Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Drone-based Integration of Hyperspectral Imaging and Magnetics for Mineral Exploration

The advent of unoccupied aerial systems (UAS) as disruptive technology has a lasting impact on remote sensing, geophysics and most geosciences. Small, lightweight, and low-cost UAS enable researchers and surveyors to acquire earth observation data in higher spatial and spectral resolution as compared to airborne and satellite data. UAS-based applications range from rapid topographic mapping using photogrammetric techniques to hyperspectral and geophysical measurements of surface and subsurface geology. UAS surveys contribute to identifying metal deposits, monitoring of mine sites and can reveal arising environmental issues associated with mining. Further, affordable UAS technology will boost exploration data availability and expertise in the global south. This thesis investigates the application of UAS-based multi-sensor data for mineral exploration, in particular the integration of hyperspectral imagers, magnetometers and digital cameras (covering the visible red, green, blue light spectrum). UAS-based research is maturing, however the aforementioned methods are not unified effectively. RGB-based photogrammetry is used to investigate topography and surface texture. Image spectrometers measure mineral-specific surface signatures. Magnetometers detect geomagnetic field changes caused by magnetic minerals at surface and depth. The integration of such UAS sensor-based methods in this thesis augments exploration potential with non-invasive, high-resolution, safe, rapid and practical survey methods. UAS-based surveying acquired, processed and integrated data from three distinct test sites. The sites are located in Finland (Fe-Ti-V at Otanmäki; apatite at Siilinjärvi) and Greenland (Ni-Cu-PGE at Qullissat, Disko Island) and were chosen as geologically diverse areas in subarctic to arctic environments. Restricted accessibility, unfavourable atmospheric conditions, dark rocks, debris and vegetation cover and low solar illumination were common features. While the topography in Finland was moderately flat, a steep landscape challenged the Greenland field work. These restraints meant that acquisitions varied from site to site and how data was integrated and interpreted is dependent on the commodity of interest. Iron-based spectral absorption and magnetic mineral response were detected using hyperspectral and magnetic surveying in Otanmäki. Multi-sensor-based image feature detection and classification combined with magnetic forward modelling enabled seamless geologic mapping in Siilinjärvi. Detailed magnetic inversion and multispectral photogrammetry led to the construction of a comprehensive 3D model of magmatic exploration targets in Greenland. Ground truth at different intensity was employed to verify UAS-based data interpretations during all case studies. Laboratory analysis was applied when deemed necessary to acquire geologic-mineralogic validation (e.g., X-ray diffraction and optical microscopy for mineral identification to establish lithologic domains, magnetic susceptibility measurements for subsurface modelling), for example for trace amounts of magnetite in carbonatite (Siilinjärvi) and native iron occurrence in basalt (Qullissat). Technical achievements were the integration of a multicopter-based prototype fluxgate-magnetometer data from different survey altitudes with ground truth, and a feasibility study with a high-speed multispectral image system for fixed-wing UAS. The employed case studies transfer the experiences made towards general recommendations for UAS application-based multi-sensor integration. This thesis highlights the feasibility of UAS-based surveying at target scale (1–50 km2) and solidifies versatile survey approaches for multi-sensor integration.Ziel dieser Arbeit war es, das Potenzial einer Drohnen-basierten Mineralexploration mit Multisensor-Datenintegration unter Verwendung optisch-spektroskopischer und magnetischer Methoden zu untersuchen, um u. a. übertragbare Arbeitsabläufe zu erstellen. Die untersuchte Literatur legt nahe, dass Drohnen-basierte Bildspektroskopie und magnetische Sensoren ein ausgereiftes technologisches Niveau erreichen und erhebliches Potenzial für die Anwendungsentwicklung bieten, aber es noch keine ausreichende Synergie von hyperspektralen und magnetischen Methoden gibt. Diese Arbeit umfasste drei Fallstudien, bei denen die Drohnengestützte Vermessung von geologischen Zielen in subarktischen bis arktischen Regionen angewendet wurde. Eine Kombination von Drohnen-Technologie mit RGB, Multi- und Hyperspektralkameras und Magnetometern ist vorteilhaft und schuf die Grundlage für eine integrierte Modellierung in den Fallstudien. Die Untersuchungen wurden in einem Gelände mit flacher und zerklüfteter Topografie, verdeckten Zielen und unter oft schlechten Lichtverhältnissen durchgeführt. Unter diesen Bedingungen war es das Ziel, die Anwendbarkeit von Drohnen-basierten Multisensordaten in verschiedenen Explorationsumgebungen zu bewerten. Hochauflösende Oberflächenbilder und Untergrundinformationen aus der Magnetik wurden fusioniert und gemeinsam interpretiert, dabei war eine selektive Gesteinsprobennahme und Analyse ein wesentlicher Bestandteil dieser Arbeit und für die Validierung notwendig. Für eine Eisenerzlagerstätte wurde eine einfache Ressourcenschätzung durchgeführt, indem Magnetik, bildspektroskopisch-basierte Indizes und 2D-Strukturinterpretation integriert wurden. Fotogrammetrische 3D-Modellierung, magnetisches forward-modelling und hyperspektrale Klassifizierungen wurden für eine Karbonatit-Intrusion angewendet, um einen kompletten Explorationsabschnitt zu erfassen. Eine Vektorinversion von magnetischen Daten von Disko Island, Grönland, wurden genutzt, um großräumige 3D-Modelle von undifferenzierten Erdrutschblöcken zu erstellen, sowie diese zu identifizieren und zu vermessen. Die integrierte spektrale und magnetische Kartierung in komplexen Gebieten verbesserte die Erkennungsrate und räumliche Auflösung von Erkundungszielen und reduzierte Zeit, Aufwand und benötigtes Probenmaterial für eine komplexe Interpretation. Der Prototyp einer Multispektralkamera, gebaut für eine Starrflügler-Drohne für die schnelle Vermessung, wurde entwickelt, erfolgreich getestet und zum Teil ausgewertet. Die vorgelegte Arbeit zeigt die Vorteile und Potenziale von Multisensor-Drohnen als praktisches, leichtes, sicheres, schnelles und komfortabel einsetzbares geowissenschaftliches Werkzeug, um digitale Modelle für präzise Rohstofferkundung und geologische Kartierung zu erstellen

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 2: TIMS Workshop

This publication is the second volume of the summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop on January 23-24. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop on January 25-26. The summaries for this workshop appear in volume 3; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop on January 26. The summaries for this workshop appear in this volume

Development of assessment tools for Lake Sevan (Armenia) by the application of remote sensing data and geographic information systems (GIS) techniques

Lake Sevan is the biggest source of water in Armenia. Its littoral zone, in addition to being a food source and a substrate for macrophytes, algae and invertebrates, provide refuge and spawning habitats for both young & old organisms especially fishes. Between 1933 and 1960s, the lake level had been lowered by 20 m below the original level by increasing the lake outflow intermittently for irrigation and electricity generation. This evidently had ecological and economical consequences on the lake ecosystem. The importance of assessing the accuracy of spatial data classifications derived from remote sensing methods and used in geographic information system (GIS) analyses has been regarded as a critical component of many projects. In this project, supervised classified QuickBird satellite imageries of both submersed macrophytes and landcover types (emersed vegetation) of the Gavaraget, Tsovazard and Masrik Regions of the study area were validated in a GIS environment. The results of these assessments were represented by error matrices presenting the overall accuracy, the user and producer accuracies in each category, as well as the kappa coefficients. For submersed macrophytes at the vegetation level, the overall accuracy ranging between 77-88% was achieved in all the investigation years. Alga blooms in the different years impacted on the accuracy of the classification. However, even through severe algal blooms user accuracies between 55% and 95% were achieved. On the other hand, at the growth type level, the overall accuracy was as high as over 70% and as low as below 49%. For emersed vegetation types, predominantly high overall accuracies of more than 70% were obtained in 2 of the investigation years. Above all, in 2008, only slight overall accuracy could be obtained. For reeds areas, high user accuracies of more than 78% could be obtained, while for shrubs, trees, no vegetation and grasses in the different years, very different classification accuracies were attained. Two habitat suitability models (one for fishes and one for birds) were built in a GIS environment in this project. While the Crucian Carp (Carassius auratus Gibelio Bloch) was chosen as lead species for the fish habitat, the Common Coot (Fulica atra) and the Great Crested Grebe (Podiceps cristatus) were chosen for the bird habitat models based on expert knowledge on Lake Sevan. Five fish habitat suitability classes were assigned in the model. There was a similar trend in the fish habitat areas in all the landscapes in Gavaraget, Tsovazard and Masrik regions. The habitat areas increased in 2007 and decreased in 2008. The increases in all the regions were the same (around 43%) while the highest reduction occurred in Gavaraget (47%) followed by Masrik (38%) and Tsovazard (25%) respectively. Apart from the reductions in habitat areas in 2008, there were severe decreases in the quality of the habitat areas in all the regions of interests. The increases and decreases were as a result of interannual fluctuations due to water level fluctuations and algal blooms of Lake Sevan. Also, for the bird habitat model, five classes were assigned. Tsovazard and Masrik had a similar trend in habitat areas with an initial increase in 2007 followed by a decrease in 2008. However, Gavaraget had reductions in 2007 and 2008. Again, in addition to the severe reductions in the habitat areas in 2008, there were severe decreases in the quality of the habitat areas in all the regions of interests. The changes in emersed macrophyte vegetations and the lake water level fluctuations effected the different changes in the bird habitat areas.Der Sevansee stellt die größte Süßwasserressource Armeniens. Seine Litoralzone bietet neben Nahrungsressourcen