations, as the latter often represent an essential tool to infer whether a given phenomenon, which can be hypothesized by the former, is presently under development.

The use of satellite Synthetic Aperture Radar Interferometry (InSAR) for monitoring ground instability due to landslide events, although advantageous over large spatial scales, still poses challenges related to the recurrently complex kinematics of the phenomena or to the unfavorable settings of the examined areaswith respect to steep topography and vegetated land cover. This paper presents results obtained by usingMulti-temporal InSAR techniques with high resolution TerraSARX (TSX) data formonitoring the Carlantino landslide, located in theDaunian Subapennine (Apulia region, southern Italy) on a slope overlooking a water reservoir, and subjected to several investigations and consolidationworks. The targets detected by using Persistent Scatterer Interferometry (PSI) correspond to urban structures or peri- urban walls and guard rails, while the landslide body is almost completely devoid of stable targets, due to the widespread vegetation and variable land cover. To allowstability monitoring, a network of six Corner Reflectors (CR)was designed and deployed over the landslide test site. The TSX imageswere analyzed by using both the PSI processing and a procedure, based on the double difference analysis of InSAR phase values on the CR pixels. De- spite residual noise and the loss of 2 CRs due to vandalism, the processing allowed verifying the stability of the upper and central part of the landslide body, and relating indirectly the movements at the toe of the landslide to the water level fluctuations of the reservoir. Finally, this experiment suggests some recommendations and guidelines in planning CR deployment in complex landslide sites.©

This paper describes a novel SAR wind direction estimation method based on the computation of local gradients over quasi-linear and quasi-periodic structures detected by SAR imagery. The method relies upon the standard LG method for the part relevant to the computation of the local gradients. The novelty is that the dominant local wind direction and related accuracy are estimated using results derived from the Directional Statistics. The LG-Mod is validated against in situ coastal wind measurements provided by instrumented buoys with 63 ENVISAT ASAR images. Results show an overall agreement with RMSE values obtained for off-shore areas, but residual effects due to the complex phenomena occurring in the proximity of shoreline may degrade the performance when running in automated mode.

L'osservazione della Terra da piattaforme spaziali, integrata con misure in situ e con acquisizione da piattaforme aeree, è una tecnologia di riferimento per il monitoraggio di ampie zone del Territorio con una elevata frequenza spaziale. Queste ca-ratteristiche sono essenziali per investigare, da un lato, l'effetto di modifiche indotte da cambiamenti climatici, dall'altro la presenza di situazioni che siano precursori di cambiamenti, in un'ottica di previsione ed allerta. Nel presente lavoro vengono presentati alcuni esempi applicativi in questo scenario.

This study shows how different remote sensing techniques can be used to distinguish between surface accumulations (scum) and dense blooms of cyanobacteria in the Curonian Lagoon, the largest lagoon in Europe. Cyanobacteria blooms are a major concern in this region due to water quality issues interfering with the conservation of the whole ecosystem. Chlorophyll-a (Chl-a) concentrations can be extremely high (up to about 500mg/m3 in some cases) during cyanobacteria blooms, which are often associated with a surface accumulation of algae. The Medium Resolution Imaging Spectrometer (MERIS) was used to acquire 52 images covering the summers of 2004 to 2011. These images were analyzed to map Chl-a concentrations and the presence of scum using two different band ratio algorithms applied to atmospherically-corrected data. The results identify wind speed as the main driving factor in the surface accumulation of algae, as well as in the spatial distribution of Chl-a. The utility of microwave images was also assessed, as since any cloud cover obviously hampers the use of optical data. Advanced Synthetic Aperture Radar (ASAR) images were collected synchronously with the MERIS data and the normalized radar cross-section (NRCS) signal was corrected for the contribution of wind for the purposes of correlating the results with the MERIS-derived Chl-a concentrations. In general, there was a stepwise decrease in the NRCS for high values of Chl-a (>50mg/m3) with wind speeds in the range of 2 to 6m/s. Under these conditions, our results demonstrate that optical and microwave signals can be used in combination to improve our understanding of cyanobacteria blooming

Air pollution, caused by fuel industries and urban traffic and its environmental impact, are of considerable interest to studies in air quality. In this paper, the monitoring of the air pollution over urban areas in Italy through Aerosol Optical Thickness (AOT) data retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements is presented. The high spatio-temporal frequency of MODIS AOT products (twice per day at 470nm, 1km full resolution) demonstrates that this satellite can be potentially used to routinely monitor the air pollution over land, especially urban area, which is the main source of aerosol particles. In this work AOT data derived by MODIS from November 2010 to February 2011 (winter period) and from May 2011 to August 2011 (summer period) were compared with AOT measurements from 6 different Aerosol Robotic Network (AERONET) stations over Italy (Bari, Lecce, Roma, Ispra, Potenza, Etna). The statistical analysis shows a good agreement between the ground based AOT measurements and the values retrieved using space based sensors, as shown in Figure 1. For all the stations the mean error is negligible, with a correlation ranging from 0.725 (in the worst case) to 0.96 (see Table 1). Moreover, LANDSAT-panchromatic images were used to discriminate urban and rural areas, based on the typical finger-like projections of urban land uses. The results of this study will be presented and commented.

Marina di Lesina is a peculiar geological site, affected by sinkhole phenomena, causing instabilities and failures of infrastructures. This tourist village, lying not far from Punta delle Pietre Nere, the only outcrop of magmatic rock in the Mediterranean basin, sits on a diapir made of Triassic gypsum, mantled by Quaternary sandy deposits. The cutting of the artificial Acquarotta canal in 1930, connecting the nearby Lesina lagoon to the Mediterranean Sea, exposed this grey micro and meso-crystalline gypsum with intercalations of black limestones and marls. This event is a likely cause for the formation of dissolutional conduits and cavities, found in the area, leading to the formation of the sinkholes which have been plaguing the site in the last years [1]. This peculiar geological setting, coupled with its relatively high value as a local touristic resort, led to its selection as a test site for precise InSAR displacement monitoring techniques. The monitoring, started with legacy ERS and ENVISAT sensors, is continuing through analysis of higher-resolution data.

The detection of marine oil slicks using satellite sun-glittered optical imagery has been recently assessed. As the nature of the imaging mechanism involves the altered features of the wind-roughened oil-covered sea surface, it is expected that the radiation reflected from the oil-water system carries information about the physical properties of the floating oil layer. In this paper, we report an investigation on the capability to retrieve the average thickness of thin marine oil slicks by using the sun-glittered component of the solar radiation in the near-infrared (NIR) bands of MEdium Resolution Imaging Spectrometer Instrument (MERIS) and MODerate Resolution Imaging Spectroradiometer (MODIS) images. The developed procedure exploits the Cox and Munk model to compute sun glint reflectance at the sea surface level for both clean and oil polluted sea surface as well. It is assumed that the Fresnel reflection coefficient of the oil-water system carries the relevant optical dependence on oil layer thickness and oil type. The expected oil-water system reflectance is computed by taking into account the non-uniform spatial distribution of the oil volume. This is achieved by considering a pdf of oil thicknesses that matches the observations on controlled oil slicks already reported in the scientific literature. MERIS and MODIS images gathered during the Lebanon oil spill occurred on July and August 2006 were selected as case study. When available, co-located SAR imagery was also considered to corroborate NIR-detected oil slicks.

Sparse phase measurements often need to be interpolated on regular grids, to extend the information to unsampled locations. Typical cases involve the removal of atmospheric phase screen information from Interferometric Synthetic Aperture Radar (InSAR) stacks, or the retrieval of displacement information over extended areas in Persistent Scatterers Interferometry (PSI) applications, when sufficient point densities are available. This operation is usually done after a phase unwrapping (PU) of the sparse measurements to remove the sharp phase discontinuities due to the wrap operation. PU is a difficult and error-prone operation, especially for sparse data. In this work, we investigate from the empirical point of view an alternative procedure, which involves an interpolation of the complex field derived from the sparse phase measurements. Unlike traditional approaches, our method allows to bypass the PU step and obtain a regular-grid complex field from which a wrapped phase field can be extracted. Under general conditions, this can be shown to be a good approximation of the original phase without noise. Moreover, the interpolated, wrapped phase field can be fed to state-of-the-art, regular-grid PU algorithms, to obtain an improved absolute phase field, compared to the canonical method consisting of first unwrapping the sparse-grid data. We evaluate the performance of the method in simulation, comparing it to the classical methodology described above, as well as to an alternative procedure, recently proposed, to reduce a sparse PU problem to a regular-grid one, through a nearest-neighbor interpolation step. Results confirm the increased robustness of the proposed method with respect to the effects of noise and undersampling.

Multi-sensor satellite data are used to assess cyanobacteria blooms in the Curonian Lagoon. The exploitation of SAR, in combination with optical data, is investigated to take full advantage from the all-weather, night/day SAR imaging capability. A dataset of images has been analyzed to: 1) study the effect of cyanobacteria on microwave signals; 2) assess the daily evolution of cyanobacteria bloom from multi-sensors data; and 3) evaluate the dependence of dynamics of blooms on winds. The results show a significant correlation (R2 > 0.8, p<0.001) between the X- and C-band Normalized Radar Cross Section (NRCS) attenuation and the NIR-Red band ratio Index, with the latter considered as a proxy for the presence of cyanobacteria blooms. A combined use of microwave and optical observations can improve the detection of cyanobacteria blooms and their dependency on wind action.

Global warming has increased the frequency of algal blooms in internal water bodies. The algal blooms are an unpleasant sight and hinder various recreational and economic. The increase in the anthropogenic load of nutrients (eutrophication) has led to an increase in the presence of toxic algae, the blue-green algae in the coastal and internal water bodies. A mature flowering of blue-green algae often emerges on top like a layer of foam containing high concentrations of toxins. Contact with these toxins poses a direct health risk for both humans and animals. Therefore, monitoring the concentration of algae and the occurrence of scum in lakes has become a topic of interest for management and science.Optical remote sensing is a validated tool for sensing, monitoring and developing better understanding of the state of lakes. However, it is highly hindered by clouds. For regions with frequent cloud cover, this means loss of data, which derails the purpose of sensing. This makes difficult to spatially and temporally characterize scum area for a comprehensive ecological analysis. Combining data obtained using different types of sensor can be an option worth investigating, and a good candidate for this purpose is the synthetic aperture radar (SAR), due partly to its capacity to collect data independent of cloudy cover.We use a synergistic approach involving optical and SAR images together with meteorological parameters to monitor algal cyanobacteria blooms over Tai Hu and Chaohu lakes and Curonian Lagoon. The satellite images are provided by the Sentinel 1, 2 and 3 satellites. Meteorological parameters come from in situ stations or from the European Centre for Medium-Range Weather Forecasts (ECMWF) database. With respect to optical data, the scum index was developed using ratio of TOA reflectance in NIR and RED bands exploiting the high difference in backscattering and absorption between water with and without scum. For S1 imagery, a polarimetric index is defined and results able to identify anomalies on the lakes surface. The use of Google Earth Engine helped with the images selection and the time series analysis of the indexes. A preliminary study suggests that this index combined with the knowledge of wheatear variables, such as wind speed and the 2 meters air temperature, can reliably detect the occurrences of algal blooms.

Stochastic models are often used to describe the spatial structure of atmospheric phase delays in differential interferometric synthetic aperture radar (DInSAR) data. Synthetic aperture radar interferograms often exhibit anisotropic atmospheric signals. In view of this, the use of anisotropic models for atmospheric phase estimation is increasingly advocated. However, anisotropic models lead to increased computational complexity in estimating the correlation function parameters with respect to the isotropic case. Moreover, the performance is degraded when dealing with DInSAR techniques involving only a few sparse points usable for computations, as in the case of persistent scatterer interferometry applications, particularly when this estimation has to be done in an automated way on many interferograms. In the present work, we propose some observations about the actual advantage given by anisotropic modeling of atmospheric phase in the case of sparse-grid point-target DInSAR applications. Through analysis of simulated data, we observe that an improvement in the performances of kriging reconstruction approaches can be obtained only when sufficient sampling densities are available. In critical sampling conditions, automated methods with reasonable computational cost may improve their performance if external information on the atmospheric phase screen field is available. © 2006 IEEE.

Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, presence of atmospheric artifacts, and visibility problems related to the ground elevation. Nowadays, several satellite missions are available, providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit times.High-resolution X-Band SAR sensors, such as the COSMO-SkyMed constellation, acquire data with spatial resolution reaching metric values, and revisit time up to a few days, leading to an increase in the density of usable targets, as well as to an improved detection of non linear movements. Medium resolution C-band SAR data have been thoroughly exploited in the last two decades, thanks to the ERS-1/2 and ENVISAT-ASAR missions, and Radarsat-1/2. A new interesting opportunity is provided by the Sentinel-1 mission, which has a spatial resolution comparable to previous ESA C-band missions, and a revisit time reduced to 12 and 6 days, by considering, respectively, one or two satellites. It is envisioned that, by offering regular, global scale coverage, improved temporal resolution and freely available imagery, Sentinel-1 will guarantee an increasing use of MTI for ground displacement investigations.The present work discusses opportunities of MTI applications to ground instability monitoring by assessing the performance of the different available satellite missions, according to acquisition parameters such as wavelength, spatial resolution, revisit time and orbital tube size. This performance analysis allows to foresee the quality of displacement maps estimated through MTI according to mission characteristics, and thus to support SAR data selection. In particular, a comparative analysis is carried out, aimed at addressing specific advantages of different satellite missions in L-, C- and X-band. For instance, high resolution data increase the density of coherent targets, thus improving the monitoring of local scale events. Short (X-band) wavelengths improve the sensitivity to displacements. Short revisit times allow collecting large data stacks in short times, and improve the temporal sampling, thus increasing the chances to catch pre-failure signals (high-rate, nonlinear signals). The precision of the displacement rate detection depends on the number of images and on the phase noise, while the precision of the residual height error estimation depends also on the orbital tube size. Sentinel-1 will provide data for the next years with short revisit time, and it is thus likely to provide reliable displacement estimations at large scale, and in quite limited observation time spans. However, due to its narrow orbital tube size, it has a limited height precision, which leads to poor geo-location quality.An example of multi-sensor ground instability investigation is also presented concerning the site of Marina di Lesina, in Southern Italy, where s

Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, atmospheric artifacts, and visibility problems related to ground elevation. Nowadays, several satellite missions are available providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit time. A new and interesting opportunity is provided by Sentinel-1, which has a spatial resolution comparable to that of previous ESA C-band sensors, and revisit times improved by up to 6 days. According to these different SAR space-borne missions, the present work discusses current and future opportunities of MTI applications in terms of ground instability monitoring. Issues related to coherent target detection, mean velocity precision, and product geo-location are addressed through a simple theoretical model assuming backscattering mechanisms related to point scatterers. The paper also presents an example of a multi-sensor ground instability investigation over Lesina Marina, a village in Southern Italy lying over a gypsum diapir, where a hydration process, involving the underlying anhydride, causes a smooth uplift and the formation of scattered sinkholes. More than 20 years of MTI SAR data have been processed, coming from both legacy ERS and ENVISAT missions, and latest-generation RADARSAT-2, COSMO-SkyMed, and Sentinel-1A sensors. Results confirm the presence of a rather steady uplift process, with limited to variations throughout the whole monitored time-period.

Bora is the north-eastern wind which, blowing in the Adriatic Sea from NE and interacting with the orography of the Croatian Dinaric Alps, is characterized by multiple surface intense wind jets [1]. The exceptional Bora events of the beginning of February 2012 over the Gulf of Trieste have been studied by means of two ASAR Wide Swath (WS) images, acquired on the 2nd and 5th of February. Two different methods to extract wind direction from SAR images have been exploited to catch the spatial dynamic of this extreme phenomenon. In particular, SAR sea surface wind directions obtained with Local Gradient (LG) method [2] and the relatively novel technique based on the use of 2D continuous wavelet transform (CWT2) [3] have been analyzed. The retrieved wind directions extracted by the above mentioned methods have been used to estimate the wind speeds with a classical inversion procedure applied to the forward semi-empirical backscatter model CMOD-5 [4]. Results will be compared with wind fields simulated by the atmospheric ETA model [5]. © 2013 IEEE.

