Assessment of high resolution SAR imagery for mapping floodplain water bodies: a comparison between Radarsat-2 and TerraSAR-X

Flooding is a world-wide problem that is considered as one of the most devastating natural hazards. New commercially available high spatial resolution Synthetic Aperture RADAR satellite imagery provides new potential for flood mapping. This research provides a quantitative assessment of high spatial resolution RADASAT-2 and TerraSAR-X products for mapping water bodies in order to help validate products that can be used to assist flood disaster management. An area near Dhaka in Bangladesh is used as a test site because of the large number of water bodies of different sizes and its history of frequent flooding associated with annual monsoon rainfall. Sample water bodies were delineated in the field using kinematic differential GPS to train and test automatic methods for water body mapping. SAR sensors products were acquired concurrently with the field visits; imagery were acquired with similar polarization, look direction and incidence angle in an experimental design to evaluate which has best accuracy for mapping flood water extent. A methodology for mapping water areas from non-water areas was developed based on radar backscatter texture analysis. Texture filters, based on Haralick occurrence and co-occurrence measures, were compared and images classified using supervised, unsupervised and contextual classifiers. The evaluation of image products is based on an accuracy assessment of error matrix method using randomly selected ground truth data. An accuracy comparison was performed between classified images of both TerraSAR-X and Radarsat-2 sensors in order to identify any differences in mapping floods. Results were validated using information from field inspections conducted in good conditions in February 2009, and applying a model-assisted difference estimator for estimating flood area to derive Confidence Interval (CI) statistics at the 95% Confidence Level (CL) for the area mapped as water. For Radarsat-2 Ultrafine, TerraSAR-X Stripmap and Spotlight imagery, overall classification accuracy was greater than 93%. Results demonstrate that small water bodies down to areas as small as 150m² can be identified routinely from 3 metre resolution SAR imagery. The results further showed that TerraSAR-X stripmap and spotlight images have better overall accuracy than RADARSAT-2 ultrafine beam modes images. The expected benefits of the research will be to improve the provision of data to assess flood risk and vulnerability, thus assisting in disaster management and post-flood recovery

The use of satellite remote sensing for flood risk management

Over the last decades the impact of natural disasters to the global environment is becoming more and more severe. The number of disasters has dramatically increased, as well as the cost to the global economy and the number of people affected. Among the natural disaster, flood catastrophes are considered to be the most costly, devastating, broad extent and frequent, because of the tremendous fatalities, injuries, property damage, economic and social disruption they cause to the humankind. In the last thirty years, the World has suffered from severe flooding and the huge impact of floods has caused hundreds of thousands of deaths, destruction of infrastructures, disruption of economic activity and the loss of property for worth billions of dollars. In this context, satellite remote sensing, along with Geographic Information Systems (GIS), has become a key tool in flood risk management analysis. Remote sensing for supporting various aspects of flood risk management was investigated in the present thesis. In particular, the research focused on the use of satellite images for flood mapping and monitoring, damage assessment and risk assessment. The contribution of satellite remote sensing for the delineation of flood prone zones, the identification of damaged areas and the development of hazard maps was explored referring to selected cases of study

Using ALOS PALSAR derived high - resolution DInSAR to detect slow - moving landslides in tropical forest: Cameron Highlands, Malaysia

Landslide is one of the natural hazards that pose maximum threat for human lives and property in mountainous regions. Mitigation and prediction of this phenomenon can be done through the detection of landslide-susceptible areas. Therefore, an appropriate landslide analysis is needed in order to map and consequently understand the characteristic of this disaster. One of the recent popular remote sensing techniques in deformation analysis is the differential interferometric synthetic aperture radar which is popularly known as DInSAR. Due to the mass vegetation condition in Malaysia, a long-wavelength synthetic aperture radar (∼24 cm) is required in order to be able to penetrate through the forests and reach the bare land. For that reason, ALOS PALSAR HH imagery was used in this study to derive a deformation map of the Gunung Pass area located in the tropical forest of the Cameron Highlands, Malaysia. In this study, the ascending orbit ALOS PALSAR images were acquired in September 2008, January 2009 and December 2009. Subsequently the displacement measurements of the study site (Gunung Pass) were calculated. The accuracy of the result was evaluated through its comparison with ground truth data using the R2 and root mean square error (RMSE) methods. The resulted deformation map showed the landslide locations in the study area from interpretation of the results with 0.84 R2 and 0.151 RMSE. The DInSAR precision was 11.8 cm which proved the efficiency of the proposed method in detecting landslides in a tropical country like Malaysia. It is highly recommended to use the proposed method for any other deformation studies

Flood Extent and Volume Estimation using Multi-Temporal Synthetic Aperture Radar.

Ph. D. Thesis.Satellite imagery has the potential to monitor flooding across wide geographical regions. Recent launches have improved the spatial and temporal resolution of available data, with the European Space Agency (ESA) Copernicus programme providing global imagery at no end-user cost. Synthetic Aperture Radar (SAR) is of particular interest due to its ability to map flooding independent of weather conditions. Satellite-derived flood observations have real-world application in flood risk management and validation of hydrodynamic models. This thesis presents a workflow for estimating flood extent, depth and volume utilising ESA Sentinel-1 SAR imagery. Flood extents are extracted using a combination of change detection, variable histogram thresholding and object-based region growing. An innovative technique has been developed for estimating flood shoreline heights by combining the inundation extents with high-resolution terrain data. A grid-based framework is used to derive the water surface from the shoreline heights, from which water depth and volume are calculated. The methodology is applied to numerous catchments across the north of England that suffered from severe flooding throughout the winter of 2015-16. Extensive flooding has been identified throughout the study region, with peak inundation occurring on 29th December 2015. On this date, over 100 km2 of flooding is identified in the Ouse catchment, equating to a water volume of 0.18 km3. The SAR flood extents are validated against satellite optical imagery, achieving a Total Accuracy of 91% and a Critical Success Index of 77%. The derived water surfaces have an average error of 3 cm and an RMSE of 98 cm compared to river stage measurements. The methods developed are robust and globally applicable, shown with an additional study along the Mackenzie River in Australia. The presented methodology, alongside the increased temporal resolution provided by Sentinel-1, highlights the potential for accurate, reliable mapping of flood dynamics using satellite imagery.NERC, (DREAM) CD

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

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

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel) versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877). Both methods outperformed the standard Normalized Difference Water Index (NDWI) thresholding (OA = 94.63, K = 0.818) by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models). This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study offers important information towards exploring the possibilities of the use of such data to map other significant flood events from space in an economically viable way

Quantifying the Infilling of Mekong Floodplains in Cambodia using DEM Differencing

Global sand and gravel (aggregate) extraction is changing landscapes, with 29,375x106m3 to 36,875x106m3 globally mined each year (Steinberger et al, 2010). Rapid economic development in South East Asia has markedly driven demand for sand in the Mekong River basin (Hackney et al, 2019). The Mekong River, a convenient source of high-quality aggregate, is the main source of sediment in the region (Kondolf, 1994; Bravard et al, 2013). Aggregate is used in the burgeoning construction industry, reclamation projects, and infilling of land along the Mekong’s low-lying floodplains (Pierdet, 2008; Doyle, 2012; Mailhe et al, 2019). Questionnaire-based research found all countries that the Mekong flows through extracted material during 2010-11, of these Cambodia extracted the largest volume of at least 21x106m3yr-1 of Mekong River sediment (Bravard et al, 2013). However, the study timeframe was limited, and specific destinations of this sand were not mapped or quantified with field measurements. This research develops differencing methods that tie the datasets together to facilitate direct comparisons between the 2000 (NASADEM) and 2013 (TanDEM-X) global elevation DEMs, a promising new approach that could be applied globally. For the Cambodia study area over 183±18 x106 m3 of floodplain infill (FPI) occurred, identifying FPI the likely biggest demand for sand along the Cambodian Mekong River. Timing of FPI can be determined with annual high-resolution imagery, with peak infill activity occurring between 2010 and 2011. FPI volumes measured in this period were over 4±2x106m3 greater than estimates of sediment extraction from the Mekong during 2011 (Bravard et al, 2013). FPI decreases flood risk, bringing economic and social advantages to property owners in the basin (Pierdet, 2008). But rising demand for sand in the basin has caused local channel changes and a sediment deficit being supplied to the delta, causing delta retreat and flooding, which has displaced people (Orr et al, 2012; Jordan et al, 2019). The timing and enormous scale of FPI quantified in this paper can inform future frameworks and policies, and the novel techniques provide a pathway to expanding this research elsewhere

Coastal inundation risk assessment using multi-mission satellite altimeter and digital elevation models

One of the most significant impacts of climate change is sea-level rise. The rate of global sea levels is expected to continue rising in the future, which would cause severe damage to coastal areas and threaten the nearby population. This study aims to determine the inundation of coastal areas caused by the effects of rising sea levels using a combination of satellite altimeters, Global Positioning Systems, and Digital Elevation Models. The inundation scenarios for the return periods of 5, 25, 45, 65, and 85 years were successfully simulated using the hydrological model obtained from altimetry data. The inundation scenarios were overlain on land elevation datasets to estimate the inundation risks. Vulnerability assessments of inundation risk maps were analysed in ArcGIS environment to develop maps for vulnerable areas and populations. The study revealed that by the year 2100, sea level is projected to be rising at the rate of between 32cm to 50cm for the sub-chosen sea over the Malaysian seas. The results from the assessment also show that Kelantan area is highly vulnerable to the sea-level rise event. At the local scales, nearly 1km2 of total land area is projected to be inundated under the smallest scenario. Under the higher sea level scenarios, over 8.2 km2 is potentially inundated with up to 46,000 people around the coastal and flood plain area of Kelantan, especially near Kota Bahru, could be affected. This study has successfully shown the integration of hydrological model to produce the inundation risk maps, taking into account the extent of land area covered under sea level at different scenarios. Findings are valuable to help the local authorities in developing strategic coastal management plans to address challenges of the climate change