Alberto Morea

The Impact of the New Earth Gravity Models on the Measurement of the Lense–Thirring Effect

We examine how the new forthcoming Earth gravity models from the CHAMP and, especially, GRACE missions could improve the measurement of the general relativistic Lense–Thirring effect according to the various kinds of observables which could be adopted. In a very preliminary way, we use the first recently released EIGEN2 CHAMP–only and GGM01C GRACE–based Earth gravity models in order to assess the impact of the mismodelling in the even zonal harmonic coefficients of geopotential which represents one of the major sources of systematic errors in this kind of measurement. However, discretion is advised on evaluating the reliability of these results because the Earth gravity models used here, especially EIGEN2, are still very preliminary and more extensive calibration tests must be performed. According to the GGM01C model, the systematic error due to the unmodelled even zonal harmonics of geopotential amounts to 2% for the combination of the nodes of LAGEOS and LAGEOS II and the Perigee of LAGEOS II used up to now by Ciufolini and coworkers in the currently performed LAGEOS-LAGEOS II Lense-Thirring experiment, and to 14% for a combination explicitly presented here which involves the nodes only of LAGEOS and LAGEOS II.

MSANOS: Data-Driven, Multi-Approach Software for Optimal Redesign of Environmental Monitoring Networks

Within the recent EU Water Framework Directive and the modification introduced into national water-related legislation, monitoring assumes great importance in the frame of territorial managerial activities. Recently, a number of public environmental agencies have invested resources in planning improvements to existing monitoring networks. In effect, many reasons justify having a monitoring network that is optimally arranged in the territory of interest. In fact, modest or sparse coverage of the monitored area or redundancies and clustering of monitoring locations often make it impossible to provide the manager with sufficient knowledge for decision-making processes. The above mentioned are typical cases requiring optimal redesign of the whole network; fortunately, using appropriate stochastic or deterministic methods, it is possible to rearrange the existing network by eliminating, adding, or moving monitoring locations and producing the optimal arrangement with regard to specific managerial objectives. This paper describes a new software application, MSANOS, containing some spatial optimization methods selected as the most effective among those reported in literature. In the following, it is shown that MSANOS is actually able to carry out a complete redesign of an existing monitoring network in either the addition or the reduction sense. Both model-based and design-based objective functions have been embedded in the software with the option of choosing, case by case, the most suitable with regard to the available information and the managerial optimization objectives. Finally, two applications for testing the goodness of an existing monitoring network and the optimal reduction of an existing groundwater-level monitoring network of the aquifer of Tavoliere located in Apulia (South Italy), constrained to limit the information loss, are presented.

Applications of Remote Sensing Techniques for Mapping Posidonia Oceanica Meadows

Posidonia Oceanica is a marine phanerogam characterizing an ultimate succession stage (climax) on sandy bottoms in the Mediterranean Sea. In particular, Posidonia Oceanica ecosystems are an important element in improving the water quality of coastal waters [1],[2]. Producing thematic maps of seagrass communities from remote sensing data is a multistep process. First, a preprocessing phase to correct satellite images. The second step is the classification phase. Subsequently the post-classification phase have to improve the accuracy of the results. In this study, Posidonia Oceanica meadows maps of Taranto Gulf, Ionian Sea, in 2001, 2002, and 2004, are produced from Ikonos, ETM+ and ASTER images. A comparison with ground truth measurements in the Ionian Sea shows the advantages and the limits of each approach.

Coastal Observation through Cosmo-SkyMed High-Resolution SAR Images

ABSTRACT Bruno, M.F.; Molfetta M.G.; Mossa, M., Nutricato, R., Morea, A., and Chiaradia, M.T., 2016. Coastal observation through Cosmo-SkyMed high-resolution SAR images. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 795–799. Coconut Creek (Florida), ISSN 0749-0208. The study deals with the application and further improvement of an advanced Earth Observation system, named COSMO-Beach, developed for semi-automatic shoreline extraction and coastal morphology identification. The system exploits SAR Single-Look-Complex data acquired by the COSMO-SkyMed constellation, which is able to provide X-band images with a short revisiting time. The implemented procedures have been tested over a very popular beach in Apulia Region (Italy), affected by erosion problems induced by human activities. The outcomes of the COSMO-Beach system are presented and discussed.

Integration of multitemporal SAR/InSAR techniques and NWM for coastal structures monitoring: Outline of the software system and of an operational service with COSMO-SkyMed data

Continuous monitoring of coastal areas is a necessary condition for proper coastal activities regulation. Coastal area alterations, even in the short term, in fact, may lead to a significant social change in the development of economic activities and lifestyle in populations living in coastal regions. This paper deals with the development of an integrated operational Earth Observation system aimed to provide a complete monitoring system for both natural and built coastal environments. Three different software engines have been coupled to fully exploit the COSMO-SkyMed images using SAR and InSAR techniques (SPINUA and COSMO-Beach modules) providing also APD corrections computed using numerical weather simulations (NWM module). This allows to automate the entire radar image process chain to obtain a complete picture of the most important phenomena for large and small scale. The implemented system has been widely tested on COSMO-SkyMed images taken within the Map Italy program over two different Italian sites and results are presented and discussed.