Near-infrared (NIR) satellite images of the oil spill event caused by the Fu Shan Hai wreck on 31 May 2003 in the waters between Sweden and Denmark were compared with numerical simulations provided by the MIKE 21 oil drift model. Assuming a skewed probability density function (pdf) of oil parcel thicknesses, a model of the NIR image oil-water contrast reflectance was developed to characterize the expected oil slick distribution in terms of average and maximum oil slick thickness. Since MIKE 21 Spill Analysis (SA) also allows non-uniform distribution of oil volume within the oil slick, both distributions were thus compared by coincidence of the Moderate Resolution Imaging Spectroradiometer (MODIS/Aqua) acquisition, which imaged the oil slick 3 days after the oil spill started. Results showed an excellent agreement in the numerical values of both the expected average and the maximum thickness. In addition, repartition of the oil volume within the slick in the usual thin (sheen) and thick (brown) parts resulted, consistent with the empirical rule of 20% and 80% of the total oil volume, respectively.

We apply high-resolution, X-band, stripmap COSMO-SkyMed data to the monitoring of flood events in the Basilicata region (Southern Italy), where multitemporal datasets are available with short spatial and temporal baselines, allowing interferometric (InSAR) processing. We show how the use of the interferometric coherence information can help to detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which affect algorithms based on SAR intensity alone. The effectiveness of using the additional InSAR information layer is illustrated by RGB composites of various combinations of intensity and coherence data. Analysis of multitemporal SAR intensity and coherence trends reveals complex behavior of various field types, which we interpret through a Bayesian inference approach, based on a manual identification of representative scattering and coherence signatures of selected homogeneous fields. The approach allows to integrate external, ancillary information to derive a posteriori probabilistic maps of flood inundation accounting for different scattering responses to the presence of water. First results of this semiautomated methodology, using simple assumptions for the SAR signatures and a priori information based on the distance from river courses, show encouraging results, and open a path to improvement through use of more complex hydrologic and topo-hydrographic information.

We apply high-resolution, X-band, stripmap COSMO/SkyMed data to the monitoring of a flood event in Southern Basilicata region (Italy), where a multi-temporal dataset is available, allowing interferometric processing. We show how the use of the interferometric phase information can actually help to detect precisely the areas affected by the flood, using e.g. RGB composites of various information layers derived from the data. We also present results of unsupervised clustering of the multi-temporal data, which allow to shed some light on the physical interpretation of some of the identified clusters.

In precision flood monitoring it is important to follow the temporal evolution of an event. Often, however, sufficient temporal coverage of events spanning several days can be attained only by recurring to multi-sensor data, due to different acquisition characteristics and schedules of different types of sensors. We present an example of a successful fusion of data coming from both SAR (COSMO-SkyMed stripmap, 3-m resolution) and optical (RapidEye, multispectral, 5 mresolution) data, covering a flood event in southern Italy. The data fusion is performed through a Bayesian network approach, a reliable means to infer probabilistic infomation from heterogeneous sources. Results show accordance with independent model-based flood mapsreaching accuracies of up to 96%.

Morphological variations of coastlines are caused by several key processes that are influenced by climate conditions, sea level variations, wave energy, tectonics and human-induced phenomena. These processes affect the dynamics of catchment basins and the coastal environment. Since the second half of the 20th century, the Jonian coast of the Basilicata Region (Italy) has witnessed a widespread retreating phenomenon, predominantly due to anthropogenic causes affecting transport processes along the riverbeds and causing reductions in the sediment supply to the coast. The disturbance in the balance between sediment transport carried out by the sea and the sediment supply performed by rivers has led to a deficit in the sediment budget. To understand the morphological dynamics of the littoral environment and quantify the amount of coastal erosion, an analysis of coastline change has been carried out using various data sources: historical cartography, aerial photographs and GPS surveys. Between 1870 and 1954, the 32-km-long Jonian littoral under examination showed an accretion trend, while the loss in beach surface steadily increased between 1954 and 2005. The average change in beach surface has been calculated as about +55,000 m(2)/yr (accretion) between 1870 and 1954 and -16,500 m(2)/yr (erosion) between 1954 and 2005. Overall, 640,000 m(2) of sandy beaches were lost along the entire Jonian coast of the Basilicata Region between 1954 and 2005. Comparing the shoreline between 1870 and 1954, the average net shoreline movement (NSM) is +110 m vs. -30 m between 1954 and 2005. This analysis approach has proven to be effective in quantifying the erosion phenomenon and its effects, despite the lack of homogeneous data series and the variety of spatial and temporal scales over which coastal evolution occurs. The study represents an important step in understanding coastal dynamics in this region. As coastal areas are being affected by an increasing number of population and socio-economic activities, identification of shoreline changes and forecasts of coastline evolution represent key information for coastal scientists, engineers, decision makers and stakeholders for both the management of and development of future plans for coastal environments and for reducing exposure risk to coastal erosion. (c) 2013 Elsevier Ltd. All rights reserved.

We report on the InSAR-related results of the National Research Project (PRIN) entitled "Advanced technologiesin the assessment and mitigation of the landslide risk: precursors detection, previsional models and thematicmapping", funded by the Italian Ministry for Scientific Research. In the framework of the project, multi-temporalinterferometric techniques were applied to time series of SAR data, from legacy ERS and ENVISAT, as well ashigh-resolution TerraSAR-X sensors. We report on the final outcomes of the project, which concentrate on twosites of the Apulia Region, representative of terrain instability problems widespread in the area.The fist one is the coastal area near the Lesina Marina tourist village, at the north of the Region, close to theGargano promontory, where the excavation of a canal exposed grey micro- and meso-crystalline gypsum which isnow showing a high density of cavities and sinkholes due to gravitational collapse processes. A slow but steady upliftphenomenon has been detected by processing through persistent scatterers interferometry (PSI) methodologiesERS and ENVISAT data, acquired in both ascending and descending geometries, and spanning a total time intervalfrom 1995 to 2010. The displacement data were validated by comparison with leveling measurements performedin 2000 and 2010. Derived vertical displacement rates exceed 3-4 mm/y on locations adjacent to the canal, gentlydecreasing towards the western end of the built up area. These observations, supported by ancillary data and insitu investigations performed in the past, seem compatible with processes such as diapirism or the hydration of theresidual anhydrite in the core of the gypsum mass.The second site is an inland landslide area close to the municipality of Carlantino, in the Daunia mountains. Here,a relatively large landslide affects the slopes spreading from the town outskirts to the banks of the Occhito lake, anartificial basin formed by a dam on the Fortore river. PS targets detected by both C- and X-band data correspond tourban structures or peri-urban walls and guard rails, while the landslide body is almost completely devoid of stabletargets, due to the vegetation cover. In order to allow stability monitoring through spaceborne SAR interferometry,a network of passive reflectors was designed and deployed on the area of interest. To design the corner reflector(CR) network, different factors were taken into account: the visibility of the CR by the satellite in terms of geometryand radiometry, the accessibility of the location on the ground, and the relative distance between CR. Resultsof the comparison of phase data over the CR with that of surrounding objects are presented.Work supported by the Italian Ministry of Research in the framework of PRIN 2008 research grant "Advancedtechnologies in the assessment and mitigation of the landslide risk: precursors detection, previsional models andthematic mapping". Te

In case of oil spills due to disasters, one of the environmental concerns is the oil trajectories and spatial distribution. To meet these new challenges, spill response plans need to be upgraded. An important component of such a plan would be models able to simulate the behaviour of oil in terms of trajectories and spatial distribution, if accidentally released, in deep water. All these models need to be calibrated with independent observations. The aim of the present paper is to demonstrate that significant support to oil slick monitoring can be obtained by the synergistic use of oil drift models and remote sensing observations. Based on transport properties and weathering processes, oil drift models can indeed predict the fate of spilled oil under the action of water current velocity and wind in terms of oil position, concentration and thickness distribution. The oil spill event that occurred on 31 May 2003 in the Baltic Sea offshore the Swedish and Danish coasts is considered a case study with the aim of producing three-dimensional models of sea circulation and oil contaminant transport. The High-Resolution Limited Area Model (HIRLAM) is used for atmospheric forcing. The results of the numerical modelling of current speed and water surface elevation data are validated by measurements carried out in Kalmarsund, Simrishamn and Kungsholmsfort stations over a period of 18 days and 17 h. The oil spill model uses the current field obtained from a circulation model. Near-infrared (NIR) satellite images were compared with numerical simulations. The simulation was able to predict both the oil spill trajectories of the observed slick and thickness distribution. Therefore, this work shows how oil drift modelling and remotely sensed data can provide the right synergy to reproduce the timing and transport of the oil and to get reliable estimates of thicknesses of spilled oil to prepare an emergency plan and to assess the magnitude of risk involved in case of oil spills due to disaster.