und Substraten für Algen und Invertebraten auch Schutz- und Laichgebiete für juvenile und adulte Tierarten vor allem Fische. Zwischen 1933 und 1960 wurde der Seespiegel zur Bewässerung und Energiegewinnung um fast 20 m abgesenkt, mit drastischen Konsequenzen für das gesamte Seeökosystem. Die Validierung der Klassifikationsgüte ist ein zentraler Bestandteil in Fernerkundungsprojekten. Innerhalb des vorliegenden Projektes wurden mittels überwachter Klassifikation von Quickbird Satellitenbildern ermittelte Bedeckungen von submersen und emersen Vegetationsstrukturen in den Untersuchungsgebieten Gavaraget, Tsovazard und Masrik der Litoralzone des Sevansees in einer GIS-Umgebung validiert. Die Ergebnisse wruden in einer Fehlermatrix präsentiert, welche Werte zur Gesamtgenauigkeit sowie zur Nutzer- und Erzeugergenauigkeit sowie Kappa-Koeffizienten zur Beurteilung der Zufallswahrscheinlichkeit des Ergebnisses beinhaltet. Die für submerse Makrophyten für die verschiedenen Untersuchungsjahre ermittelten Gesamtgenauigkeit liegen mit 77-88% auf einem hohen Güteniveau. Algenblüten führten in einzelnen Jahren in bestimmten Gebieten zu Beeinträchtigungen der Klassifikationsgüte. Noch stärker durch Algenblüten beeinträchtigt wurde die Klassifikationsgüte in einzelnen Jahren auf dem Niveau der Wuchstypen, wo die Gesamtgenauigkeiten zwischen 55% und 95% lagen. Auf Artniveau wurden hingegen teilweise recht hohe Gesamtgenauigkeiten von über 70% erzielt, aber auch niedrige von unter 49%. Für emerse Vegetationstypen wurden ebenfalls überwiegend hohe Gesamtgenauigkeiten von über 70% erzielt, wobei in den 2 Untersuchungsjahren die Werte allerdings stark schwanken. Vor allem im Jahr 2008 konnten in den meisten Untersuchungsgebieten nur geringe Gesamtgenauigkeiten erzielt werden. Vor allem für Schilfbestände Flächen konnten hohe Nutzergenauigkeiten von über 78% erzielt werden, während für Büsche, Bäume, unbewachsene Flächen und Grasland in den verschiedenen Jahren sehr unterschiedliche Klassifikationsgüten erreicht wurden. Habitatmodelle ermöglichen die Beurteilung der Habitateignung für eine bestimmte Tierart innerhalb eines Untersuchungsgebietes. Zwei Habitatmodelle, eines für Fische und eines für Wasservögel, wurden innerhalb des Projektes in einer GIS Umgebung entwickelt. Während die Karausche (Carassius auratus Gibelio Bloch) als Leitart für Fischhabitate gewählt wurde, wurden die Bläßralle (Fulica atra) und der Haubentaucher (Podiceps cristatus) für die Vogelhabitatmodelle gewählt, basierend auf Expertenwissen lokaler Experten am Sevansee. Für die Beurteilung der Flachwasserzone als Fischhabitat wurden innerhalb des Modells 5 Eignungsklassen berechnet. In allen Untersuchungsgebieten ergab sich über die Untersuchungsjahre ein ähnlicher Trend. Die Habitatflächen stiegen von 2006 nach 2007 stark an und reduzierten sich wieder nach 2008. Während die Habitatzuwächse 2007 in allen Untersuchungsgebieten in etwa gleich bei ca. 43% lagen, waren die Verluste jeweils recht unterschiedlich, in Gavaraget bei 47%, in Masrik bei 38% und in Tsovasard bei 25%. Unabhängig von den Flächenverlusten der Habitate nahm auch die Habitatqualität stark ab. Die Schwankungen können sowohl auf den Rückgang der Makrophyten, als auch auf den Anstieg des Wasserspiegels zurückgeführt werden. Auch für die Vogelhabitatmodelle wurden fünf Eignungsklassen gebildet. Für die Untersuchungsgebiete Tsovasard und Masrik ergaben sich zeitlich ähnliche Trends mit Habitatflächenzunahmen zwischen 2006 und 2007 und Abnahmen zwischen 2007 und 2008, wobei die Ausmaße in beiden Untersuchungsgebieten sehr unterschiedlich waren. In Gavaraget ergab sich jedoch eine stetiger Abnahme der Habitatflächen über die Untersuchungsjahre. Wie auch bei den Fischhabitaten ergab sich für 2008 auch eine starke Abnahme der Habitatqualität. Da die ökonomische Bedeutung der Karausche für den Sevansee sehr hoch ist, leistet die GIS basierte Habitateignungsanalyse eine wichtigen Beitrag zur Entscheidungsunterstützung bei Maßnahmenplanungen und Erfolgskontrolle. Zusätzlich kann die Karausche auch als Indikator für die ökologische Funktionsfähigkeit der submersen Makrophytenstrukturen dienen. Auch die in allen Untersuchungsgebieten vorkommenden Bläßrallen und Haubentaucher können als Leitarten für die Funktionsfähigkeit der emersen Vegetationsstrukturen dienen

EVALUATING SATELLITE DERIVED BATHYMETRY IN REGARD TO TOTAL PROPAGATED UNCERTAINTY, MULTI-TEMPORAL CHANGE DETECTION, AND MULTIPLE NON-LINEAR ESTIMATION

Acoustic and electromagnetic hydrographic surveys produce highly-accurate bathymetric data that can be used to update and improve current nautical charts. For shallow-water surveys (i.e., less than 50m depths), this includes the use of single-beam echo-sounders (SBES), multi-beam echo-sounders (MBES), and airborne lidar bathymetry (ALB). However, these types of hydrographic surveys are time-consuming and require considerable financial and operational resources to conduct. As a result, some maritime regions are seldom surveyed due to their remote location and challenging logistics. Satellite-derived bathymetry (SDB) provides a means to supplement traditional acoustic hydrographic surveys. In particular, Landsat 8 imagery: 1) provides complete coverage of the Earth’s surface every 16 days, 2) has an improved dynamic range (12-bits), and 3) is freely-available from the US Geological Survey. While the 30 m spatial resolution does not match MBES, ALB, or SBES coverage, SDB based on Landsat 8 can be regarded as a type of “reconnaissance survey” that can be used to identify potential hazards to navigation in areas that are seldom surveyed. It is also a useful means to monitor change detection in dynamic regions. This study focused on developing improved image-processing techniques and time-series analysis for SDB from Landsat 8 imagery for three different applications: 1. An improved means to estimate total propagated uncertainty (TPU), mainly the vertical component, for single-image SDB; 2. Identifying the location and movement of dynamic shallow areas in river entrances based on multiple-temporal Landsat 8 imagery; 3. Using a multiple, nonlinear SDB approach to enhance depth estimations and enable bottom discrimination. An improved TPU estimation was achieved based on the two most common optimization approaches (Dierssen et al., 2003 and Stumpf et al., 2003). Various single-image SDB band-ratio outcomes and associated uncertainties were compared against ground truth (i.e., recent Lidar surveys). Several parameters were tested, including various types of filters, kernel sizes, number of control points and their coverage, and recent vs. outdated control points. Based on the study results for two study sites (Cape Ann, MA and Ft Myers, FL), similar performance was observed for both the Stumpf and the Dierssen models. Validation was performed by comparing estimated depths and uncertainties to observed ALB data. The best performing configuration was achieved using low-pass filter (kernel size 3x3) with ALB control points that were distributed over the entire study site. A change detection process using image processing was developed to identify the location and movement of dynamic shallow areas in riverine environments. Yukon River (Alaska) and Amazon River (Brazil) entrances were evaluated as study sites using multiple satellite imagery. A time-series analysis was used to identify probable shallow areas with no usable control points. By using an SDB ratio model with image processing techniques that includes feature extraction and a well-defined topological feature to describe the shoal feature, it is possible to create a time-series of the shoal’s motion, and predict its future location. A further benefit of this approach is that vertical referencing of the SDB ratio model to chart datum is not required. In order to enhance the capabilities of the SDB approach to estimate depth in non-uniform conditions, Dierssen’s band ration SDB algorithm was transformed into a full non-linear SDB model. The model was evaluated in the Simeonof Island, AK, using Lidar control points from a previous NOAA ALB survey. Linear and non-linear SDB models were compared using the ALB survey for performance evaluation. The multi-nonlinear SDB model provides an enhanced performance compared to the more traditional linear SDB method. This is most noticeable in the very shallow waters (0-2 m), where a linear model does not provide a good correlation to the control points. In deep-waters close to the extinction depth, the multi-nonlinear SDB method is also able to better detect bottom features than the linear SDB method. By recognizing the water column contributions to the SDB solution, it is possible to achieve a more accurate estimate of the bathymetry in remote areas

Monitoring, Modelling and Management of Water Quality

Different types of pressures, such as nutrients, micropollutants, microbes, nanoparticles, microplastics, or antibiotic-resistant genes, endanger the quality of water bodies. Evidence-based pollution control needs to be built on the three basic elements of water governance: Monitoring, modeling, and management. Monitoring sets the empirical basis by providing space- and time-dependent information on substance concentrations and loads, as well as driving boundary conditions for assessing water quality trends, water quality statuses, and providing necessary information for the calibration and validation of models. Modeling needs proper system understanding and helps to derive information for times and locations where no monitoring is done or possible. Possible applications are risk assessments for exceedance of quality standards, assessment of regionalized relevance of sources and pathways of pollution, effectiveness of measures, bundles of measures or policies, and assessment of future developments as scenarios or forecasts. Management relies on this information and translates it in a socioeconomic context into specific plans for implementation. Evaluation of success of management plans again includes well-defined monitoring strategies. This book provides an important overview in this context

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification