An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic

Abstract. An algorithm developed to map flooded areas from synthetic aperture radar imagery is presented in this paper. It is conceived to be inserted in the operational flood management system of the Italian Civil Protection and can be used in an almost automatic mode or in an interactive mode, depending on the user's needs. The approach is based on the fuzzy logic that is used to integrate theoretical knowledge about the radar return from inundated areas taken into account by means of three electromagnetic scattering models, with simple hydraulic considerations and contextual information. This integration aims at allowing a user to cope with situations, such as the presence of vegetation in the flooded area, in which inundation mapping from satellite radars represents a difficult task. The algorithm is designed to work with radar data at L, C, and X frequency bands and employs also ancillary data, such as a land cover map and a digital elevation model. The flood mapping procedure is tested on an inundation that occurred in Albania on January 2010 using COSMO-SkyMed very high resolution X-band SAR data

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Enhanced delineation of degradation in aortic walls through OCT

Degradation of the wall of human ascending thoracic aorta has been assessed through Optical Coherence Tomography (OCT). OCT images of the media layer of the aortic wall exhibit micro-structure degradation in case of diseased aortas from aneurysmal vessels or in aortas prone to aortic dissections. The degeneration in vessel walls appears as low-reflectivity areas due to the invasive appearance of acidic polysaccharides and mucopolysaccharides within a typical ordered microstructure of parallel lamellae of smooth muscle cells, elastin and collagen fibers. An OCT indicator of wall degradation can be generated upon the spatial quantification of the extension of degraded areas in a similar way as conventional histopathology. This proposed OCT marker offers a real-time clinical insight of the vessel status to help cardiovascular surgeons in vessel repair interventions. However, the delineation of degraded areas on the B-scan image from OCT is sometimes difficult due to presence of speckle noise, variable SNR conditions on the measurement process, etc. Degraded areas could be outlined by basic thresholding techniques taking advantage of disorders evidences in B-scan images, but this delineation is not always optimum and requires complex additional processing stages. This work proposes an optimized delineation of degraded spots in vessel walls, robust to noisy environments, based on the analysis of the second order variation of image intensity of backreflection to determine the type of local structure. Results improve the delineation of wall anomalies providing a deeper physiological perception of the vessel wall conditions. Achievements could be also transferred to other clinical scenarios: carotid arteries, aorto-iliac or ilio-femoral sections, intracranial, etc.This work has been supported by the Spanish Government through the CYCIT projects DA2TOI (FIS2010-19860) and FOS4 (TEC2013-47264-C2-1-R)

DUAL-POLARIZED SAR DATA FOR OIL SPILL DETECTION

In this study the capability of the co-polarized phase difference (CPD) for oil spill observation has been investigated. A simple and effective filtering technique, based on the standard deviation (σ) of the CPD Synthetic Aperture Radar (SAR) image, has been implemented. First experiment, accomplished over SIR-C/X-SAR C-Band data, have shown different sensitivity of the filtering technique with respect to oil spills and biogenic oil lookalikes. Since this technique needs only HH and VV data it can be applied also on dual-polarized data such as those provided by the ASAR operated on board on the ENVISAT satellite and the forthcoming COSMO-SKYMED. Key words: SAR; dual-polarized data; oil spill

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats

Providing timely and accurate maps of surface water is valuable for mapping malaria risk and targeting disease control interventions. Radar satellite remote sensing has the potential to provide this information but current approaches are not suitable for mapping African malarial mosquito aquatic habitats that tend to be highly dynamic, often with emergent vegetation. We present a novel approach for mapping both open and vegetated water bodies using serial Sentinel-1 imagery for Western Zambia. This region is dominated by the seasonally inundated Upper Zambezi floodplain that suffers from a number of public health challenges. The approach uses open source segmentation and machine learning (extra trees classifier), applied to training data that are automatically derived using freely available ancillary data. Refinement is implemented through a consensus approach and Otsu thresholding to eliminate false positives due to dry flat sandy areas. The results indicate a high degree of accuracy (mean overall accuracy 92% st dev 3.6) providing a tractable solution for operationally mapping water bodies in similar large river floodplain unforested environments. For the period studied, 70% of the total water extent mapped was attributed to vegetated water, highlighting the importance of mapping both open and vegetated water bodies for surface water mapping

Cruise report / Fahrtbericht Littorina 17-14 [L17-14] North Sea - Dithmarschen Bay (24.08.-31.08.2017)

Vom Sediment zum Topp-Prädator – Einfluss von Eigenschaften des Meeresbodens auf Benthos und benthivore Vögel, Teilprojekt STopP-See STopP-Synthes

A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation

This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°.This work was supported in part by National Nature Science Foundation of China under Grant 41531068, 41371335, 41671356, and 41274010, the Spanish Ministry of Economy and Competitiveness and EU FEDER under Project TIN2014-55413-C2-2-P, China Scholarship Council under Grant 201406370079, and Hunan Provincial Department of Education Science Research Key Project 15A074. The BioSAR2008 campaign data is provided by European Space Agency under the ESA EO Project 14751