In the European strategy for a sustainable territory management, the European Commission (EC) gives great relevance to the active participation of the final Users, with the purpose of gathering their specific requirements in the definition processes and in the qualification of the services. Starting from the results obtained by GSE LAND project, funded by ESA within GMES initiatives and by its successor GEOLAND2 project, financed by EC within the FP7 Programme, Regione Puglia supported a regional project aimed to provide EO value added products and services in the fields of environmental monitoring and urban planning. This paper describes the operational case of the construction of the new General Urban Plans for a local Municipality.Keywords: Urban planning, land monitoring, very high resolution remote sensing.

The detection of marine oil slicks using satellite sun-glittered optical imagery has been recently assessed. As the nature of the imaging mechanism involves the altered features of the wind-roughened oil-covered sea surface, it is expected that the radiation reflected from the oil-water system carries information about the physical properties of the floating oil layer. In this paper, we report an investigation on the capability to retrieve the average thickness of thin marine oil slicks by using the sun-glittered component of the solar radiation in the near-infrared (NIR) bands of MERIS and MODIS images. The developed procedure exploits the Cox and Munk model to compute sun glint reflectance at the sea surface level for both clean and oil polluted sea surface as well. It is assumed that the Fresnel reflection coefficient of the oil-water system carries the relevant optical dependence on oil layer thickness and oil type. The expected oil-water system reflectance is computed by taking into account the non-uniform spatial distribution of the oil volume. This is achieved by considering a pdf of oil thicknesses that matches the observations on controlled oil slicks already reported in the scientific literature. MERIS and MODIS images gathered during the Lebanon oil spill occurred on July and August 2006 were selected as case study. When available, colocated SAR imagery was also considered to corroborate NIR-detected oil slicks.

In multi-temporal applications of synthetic aperture radar (SAR) interferometry, differential phase contributions due to atmospheric inhomogeneities, estimated over sparse points, have to be interpolated and removed from the regular-grid interferograms in order to highlight the phase stability of more image pixels, which then add to the available data to infer useful information about terrain displacements or other phenomena of interest. Interpolation is usually done on the phase data after a phase unwrapping (PU) operation. In a previous work, we considered the alternative interpolation step applied directly to the complex phasor derived from the wrapped phase, thus bypassing the error-prone sparse PU operation. In this article, the performances of the proposed methodology are evaluated over atmospheric phase screen (APS) data estimated from a previous processing through persistent scatterers interferometry (PSI) methods. The original persistent scatterer (PS) population is reduced by thresholding their inter-image coherence values, and then further subsampled randomly in a rectangle inside a detected subsidence bowl. Both the classical and the proposed interpolation procedures are applied to the subsampled APS phase values. The interpolated fields are then removed from the rest of the PS, and the residual phase values are compared in terms of inter-image coherence. Results confirm that interpolating complex phasors, thus avoiding PU, gives results equivalent to the standard procedure in good sampling conditions. Moreover, when point sparsity induces phase aliasing, thus hindering the PU operation, the proposed method allows to better recover phase information over unsampled pixels, improving the final results of the PSI processing.

The application of multi-temporal differential SAR interferometric analysis to slope instability monitoring poses challenges related to the complex kinematics of the phenomenon, as well as to the unfavourable settings of the area affected by landslides, often occurring on sites of limited extension, characterized by steep topography and variable vegetation cover. The use of passive reflectors allows to extend the stability monitoring on areas lacking natural coherent reflectors. The present work discusses the problematic aspects of design and deployment of a corner reflector network, and presents preliminary results obtained on the Carlantino village in the Daunia Apennines (Italy) by using X-band TerraSAR-X data. © 2012 IEEE.