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

Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

Potential and Limitations of Open Satellite Data for Flood Mapping

Satellite remote sensing is a powerful tool to map flooded areas. In recent years, the availability of free satellite data significantly increased in terms of type and frequency, allowing the production of flood maps at low cost around the world. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. The proposed methods are suitable to be applied by the community involved in flood hazard management, not necessarily experts in remote sensing processing. As case studies, we selected three flood events that recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, and Sentinel-2 and synthetic aperture radar (SAR) data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., Modified Normalized Difference Water Index, SAR backscattering variation, and supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data such as the digital elevation model-based water depth model and available ground truth data. We calculated flood detection performance (flood ratio) for the different datasets by comparing with flood maps made by official river authorities. The results show that it is necessary to consider different factors when selecting the best satellite data. Among these factors, the time of the satellite pass with respect to the flood peak is the most important. With co-flood multispectral images, more than 90% of the flooded area was detected in the 2015 Ebro flood (Spain) case study. With post-flood multispectral data, the flood ratio showed values under 50% a few weeks after the 2016 flood in Po and Tanaro plains (Italy), but it remained useful to map the inundated pattern. The SAR could detect flooding only at the co-flood stage, and the flood ratio showed values below 5% only a few days after the 2016 Po River inundation. Another result of the research was the creation of geomorphology-based inundation maps that matched up to 95% with official flood maps

The Application of Apriori Algorithm in Predicting Flood Areas

The changing of physical characteristics of the hydrological system have caused a lot of natural phenomenon, which leads to flooding as one of the major problems that cause economic damages and affect people’s life. Therefore, the need for a systematic and comprehensive approach to flood area prediction is needed. This research proposed a flood area prediction model with the application of Apriori algorithm towards hydrological data sets. Department of Irrigation and Drainage Malaysia supply the data sets and flood report from year 2009 to 2015 (November until January) which consist of 7 district. The research begins with the data selection, pre-process the data, and data transformation, then the cleaned data will be tested with the Apriori algorithm. The rules will be evaluating using support, confidence and lift value to rank either it is best rules or not. The results show that each district generates best and crucial rules which consist the association of the villages and water level. Thus, hopefully the result can be use in flood management and can give early an early warning to the villagers at flood risk area

Radar satellite imagery for humanitarian response. Bridging the gap between technology and application

This work deals with radar satellite imagery and its potential to assist of humanitarian operations. As the number of displaced people annually increases, both hosting countries and relief organizations face new challenges which are often related to unclear situations and lack of information on the number and location of people in need, as well as their environments. It was demonstrated in numerous studies that methods of earth observation can deliver this important information for the management of crises, the organization of refugee camps, and the mapping of environmental resources and natural hazards. However, most of these studies make use of -high-resolution optical imagery, while the role of radar satellites is widely neglected. At the same time, radar sensors have characteristics which make them highly suitable for humanitarian response, their potential to capture images through cloud cover and at night in the first place. Consequently, they potentially allow quicker response in cases of emergencies than optical imagery. This work demonstrates the currently unused potential of radar imagery for the assistance of humanitarian operations by case studies which cover the information needs of specific emergency situations. They are thematically grouped into topics related to population, natural hazards and the environment. Furthermore, the case studies address different levels of scientific objectives: The main intention is the development of innovative techniques of digital image processing and geospatial analysis as an answer on the identified existing research gaps. For this reason, novel approaches are presented on the mapping of refugee camps and urban areas, the allocation of biomass and environmental impact assessment. Secondly, existing methods developed for radar imagery are applied, refined, or adapted to specifically demonstrate their benefit in a humanitarian context. This is done for the monitoring of camp growth, the assessment of damages in cities affected by civil war, and the derivation of areas vulnerable to flooding or sea-surface changes. Lastly, to foster the integration of radar images into existing operational workflows of humanitarian data analysis, technically simple and easily-adaptable approaches are suggested for the mapping of rural areas for vaccination campaigns, the identification of changes within and around refugee camps, and the assessment of suitable locations for groundwater drillings. While the studies provide different levels of technical complexity and novelty, they all show that radar imagery can largely contribute to the provision of a variety of information which is required to make solid decisions and to effectively provide help in humanitarian operations. This work furthermore demonstrates that radar images are more than just an alternative image source for areas heavily affected by cloud cover. In fact, what makes them valuable is their information content regarding the characteristics of surfaces, such as shape, orientation, roughness, size, height, moisture, or conductivity. All these give decisive insights about man-made and natural environments in emergency situations and cannot be provided by optical images Finally, the findings of the case studies are put into a larger context, discussing the observed potential and limitations of the presented approaches. The major challenges are summarized which need be addressed to make radar imagery more useful in humanitarian operations in the context of upcoming technical developments. New radar satellites and technological progress in the fields of machine learning and cloud computing will bring new opportunities. At the same time, this work demonstrated the large need for further research, as well as for the collaboration and transfer of knowledge and experiences between scientists, users and relief workers in the field. It is the first extensive scientific compilation of this topic and the first step for a sustainable integration of radar imagery into operational frameworks to assist humanitarian work and to contribute to a more efficient provision of help to those in need.Die vorliegende Arbeit beschäftigt sich mit bildgebenden Radarsatelliten und ihrem potenziellen Beitrag zur Unterstützung humanitärer Einsätze. Die jährlich zunehmende Zahl an vertriebenen oder geflüchteten Menschen stellt sowohl Aufnahmeländer als auch humanitäre Organisationen vor große Herausforderungen, da sie oft mit unübersichtlichen Verhältnissen konfrontiert sind. Effektives Krisenmanagement, die Planung und Versorgung von Flüchtlingslagern, sowie der Schutz der betroffenen Menschen erfordern jedoch verlässliche Angaben über Anzahl und Aufenthaltsort der Geflüchteten und ihrer natürlichen Umwelt. Die Bereitstellung dieser Informationen durch Satellitenbilder wurde bereits in zahlreichen Studien aufgezeigt. Sie beruhen in der Regel auf hochaufgelösten optischen Aufnahmen, während bildgebende Radarsatelliten bisher kaum Anwendung finden. Dabei verfügen gerade Radarsatelliten über Eigenschaften, die hilfreich für humanitäre Einsätze sein können, allen voran ihre Unabhängigkeit von Bewölkung oder Tageslicht. Dadurch ermöglichen sie in Krisenfällen verglichen mit optischen Satelliten eine schnellere Reaktion. Diese Arbeit zeigt das derzeit noch ungenutzte Potenzial von Radardaten zur Unterstützung humanitärer Arbeit anhand von Fallstudien auf, in denen konkrete Informationen für ausgewählte Krisensituationen bereitgestellt werden. Sie sind in die Themenbereiche Bevölkerung, Naturgefahren und Ressourcen aufgeteilt, adressieren jedoch unterschiedliche wissenschaftliche Ansprüche: Der Hauptfokus der Arbeit liegt auf der Entwicklung von innovativen Methoden zur Verarbeitung von Radarbildern und räumlichen Daten als Antwort auf den identifizierten Forschungsbedarf in diesem Gebiet. Dies wird anhand der Kartierung von Flüchtlingslagern zur Abschätzung ihrer Bevölkerung, zur Bestimmung von Biomasse, sowie zur Ermittlung des Umwelteinflusses von Flüchtlingslagern aufgezeigt. Darüber hinaus werden existierende oder erprobte Ansätze für die Anwendung im humanitären Kontext angepasst oder weiterentwickelt. Dies erfolgt im Rahmen von Fallstudien zur Dynamik von Flüchtlingslagern, zur Ermittlung von Schäden an Gebäuden in Kriegsgebieten, sowie zur Erkennung von Risiken durch Überflutung. Zuletzt soll die Integration von Radardaten in bereits existierende Abläufe oder Arbeitsroutinen in der humanitären Hilfe anhand technisch vergleichsweise einfacher Ansätze vorgestellt und angeregt werden. Als Beispiele dienen hier die radargestützte Kartierung von entlegenen Gebieten zur Unterstützung von Impfkampagnen, die Identifizierung von Veränderungen in Flüchtlingslagern, sowie die Auswahl geeigneter Standorte zur Grundwasserentnahme. Obwohl sich die Fallstudien hinsichtlich ihres Innovations- und Komplexitätsgrads unterscheiden, zeigen sie alle den Mehrwert von Radardaten für die Bereitstellung von Informationen, um schnelle und fundierte Planungsentscheidungen zu unterstützen. Darüber hinaus wird in dieser Arbeit deutlich, dass Radardaten für humanitäre Zwecke mehr als nur eine Alternative in stark bewölkten Gebieten sind. Durch ihren Informationsgehalt zur Beschaffenheit von Oberflächen, beispielsweise hinsichtlich ihrer Rauigkeit, Feuchte, Form, Größe oder Höhe, sind sie optischen Daten überlegen und daher für viele Anwendungsbereiche im Kontext humanitärer Arbeit besonders. Die in den Fallstudien gewonnenen Erkenntnisse werden abschließend vor dem Hintergrund von Vor- und Nachteilen von Radardaten, sowie hinsichtlich zukünftiger Entwicklungen und Herausforderungen diskutiert. So versprechen neue Radarsatelliten und technologische Fortschritte im Bereich der Datenverarbeitung großes Potenzial. Gleichzeitig unterstreicht die Arbeit einen großen Bedarf an weiterer Forschung, sowie an Austausch und Zusammenarbeit zwischen Wissenschaftlern, Anwendern und Einsatzkräften vor Ort. Die vorliegende Arbeit ist die erste umfassende Darstellung und wissenschaftliche Aufarbeitung dieses Themenkomplexes. Sie soll als Grundstein für eine langfristige Integration von Radardaten in operationelle Abläufe dienen, um humanitäre Arbeit zu unterstützen und eine wirksame Hilfe für Menschen in Not ermöglichen