35 research outputs found

    ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

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    Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research

    Abstracts on Radio Direction Finding (1899 - 1995)

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    The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

    The WWRP Polar Prediction Project (PPP)

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    Mission statement: “Promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on time scales from hours to seasonal”. Increased economic, transportation and research activities in polar regions are leading to more demands for sustained and improved availability of predictive weather and climate information to support decision-making. However, partly as a result of a strong emphasis of previous international efforts on lower and middle latitudes, many gaps in weather, sub-seasonal and seasonal forecasting in polar regions hamper reliable decision making in the Arctic, Antarctic and possibly the middle latitudes as well. In order to advance polar prediction capabilities, the WWRP Polar Prediction Project (PPP) has been established as one of three THORPEX (THe Observing System Research and Predictability EXperiment) legacy activities. The aim of PPP, a ten year endeavour (2013-2022), is to promote cooperative international research enabling development of improved weather and environmental prediction services for the polar regions, on hourly to seasonal time scales. In order to achieve its goals, PPP will enhance international and interdisciplinary collaboration through the development of strong linkages with related initiatives; strengthen linkages between academia, research institutions and operational forecasting centres; promote interactions and communication between research and stakeholders; and foster education and outreach. Flagship research activities of PPP include sea ice prediction, polar-lower latitude linkages and the Year of Polar Prediction (YOPP) - an intensive observational, coupled modelling, service-oriented research and educational effort in the period mid-2017 to mid-2019

    Application of DInSAR techniques to the monitoring of ground deformations

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    The aim of the present thesis has been to test the applicability of the innovative Advanced DInSAR techniques in the natural risk mitigation related to subsidence phenomena. In particular, two test sites have been chosen, both located within alluvial plains and affected by subsidence phenomena: Telese Terme (Italy) where no monitoring network has been installed in spite of the great amount of damaged buildings located in the urban area; Murcia city (Spain) where subsidence has caused damage to structures and infrastructures with an estimated cost of more than 50 million euros. In this second case, the institutions have required studies since '90. For this reason, 20 years of monitoring data are available which have allowed the implementation of an integrated monitoring system based upon satellite DInSAR, conventional field techniques and geotechnical data. Therefore these two areas have been chosen to test different approaches in the use of DInSAR results which can complete a monitoring network where available (as in the case of Murcia city) and replace it where it does not exist (as in the case of Telese Terme). In Murcia case study, the correlation between the temporal evolution of ground surface displacement measures (radar and in situ) and the piezometric groundwater level variation has been analysed to determine mechanisms and critical states of failure; this has permitted to implement a finite element model (FEM) of the phenomenon. Therefore, two models have been carried out: one (called "deep") up to the end of the gravel layer (where the pumping takes place) and one (called "shallow") up to the extensometers' base. The results of the deep model have been compared with DInSAR displacements time series which represent the whole deformation of the stratigraphic column. These comparisons have allowed the individuation of local anomalies of the stiffness values, and have permitted a best model calibration. Moreover, the shallow model results have been compared with the extensometers measurements. These comparisons have showed the occurrence of vertical anisotropies of the permeability. This hypothesis has been verified, analysing the available Lefranc's tests and the most detailed stratigraphic columns and a new model has been proposed. The geotechnical model results have been interpolated through the Ordinary Kriging Radar Errors (OKRE) technique. The achieved deformation maps have been used in the SAR images processing to allow the algorithm to better estimate the no-lineal part of the interferometric phase. In Telese Terme case study, radar measured displacements have allowed to understand the phenomenon spatial extension, its magnitude as same as its historical development. This has permitted the individuation of the causes which provoked damages for some "test buildings". For one of them, a structural model has been implemented; in this case, radar data have been used to verify if its structural response to the displacements detected by SAR corresponded to the overpassing of the limit states. The model results have turned out to have a good correspondence with the forensic analysis achieved in situ. All the proposed approaches could be applied to other scenarios affected by similar phenomena.El objetivo de la presente tesis ha sido probar la aplicabilidad de las técnicas innovadoras de DInSAR Advanced, en la mitigación de los riesgos naturales relacionados con fenómenos de subsidencia. En particular, se han elegido dos sitios de prueba, ambos ubicados en llanuras aluviales y afectados por fenómenos de subsidencia: Telese Terme (Italia) donde no se ha instalado red de vigilancia, a pesar de la gran cantidad de edificios dañados ubicadas en el área urbana y la ciudad de Murcia (España), donde la subsidencia ha causado daños a las estructuras e infraestructuras con un coste estimado de más de 50 millones de euros. En este segundo caso, las instituciones han requerido estudios desde los años 90.Por esta razón, se dispone de 20 años de datos monitorizados los cuales han permitido la implementación de un sistema integrado de vigilancia basado en el satélite dinSAR, técnicas de datos convencionales y datos geotécnicos. Por lo tanto, para probar diferentes enfoques en el uso de los resultados de DInSAR, se han escogidas estas dos áreas de modo que se pueda completar una red de monitoreo donde esté disponible (como en el caso de la ciudad de Murcia) y reemplazarla donde no existe (como en el caso de Telese Terme). En el caso de Murcia, se ha analizado la correlación entre la evolución temporal de las medidas de desplazamiento de la superficie del suelo (radar in situ) y la variación piezométrica del nivel de las aguas subterráneas para determinar los mecanismos y estados críticos de fracaso. Esto ha permitido poner en práctica un modelo de elementos finitos (FEM) del fenómeno. Teniendo en cuenta estos estudios, se han llevado a cabo dos modelos FEM: uno (llamado "(deep) profundo") hasta el extremo del nivel de grava (donde se lleva a cabo el bombeo) y uno (llamado "(shallow) superficial") hasta la base de los extensómetros. Los resultados del modelo de profundidad han sido comparados con las series temporales de deformación DInSAR que representan toda la deformación de la columna estratigráfica. Estas comparaciones han permitido a la individuación de las anomalías locales de los valores de rigidez, y han permitido una mejor calibración del modelo. Por otra parte, los resultados del modelo superficial (shallow), se han comparado con las mediciones extensométricas. Estas comparaciones han mostrado la ocurrencia de anisotropías verticales de la permeabilidad. Esta hipótesis ha sido verificada, analizando las pruebas disponibles de la Lefranc y las columnas estratigráficas más detalladas y se ha propuesto un nuevo modelo. Los resultados del modelo geotécnico han sido interpolados a través de la técnica "Ordinary Kriging Radar Errors" (OKRE). Los mapas de deformación obtenidos han sido utilizados en el procesado de imágenes SAR para permitir al algoritmo una mejor estimación de la parte no lineal de la fase interferométrica. En el caso de Telese Terme, los desplazamientos radar medidos han permitido comprender la extensión espacial del fenómeno, su magnitud y su desarrollo histórico. Esto ha permitido la individuación de las causas que provocaron daños en algunos edificios "de prueba". Para uno de ellos, se ha implementado un modelo estructural; en este caso, se han utilizados, los datos radar para verificar si su respuesta estructural a los desplazamientos detectados por SAR correspondían a la "superación de los estados límite". Los resultados del modelo han resultado tener una buena correspondencia con el análisis forense conseguida in situ. Todos los aproches propuestos se podrían aplicar a otros escenarios afectados por fenómenos similares

    Topics in environmental and physical geodesy

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    A compilation of mathematical techniques and physical basic knowledge in order to prepare the post graduate students of the subjects of physical geodesy, environmental physics and the visiting students of Erasmus-Socrates projects of the Mediterranean Institute of Oceanography of Toulon and the Campus Universitari de la Mediterrania in Vilanova i la Geltru, Barcelona.Postprint (published version

    Advanced techniques for classification of polarimetric synthetic aperture radar data

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    With various remote sensing technologies to aid Earth Observation, radar-based imaging is one of them gaining major interests due to advances in its imaging techniques in form of syn-thetic aperture radar (SAR) and polarimetry. The majority of radar applications focus on mon-itoring, detecting, and classifying local or global areas of interests to support humans within their efforts of decision-making, analysis, and interpretation of Earth’s environment. This thesis focuses on improving the classification performance and process particularly concerning the application of land use and land cover over polarimetric SAR (PolSAR) data. To achieve this, three contributions are studied related to superior feature description and ad-vanced machine-learning techniques including classifiers, principles, and data exploitation. First, this thesis investigates the application of color features within PolSAR image classi-fication to provide additional discrimination on top of the conventional scattering information and texture features. The color features are extracted over the visual presentation of fully and partially polarimetric SAR data by generation of pseudo color images. Within the experiments, the obtained results demonstrated that with the addition of the considered color features, the achieved classification performances outperformed results with common PolSAR features alone as well as achieved higher classification accuracies compared to the traditional combination of PolSAR and texture features. Second, to address the large-scale learning challenge in PolSAR image classification with the utmost efficiency, this thesis introduces the application of an adaptive and data-driven supervised classification topology called Collective Network of Binary Classifiers, CNBC. This topology incorporates active learning to support human users with the analysis and interpretation of PolSAR data focusing on collections of images, where changes or updates to the existing classifier might be required frequently due to surface, terrain, and object changes as well as certain variations in capturing time and position. Evaluations demonstrated the capabilities of CNBC over an extensive set of experimental results regarding the adaptation and data-driven classification of single as well as collections of PolSAR images. The experimental results verified that the evolutionary classification topology, CNBC, did provide an efficient solution for the problems of scalability and dynamic adaptability allowing both feature space dimensions and the number of terrain classes in PolSAR image collections to vary dynamically. Third, most PolSAR classification problems are undertaken by supervised machine learn-ing, which require manually labeled ground truth data available. To reduce the manual labeling efforts, supervised and unsupervised learning approaches are combined into semi-supervised learning to utilize the huge amount of unlabeled data. The application of semi-supervised learning in this thesis is motivated by ill-posed classification tasks related to the small training size problem. Therefore, this thesis investigates how much ground truth is actually necessary for certain classification problems to achieve satisfactory results in a supervised and semi-supervised learning scenario. To address this, two semi-supervised approaches are proposed by unsupervised extension of the training data and ensemble-based self-training. The evaluations showed that significant speed-ups and improvements in classification performance are achieved. In particular, for a remote sensing application such as PolSAR image classification, it is advantageous to exploit the location-based information from the labeled training data. Each of the developed techniques provides its stand-alone contribution from different viewpoints to improve land use and land cover classification. The introduction of a new fea-ture for better discrimination is independent of the underlying classification algorithms used. The application of the CNBC topology is applicable to various classification problems no matter how the underlying data have been acquired, for example in case of remote sensing data. Moreover, the semi-supervised learning approach tackles the challenge of utilizing the unlabeled data. By combining these techniques for superior feature description and advanced machine-learning techniques exploiting classifier topologies and data, further contributions to polarimetric SAR image classification are made. According to the performance evaluations conducted including visual and numerical assessments, the proposed and investigated tech-niques showed valuable improvements and are able to aid the analysis and interpretation of PolSAR image data. Due to the generic nature of the developed techniques, their applications to other remote sensing data will require only minor adjustments

    Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

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    This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

    Techniques for wide-area mapping of forest biomass using radar data

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    Aspects of forest biomass mapping using SAR (Synthetic Aperture Radar) data were studied in study sites in northern Sweden, Germany, and south-eastern Finland. Terrain topography – via the area of a resolution cell – accounted for 61 percent of the total variation in a Seasat (L-band) SAR scene in a hilly and mountainous study site. A methodology – based on least squares adjustment of tie point and ground control point observations in a multi-temporal SAR mosaic dataset – produced a tie point RMSE (Root Mean Square Error) of 56 m and a GCP RMSE of 240 m in the African mosaic of the GRFM (Global Rain Forest Mapping) project. The mosaic consisted of 3624 JERS SAR scenes. A calibration revision methodology – also based on least squares adjustment and points in overlap areas between scenes – removed a calibration artifact of about 1 dB. A systematic search of the highest correlation between forest stem volume and backscattering amplitude was conducted over all combinations of transmit and receive polarisations in three AIRSAR scenes in a German study site. In the P-band, a high and narrow peak around HV-polarisation was found, where the correlation coefficient was 0.75, 0.59, and 0.71 in scenes acquired in August 1989, June 1991, and July 1991, respectively. In other polarisations of P-band, the correlation coefficient was lower. In L-band, the polarisation response was more flat and correlations lower, between 0.54 and 0.70 for stands with a stem volume 100 m3/ha or less. Three summer-time JERS SAR scenes produced very similar regression models between forest stem volume and backscattering amplitude in a study site in south-eastern Finland. A model was proposed for wide area biomass mapping when biomass accuracy requirements are not high. A multi-date regression model employing three summer scenes and three winter scenes produced a multiple correlation coefficient of 0.85 and a stem volume estimation RMSE of 41.3 m3/ha. JERS SAR scenes that were acquired in cold winter conditions produced very low correlations between stem volume and backscattering amplitude.reviewe
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