Automatic GCP extraction with high resolution COSMO-SkyMed products

High-resolution Synthetic Aperture Radar (SAR) data represent an essential resource for the extraction of Ground Control Points (GCP) with sub-metric accuracy without in situ measurement campaigns. Conceptually, SAR-based GCP extraction consists of the following two steps: (i) identification of the same local feature on more SAR images and determination of their range/azimuth coordinates; (ii) spatial 3D positioning retrieval from the 2D radar coordinates, through spatial triangulation (stereo analysis) and inversion methods. In order to boost the geolocation accuracy, SAR images must be acquired from different line of sights, with intersection angles typically wider than 10 degrees, or even in opposite looking directions. In the present study, we present an algorithm specifically designed for ensuring robustness and accuracy in the fully automatic detection of bright isolated targets (steel light poles or towers) even when dealing with opposite looking data takes. In particular, the popular Harris algorithm has been selected as detector because it is the most stable and robust-to-noise algorithm for corners detection on SAR images. We outline the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight (ES) stereo images are available, thus resulting an optimal test site for an assessment of the performances of the processing chain. The experimental analysis proofs that, assumed timing has been properly recalibrated, we are capable to automatically extract GCP from CSK ES data takes consisting in a very limited number of images.

On the use of SAR interferometry to aid navigation of UAV

This study is aimed at exploring the potentials of SAR Interferometry (InSAR) to aid Unmanned Aerial Vehicles (UAV) navigation. The basic idea is to infer both position and attitude of an aerial platform by inspecting the InSAR phase derived by a real time SAR interferometer mounted onboard the platform. Thanks to the expected favorable conditions in terms of geometrical sensitivity as well as signal coherence, the InSAR phase field can be used to derive the terrain elevation. By using both approximated position and attitude values of the platform as well as a reference Digital Terrain Model (DTM) from a mission database available onboard, it is possible to generate a synthetic InSAR phase model to be compared w.r.t. that derived by SAR observations. The geometrical transformation needed to match these two terrain models depends on the difference between position and attitude values derived by the instruments available on board and their actual values. Hence, this matching provides a feedback to be used for adjusting position and attitude. In order to assess the reliability of the proposed approach, we evaluated the interferometric sensitivity to changes in position and attitude. This analysis defines the limits of applicability of the InSAR-based approach and provides indications and requirements on geometric and radiometric parameters.

Monitoring Flood Extent and Area Through Multisensor, Multi-temporal Remote Sensing: The Strymonas (Greece) River Flood

Satellite monitoring of flood events at high spatial and temporal resolution is considered a difficult problem, mainly due to the lack of data with sufficient acquisition frequency and timeliness. Typically, cloudy weather conditions associated with floods obstacle the propagation of e.m. waves in the optical spectral range, forbidding acquisitions by optical sensors. This problem is not present for longer wavelengths, so that radar imaging sensors are recognized as viable solutions for long-term flood event monitoring. In selected cases, however, weather conditions may remain clear for sufficient amounts of time, enabling monitoring of the evolution of flood events through long time series of satellite images, both optical and radar. In this contribution, we present a case study of long-term integrated monitoring of a flood event which affected part of the Strymonas river basin, a transboundary river with source in Bulgaria, which flows then through Greece up to the Aegean Sea. The event started at the beginning of April 2015, due to heavy rain, and the flooded areas lasted up to the beginning of September. Due to the arid climate characterizing the area in this period of the year, weather conditions were cloud-free for most of the time interval covering the event. We collected remotely sensed data, including one high-resolution, X-band, COSMO-SkyMed and several C-band, Sentinel-1 SAR, and optical Landsat 8 images of the area. The SAR backscatter and optical NDVI maps were thresholded to obtain binary flood maps for each day. Threshold values for microwave and optical data were calibrated by comparing one SAR and one optical image acquired on the same date. Results allow to draw a multi-temporal map of the flood evolution with high temporal resolution. The extension of flooded area can also be tracked in time, allowing post-flood recovery monitoring, as well as to envisage future testing of evapotranspiration/absorption models.

Prototype of a multi-platform remote sensing service for fishing forecasting

The present work concerns the development of an automatic Fishing Forecasting System (FiFoS) where satellite observations, ancillary data and in situ measurements (Catch Per Unit Effort) are used to set up, calibrate and validate a fishing forecasting model. Multi-temporal and multi-sensor data fusion techniques are applied to multi-spectral data in order to detect chlorophyll and sea temperature fronts that according to physical models of the upwelling phenomena are related to areas rich of phytoplankton nutrients where a high concentration of pelagic fish is expected.

Automatic SAR/optical cross-matching for GCP monograph generation

Ground Control Points (GCP), automatically extracted from Synthetic Aperture Radar (SAR) images through 3D stereo analysis, can be effectively exploited for an automatic orthorectification of optical imagery if they can be robustly located in the basic optical images. The present study outlines a SAR/Optical cross-matching procedure that allows a robust alignment of radar and optical images, and consequently to derive automatically the corresponding sub-pixel position of the GCPs in the optical image in input, expressed as fractional pixel/line image coordinates. The cross-matching in performed in two subsequent steps, in order to gradually gather a better precision. The first step is based on the Mutual Information (MI) maximization between optical and SAR chips while the last one uses the Normalized Cross-Correlation as similarity metric. This work outlines the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight stereo images with different beams and passes are available. The experimental analysis involves different satellite images, in order to evaluate the performances of the algorithm w.r.t. the optical spatial resolution. An assessment of the performances of the algorithm has been carried out, and errors are computed by measuring the distance between the GCP pixel/line position in the optical image, automatically estimated by the tool, and the “true” position of the GCP, visually identified by an expert user in the optical images.