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

Semantic location extraction from crowdsourced data

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction

Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Synthetic aperture radar remote sensing for landfill monitoring

Despite today’s intensive efforts directed at the recycling and recovery of solid wastes, the controlled disposal of refuse into land remains an important and necessary means of effective waste management. The work presented in this thesis investigates the use of Synthetic Aperture Radar (SAR) data to monitor solid waste landfills. The end-users’ interests vary from detecting the presence of a landfill to more specifically monitoring on-site operations and environmental conditions. Following a general literature review on the application of Earth Observation data for landfill monitoring, the identified research objectives are to: 1) assess whether SAR data can support the identification of landfill sites by distinguishing them from other disturbed areas which present similar optical spectral signatures, and 2) assess the possibility of correlating SAR data with onsite operational procedures. Data acquired for the research are: ground observations and measurements examining the spatial, temporal and biophysical characteristics of a landfill that can influence SAR data; historical and new programmed SAR scenes obtained from the ESA ERS-1 and -2 satellites and from Envisat ASAR instrument; ground based SAR (GB-SAR) acquisitions; simulations based on the RT2 backscatter model; additional space-based and airborne optical data to support the analysis and discussion. The examination of both the SAR amplitude spatial structure and the temporal decorrelation of these sites shows that there are three key characteristics that can distinguish them from other disturbed areas with similar optical spectral signatures: the presence of anisotropic features that strongly affect the SAR backscatter; the fact that the coherence magnitude images of these sites are characterised by large decorrelated areas with transient attributes; and their distinctive positive topography. The analysis highlights that one single-polarisation acquisition can hardly provide correct land-cover information, and consequently knowledge on land-use. The research demonstrates the key value of merging together complementary information derived from both the space and time dimensions, achieving fairly accurate land-use classification results. The research also provides an appreciation of the applicability of the developed techniques in an operational framework. These can suffer a number of limitations if a landfill site is located in a particular environment, and/or if meteorological conditions can significantly affect the radar signal, and/or unusual landfilling procedures are applied by the operators. Concluding remarks on the end-users needs point out that there are a number of aspects, ranging from practical and managerial matters to legal and technical issues, that often discourage the utilisation of EO data by new potential users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin

Characterisation and monitoring of forest disturbances in Ireland using active microwave satellite platforms

Forests are one of the major carbon sinks that significantly contribute towards achieving targets of the Kyoto Protocol, and its successors, in reducing greenhouse (GHG) emissions. In order to contribute to regular National Inventory Reporting, and as part of the on-going development of the Irish national GHG reporting system (CARBWARE), improvements in characterisation of changes in forest carbon stocks have been recommended to provide a comprehensive information flow into CARBWARE. The Irish National Forest Inventory (NFI) is updated once every six years, thus there is a need for an enhanced forest monitoring system to obtain annual forest updates to support government agencies and forest management companies in their strategic decision making and to comply with international GHG reporting standards. Sustainable forest management is imperative to promote net carbon absorption from forests. Based on the NFI data, Irish forests have removed or sequestered an average of 3.8 Mt of atmospheric CO2 per year between 2007 and 2016. However, unmanaged and degraded forests become a net emitter of carbon. Disturbances from human induced activities such as clear felling, thinning and deforestation results in carbon emissions back into the atmosphere. Funded by the Department of Agriculture, Food and the Marine (DAFM, Ireland), this PhD study focuses on exploring the potential of data from L-band Synthetic Aperture Radar (SAR) satellite based sensors for monitoring changes in the small stand forests of Ireland. Historic data from ALOS PALSAR in the late 2000s and more recent data from ALOS-2 PALSAR-2 sensors have been used to map forest areas and characterise the different disturbances observed within three different regions of Ireland. Forest mapping and disturbance characterisation was achieved by combining the machine learning supervised Random Forests (RF) and unsupervised Iterative Self-Organizing Data Analysis (ISODATA) classification techniques. The lack of availability of ground truth data supported use of this unsupervised approach which forms natural clusters based on their multi-temporal signatures, with divergence statistics used to select the optimal number of clusters to represent different forest classes. This approach to forest monitoring using SAR imagery has not been reported in the peer-review literature and is particularly beneficial where there is a dearth of ground-based information. When applied to the forests, mapped with an accuracy of up to 97% by RF, the ISODATA technique successfully identified the unique multi-temporal pattern associated with clear-fells which exhibited a decrease of 4 to 5 decibels (dB) between the images acquired before and after the event. The clustering algorithm effectively highlighted the occurrence of other disturbance events within forests with a decrease of 2±0.5dB between two consecutive years, as well as areas of tree growth and afforestation. A highlight of the work is the successful transferability of the algorithm, developed using ALOS PALSAR, to ALOS-2 PALSAR-2 data thereby demonstrating the potential continuity of annual forest monitoring. The higher spatial and radiometric resolutions of ALOS-2 PALSAR-2 data have shown improvements in forest mapping compared to ALOS PALSAR data. From mapping a minimum forest size of 1.8 ha with ALOS PALSAR, a minimum area of 1.1 ha was achieved with the ALOS-2 PALSAR-2 images. Moreover, even with some different backscatter characteristics of images acquired in different seasons, similar signature patterns between the sensors were retrieved that helped to define the cluster groups, thus demonstrating the robustness of the algorithm and its successful transferability. Having proven the potential to monitor forest disturbances, the results from both the sensors were used to detect deforestation over the time period 2007-2016. Permanent land-use changes pertaining to conversion of forests to agricultural lands and windfarms were identified which are important with respect to forest monitoring and carbon reporting in Ireland. Overall, this work has presented a viable approach to support forest monitoring operations in Ireland. By providing disturbance information from SAR, it can supplement projects working with optical images which are generally limited by cloud cover, particularly in parts of northern, western and upland Ireland. This approach adds value to ground based forest monitoring by mapping distinct forests over large areas on an annual basis. This study has demonstrated the ability to apply the algorithm to three different study areas, with a vision to operationalise the algorithm on a national scale. The main limitations experienced in this study were the lack of L-band SAR data availability and reference datasets. With typically only one image acquired per year, and discrepancies and omissions existing within reference datasets, understanding the behaviour of certain cluster groups representing disturbances was challenging. However, this approach has addressed some issues within the reference datasets, for example locating areas for which a felling licence was granted but where trees were never cut, by providing detailed systematic mapping of forests. Future satellites such as Tandem-L, SAOCOM-2A and 2B, P-band BIOMASS mission and ALOS-4 PALSAR-3 may overcome the issue of limited SAR image acquisitions provided more images per year are available, especially during the summer months

Applications of Photogrammetry for Environmental Research

ISPRS International Journal of Geo-Information: special issue entitled "Applications of Photogrammetry for Environmental Research

Non-Contact Evaluation Methods for Infrastructure Condition Assessment

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections