A comparative study of operational vessel detectors for maritime surveillance using satellite-borne synthetic aperture radar

This paper presents a comparative study among four operational detectors that work by automatically post-processing synthetic aperture radar (SAR) images acquired from the satellite platforms RADARSAT-2 and COSMO-SkyMed. Challenging maritime scenarios have been chosen to assess the detectors' performance against features such as ambiguities, significant sea clutter, or irregular shorelines. The SAR images which form the test data are complemented with ground truth to define the reference detection configuration, which permits quantifying the probability of detection, the false alarm rate, and the accuracy of estimating ship dimensions. Although the results show that all the detectors perform well, there is no perfect detector, and a better detection system could be developed that combines the best elements from each of the single detectors. In addition to the comparison exercise, the study has facilitated the improvement of the detectors by highlighting weaknesses and providing means for fixing them.Peer ReviewedPostprint (published version

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

A MODIS-Based Automated Flood Monitoring System for Southeast Asia

Flood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 16 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017). The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-meter Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-hour latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

Efficient SAR MTI simulator of marine scenes

Tècniques de detecció de moviment amb radars d'apertura sintètica multicanals sobre escenaris marítims.[ANGLÈS] Multichannel spaceborne and airborne synthetic aperture radars (SAR) offer the opportunity to monitor maritime traffic through specially designed instruments and applying a suitable signal processing in order to reject sea surface clutter. These processing techniques are known as Moving Target Indication techniques (MTI) and the choice of the most adequate method depends on the radar system and operating environment. In maritime scenes the seas presents a complicated clutter whose temporal/spatial coherence models and background reflectivity depends on a large number of factors and are still subject of research. Moreover the targets kinematics are influenced by the sea conditions, producing in some situations high alterations in the imaged target. These aspects make difficult the detectability analysis of vessels in maritime scenarios, requiring both theoretical models and numerical simulations. This thesis looks into the few available MTI techniques and deals experimentally with them in a developed simulator for maritime SAR images. The results are also presented in a image format, giving the sequence for one trial simulation and the asymptotic probability of detection for the simulated conditions.[CASTELLÀ] Los radares de apertura sintética (SAR) multicanal a bordo de satélites o plataformas aerotransportadas ofrecen la oportunidad de monitorizar el tráfico marítimo a través de instrumentos especialmente diseñados y procesando los datos recibidos de forma adecuada para rechazar la señal provocada por la reflexión del mar. A estas técnicas se las conoce como Moving Target Indication techniques (MTI) y la elección de la más adecuada depende del sistema y del entorno de aplicación. En escenarios marinos, el mar presenta un clutter complicado de modelar, cuya coherencia espacio-temporal y reflectividad radar dependen de un gran número de factores que hoy en día todavía siguen siendo investigados. Por otra parte los parámetros dinámicos del target estan influenciados por las condiciones del mar, produciendo en algunas situaciones graves alteraciones en la formación de la imagen. Estos aspectos dificultan el análisis de la detección de las embarcaciones, requiriendo modelos teóricos y simulaciones numéricas. Este Proyecto Final de Carrera investiga las técnicas MTI disponibles, aplicándolas sobre las imágenes marítimas generadas por un simulador SAR. Los resultados son la generación de los productos MTI en formato imagen y el cálculo de la probabilidad de detección para cada target.[CATALÀ] Els radars d'obertura sintètica (SAR) multicanal embarcats en satèl·lits o plataformes aerotransportades ofereixen l'oportunitat de monitoritzar el tràfic marítim a través d'instruments especialment dissenyats i processant les dades rebudes de forma adequada per rebutjar la senyal provocada per la reflexió del mar. A aquestes tècniques se les coneix com Moving Target indication techniques (MTI) i l'elecció de la més adequada depèn del sistema i de l'entorn d'aplicació. En escenaris marins, el mar presenta un clutter complicat de modelar, la coherència espai-temporal i reflectivitat radar depenen d'un gran nombre de factors que avui dia encara segueixen sent investigats. D'altra banda els paràmetres dinàmics del target estan influenciats per les condicions de la mar, produint en algunes situacions greus alteracions en la formació de la imatge. Aquests aspectes dificulten l'anàlisi de la detecció de les embarcacions, requerint models teòrics i simulacions numèriques. Aquest Projecte Final de Carrera investiga les tècniques MTI disponibles, aplicant-les sobre les imatges marítimes generades per un simulador SAR. Els resultats són la generació dels productes MTI en format imatge i el càlcul de la probabilitat asimptòtica de detecció per a cada target

Detection of temporarily flooded vegetation using time series of dual polarised C-band synthetic aperture radar data

The intense research of the last decades in the field of flood monitoring has shown that microwave sensors provide valuable information about the spatial and temporal flood extent. The new generation of satellites, such as the Sentinel-1 (S-1) constellation, provide a unique, temporally high-resolution detection of the earth's surface and its environmental changes. This opens up new possibilities for accurate and rapid flood monitoring that can support operational applications. Due to the observation of the earth's surface from space, large-scale flood events and their spatiotemporal changes can be monitored. This requires the adaptation of existing or the development of new algorithms, which on the one hand enable precise and computationally efficient flood detection and on the other hand can process a large amounts of data. In order to capture the entire extent of the flood area, it is essential to detect temporary flooded vegetation (TFV) areas in addition to the open water areas. The disregard of temporary flooded vegetation areas can lead to severe underestimation of the extent and volume of the flood. Under certain system and environmental conditions, Synthetic Aperture Radar (SAR) can be utilized to extract information from under the vegetation cover. Due to multiple backscattering of the SAR signal between the water surface and the vegetation, the flooded vegetation areas are mostly characterized by increased backscatter values. Using this information in combination with a continuous monitoring of the earth's surface by the S-1 satellites, characteristic time series-based patterns for temporary flooded vegetation can be identified. This combination of information provides the foundation for the time series approach presented here. This work provides a comprehensive overview of the relevant sensor and environmental parameters and their impact on the SAR signal regarding temporary open water (TOW) and TFV areas. In addition, existing methods for the derivation of flooded vegetation are reviewed and their benefits, limitations, methodological trends and potential research needs for this area are identified and assessed. The focus of the work lies in the development of a SAR and time series-based approach for the improved extraction of flooded areas by the supplementation of TFV and on the provision of a precise and rapid method for the detection of the entire flood extent. The approach developed in this thesis allows for the precise extraction of large-scale flood areas using dual-polarized C-band time series data and additional information such as topography and urban areas. The time series features include the characteristic variations (decrease and/or increase of backscatter values) on the flood date for the flood-related classes compared to the whole time series. These features are generated individually for each available polarization (VV, VH) and their ratios (VV/VH, VV-VH, VV+VV). The generation of the time series features was performed by Z-transform for each image element, taking into account the backscatter values on the flood date and the mean value and standard deviation of the backscatter values from the nonflood dates. This allowed the comparison of backscatter intensity changes between the image elements. The time series features constitute the foundation for the hierarchical threshold method for deriving flood-related classes. Using the Random Forest algorithm, the importance of the time series data for the individual flood-related classes was analyzed and evaluated. The results showed that the dual-polarized time series features are particularly relevant for the derivation of TFV. However, this may differ depending on the vegetation type and other environmental conditions. The analyses based on S-1 data in Namibia, Greece/Turkey and China during large-scale floods show the effectiveness of the method presented here in terms of classification accuracy. Theiv supplementary integration of temporary flooded vegetation areas and the use of additional information resulted in a significant improvement in the detection of the entire flood extent. It could be shown that a comparably high classification accuracy (~ 80%) was achieved for the flood extent in each of study areas. The transferability of the approach due to the application of a single time series feature regarding the derivation of open water areas could be confirmed for all study areas. Considering the seasonal component by using time series data, the seasonal variability of the backscatter signal for vegetation can be detected. This allows for an improved differentiation between flooded and non-flooded vegetation areas. Simultaneously, changes in the backscatter signal can be assigned to changes in the environmental conditions, since on the one hand a time series of the same image element is considered and on the other hand the sensor parameters do not change due to the same acquisition geometry. Overall, the proposed time series approach allows for a considerable improvement in the derivation of the entire flood extent by supplementing the TOW areas with the TFV areas

Digital Affordances and Human Rights Advocacy

Keck and Sikkink’s boomerang model (1998) and Risse, Ropp, and Sikkink’s spiral model (1999) anchor much of the scholarly debate about human rights norms propagation. At the heart of both models is “information exchange” among members of broad coalitions advocating for better compliance with human rights norms. An updated spiral model (2013) offers a more liminal, ambiguous, and conditional set of actors and processes than appeared in the first boomerang and spiral models. In this context, we consider the effects of a wide array of digital technologies on human rights NGOs advocacy work and how they affect 21st century information exchange. Traditionally, evidence in human rights investigations is collected in face-to-face meetings among activists and on fact-finding missions. We argue that clusters of digital technologies create “digital affordances” that provide nonstate actors with tools that strengthen their ability to gather scientifically grounded information that pressures noncompliant actors toward commitments with broadly shared human rights norms. As to whether this also leads to greater compliance is less clear.Das Boomerang-Modell von Keck und Sikkink (1998) und das Spiral-Modell von Risse, Ropp und Sikkink (1999) bestimmen einen großen Teil der wissenschaftlichen Debatte über die Verbreitung von Menschenrechtsnormen. Beiden Modellen liegt im Kern der 'Informationsaustausch' unter Angehörigen breiter Koalitionen zugrunde, die die bessere Einhaltung der Menschenrechtsnormen befürworten. Das aktualisierte Spiral-Modell (2013) bietet eine kontextspezifischere und mehrdeutigere Zusammenstellung von Akteuren und Prozessen, als dies in den ersten Boomerang- und Spiral-Modellen der Fall war. In diesem Zusammenhang untersuchen wir die Auswirkungen eines breiten Spektrums an digitalen Technologien auf die Advocacy-Arbeit von Nichtregierungsorganisationen im Bereich der Menschenrechte und wie diese den Informationsaustausch im 21. Jahrhundert beeinflussen. Herkömmlicherweise wird Beweismaterial bei Menschenrechtsuntersuchungen in direktem Austausch unter Aktivist/Innen und bei Erkundungsmissionen gesammelt. Unserer Argumentation zufolge schaffen Cluster von digitalen Technologien "digital affordances", die nichtstaatlichen Akteuren Werkzeuge zur Stärkung ihrer Fähigkeit verschaffen, wissenschaftlich fundierte Informationen zu sammeln, Akteure unter Druck zu setzen und sie zur Einhaltung weitgehend gemeinsamer Menschenrechtsnormen zu verpflichten. Ob dies auch zu einer besseren Einhaltung der Normen führt, ist weniger klar

Power Sensitivity Analysis of Multi-Frequency, Multi-Polarized, Multi-Temporal SAR Data for Soil-Vegetation System Variables Characterization

The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Palsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co- and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencie