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In den letzten Jahren wurden relativ komplexe Erosionsmodelle entwickelt, deren Teilprozesse immer mehr auf physikalisch begründeten Ansätzen beruhen. Damit verbunden ist eine höhere Anzahl aktueller Eingangsparameter, deren Bestimmung im Feld arbeits- und kostenaufwendig ist. Zudem werden die Parameter punktuell, also an bestimmten Stellen und nicht flächenhaft wie bei der Fernerkundung, erfasst. Im Rahmen dieser Arbeit wird gezeigt, wie Satellitendaten als relativ kostengünstige Ergänzung oder Alternative zur konventionellen Parametererhebung genutzt werden können. Dazu werden beispielhaft der Blattflächenindex (LAI) und der Bedeckungsgrad für das physikalisch begründete Erosionsmodell EROSION 3D abgeleitet. Im Mittelpunkt des Interesses steht dabei das Aufzeigen von existierenden Methoden, die die Basis für eine operationelle Bereitstellung solcher Größen nicht nur für Erosions- sondern allgemein für Prozessmodelle darstellen. Als Untersuchungsgebiet dient das primär landwirtschaftlich genutzte Einzugsgebiet des Mehltheuer Baches, das sich im Sächsischen Lößgefilde befindet und für das Simulationsrechnungen mit konventionell erhobenen Eingangsparametern für 29 Niederschlagsereignisse im Jahr 1999 vorliegen [MICHAEL et al. 2000]. Die Fernerkundungsdatengrundlage bilden Landsat-5-TM-Daten vom 13.03.1999, 30.04.1999 und 19.07.1999. Da die Vegetationsparameter für alle Niederschlagsereignisse vorliegen sollen, werden sie basierend auf der Entwicklung des LAI zeitlich interpoliert. Dazu erfolgt zunächst die Ableitung des LAI für alle vorhandenen Fruchtarten nach den semi-empirischen Modellen von CLEVERS [1986] und BARET & GUYOT [1991] mit aus der Literatur entnommenen Koeffizienten. Des Weiteren wird eine Methode untersucht, nach der die Koeffizienten für das Clevers-Modell aus den TM-Daten und einem vereinfachten Wachstumsmodell bestimmt werden. Der Bedeckungsgrad wird nach ROSS [1981] aus dem LAI ermittelt. Die zeitliche Interpolation des LAI wird durch die schlagbezogene Anpassung eines vereinfachten Wachstumsmodells umgesetzt, das dem hydrologischen Modell SWIM [KRYSANOVA et al. 1999] entstammt und in das durchschnittliche Tagestemperaturen eingehen. Mit den genannten Methoden bleiben abgestorbene Pflanzenteile unberücksichtigt. Im Vergleich zur konventionellen terrestrischen Parametererhebung ermöglichen sie eine differenziertere Abbildung räumlicher Variabilitäten und des zeitlichen Verlaufes der Vegetationsparameter. Die Simulationsrechnungen werden sowohl mit den direkten Bedeckungsgraden aus den TM-Daten (pixelbezogen) als auch mit den zeitlich interpolierten Bedeckungsgraden für alle Ereignisse (schlagbezogen) durchgeführt. Bei beiden Vorgehensweisen wird im Vergleich zur bisherigen Abschätzung eine Verbesserung der räumlichen Verteilung der Parameter und somit eine räumliche Umverteilung von Erosions- und Depositionsflächen erreicht. Für die im Untersuchungsgebiet vorliegende räumliche Heterogenität (z. B. Schlaggröße) bieten Landsat-TM-Daten eine ausreichend genaue räumliche Auflösung. Damit wird nachgewiesen, dass die satellitengestützte Fernerkundung im Rahmen dieser Untersuchungen sinnvoll einsetzbar ist. Für eine operationelle Bereitstellung der Parameter mit einem vertretbaren Aufwand ist es erforderlich, die Methoden weiter zu validieren und möglichst weitestgehend zu automatisieren.
Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure.
Within a research project about future sustainable water management options in the Elbe River basin, quasi-natural discharge scenarios had to be provided. The semi-distributed eco-hydrological model SWIM was utilised for this task. According to scenario simulations driven by the stochastical climate model STAR, the region would get distinctly drier. However, this thesis focuses on the challenge of meeting the requirement of high model fidelity even for smaller sub-basins. Usually, the quality of the simulations is lower at inner points than at the outlet. Four research paper chapters and the discussion chapter deal with the reasons for local model deviations and the problem of optimal spatial calibration. Besides other assessments, the Markov Chain Monte Carlo method is applied to show whether evapotranspiration or precipitation should be corrected to minimise runoff deviations, principal component analysis is used in an unusual way to evaluate local precipitation alterations by land cover changes, and remotely sensed surface temperatures allow for an independent view on the evapotranspiration landscape. The overall insight is that spatially explicit hydrological modelling of such a large river basin requires a lot of local knowledge. It probably needs more time to obtain such knowledge as is usually provided for hydrological modelling studies.
Remote sensing technology, such as airborne, mobile, or terrestrial laser scanning, and photogrammetric techniques, are fundamental approaches for efficient, automatic creation of digital representations of spatial environments. For example, they allow us to generate 3D point clouds of landscapes, cities, infrastructure networks, and sites. As essential and universal category of geodata, 3D point clouds are used and processed by a growing number of applications, services, and systems such as in the domains of urban planning, landscape architecture, environmental monitoring, disaster management, virtual geographic environments as well as for spatial analysis and simulation.
While the acquisition processes for 3D point clouds become more and more reliable and widely-used, applications and systems are faced with more and more 3D point cloud data. In addition, 3D point clouds, by their very nature, are raw data, i.e., they do not contain any structural or semantics information. Many processing strategies common to GIS such as deriving polygon-based 3D models generally do not scale for billions of points. GIS typically reduce data density and precision of 3D point clouds to cope with the sheer amount of data, but that results in a significant loss of valuable information at the same time.
This thesis proposes concepts and techniques designed to efficiently store and process massive 3D point clouds. To this end, object-class segmentation approaches are presented to attribute semantics to 3D point clouds, used, for example, to identify building, vegetation, and ground structures and, thus, to enable processing, analyzing, and visualizing 3D point clouds in a more effective and efficient way. Similarly, change detection and updating strategies for 3D point clouds are introduced that allow for reducing storage requirements and incrementally updating 3D point cloud databases. In addition, this thesis presents out-of-core, real-time rendering techniques used to interactively explore 3D point clouds and related analysis results. All techniques have been implemented based on specialized spatial data structures, out-of-core algorithms, and GPU-based processing schemas to cope with massive 3D point clouds having billions of points.
All proposed techniques have been evaluated and demonstrated their applicability to the field of geospatial applications and systems, in particular for tasks such as classification, processing, and visualization. Case studies for 3D point clouds of entire cities with up to 80 billion points show that the presented approaches open up new ways to manage and apply large-scale, dense, and time-variant 3D point clouds as required by a rapidly growing number of applications and systems.
More than a billion people rely on water from rivers sourced in High Mountain Asia (HMA), a significant portion of which is derived from snow and glacier melt. Rural communities are heavily dependent on the consistency of runoff, and are highly vulnerable to shifts in their local environment brought on by climate change. Despite this dependence, the impacts of climate change in HMA remain poorly constrained due to poor process understanding, complex terrain, and insufficiently dense in-situ measurements.
HMA's glaciers contain more frozen water than any region outside of the poles. Their extensive retreat is a highly visible and much studied marker of regional and global climate change. However, in many catchments, snow and snowmelt represent a much larger fraction of the yearly water budget than glacial meltwaters. Despite their importance, climate-related changes in HMA's snow resources have not been well studied.
Changes in the volume and distribution of snowpack have complex and extensive impacts on both local and global climates. Eurasian snow cover has been shown to impact the strength and direction of the Indian Summer Monsoon -- which is responsible for much of the precipitation over the Indian Subcontinent -- by modulating earth-surface heating. Shifts in the timing of snowmelt have been shown to limit the productivity of major rangelands, reduce streamflow, modify sediment transport, and impact the spread of vector-borne diseases. However, a large-scale regional study of climate impacts on snow resources had yet to be undertaken.
Passive Microwave (PM) remote sensing is a well-established empirical method of studying snow resources over large areas. Since 1987, there have been consistent daily global PM measurements which can be used to derive an estimate of snow depth, and hence snow-water equivalent (SWE) -- the amount of water stored in snowpack. The SWE estimation algorithms were originally developed for flat and even terrain -- such as the Russian and Canadian Arctic -- and have rarely been used in complex terrain such as HMA.
This dissertation first examines factors present in HMA that could impact the reliability of SWE estimates. Forest cover, absolute snow depth, long-term average wind speeds, and hillslope angle were found to be the strongest controls on SWE measurement reliability. While forest density and snow depth are factors accounted for in modern SWE retrieval algorithms, wind speed and hillslope angle are not. Despite uncertainty in absolute SWE measurements and differences in the magnitude of SWE retrievals between sensors, single-instrument SWE time series were found to be internally consistent and suitable for trend analysis.
Building on this finding, this dissertation tracks changes in SWE across HMA using a statistical decomposition technique. An aggregate decrease in SWE was found (10.6 mm/yr), despite large spatial and seasonal heterogeneities. Winter SWE increased in almost half of HMA, despite general negative trends throughout the rest of the year. The elevation distribution of these negative trends indicates that while changes in SWE have likely impacted glaciers in the region, climate change impacts on these two pieces of the cryosphere are somewhat distinct.
Following the discussion of relative changes in SWE, this dissertation explores changes in the timing of the snowmelt season in HMA using a newly developed algorithm. The algorithm is shown to accurately track the onset and end of the snowmelt season (70% within 5 days of a control dataset, 89% within 10). Using a 29-year time series, changes in the onset, end, and duration of snowmelt are examined. While nearly the entirety of HMA has experienced an earlier end to the snowmelt season, large regions of HMA have seen a later start to the snowmelt season. Snowmelt periods have also decreased in almost all of HMA, indicating that the snowmelt season is generally shortening and ending earlier across HMA.
By examining shifts in both the spatio-temporal distribution of SWE and the timing of the snowmelt season across HMA, we provide a detailed accounting of changes in HMA's snow resources. The overall trend in HMA is towards less SWE storage and a shorter snowmelt season. However, long-term and regional trends conceal distinct seasonal, temporal, and spatial heterogeneity, indicating that changes in snow resources are strongly controlled by local climate and topography, and that inter-annual variability plays a significant role in HMA's snow regime.
The continuously increasing demand for rare earth elements in technical components of modern technologies, brings the detection of new deposits closer into the focus of global exploration. One promising method to globally map important deposits might be remote sensing, since it has been used for a wide range of mineral mapping in the past. This doctoral thesis investigates the capacity of hyperspectral remote sensing for the detection of rare earth element deposits. The definition and the realization of a fundamental database on the spectral characteristics of rare earth oxides, rare earth metals and rare earth element bearing materials formed the basis of this thesis. To investigate these characteristics in the field, hyperspectral images of four outcrops in Fen Complex, Norway, were collected in the near-field. A new methodology (named REEMAP) was developed to delineate rare earth element enriched zones. The main steps of REEMAP are: 1) multitemporal weighted averaging of multiple images covering the sample area; 2) sharpening the rare earth related signals using a Gaussian high pass deconvolution technique that is calibrated on the standard deviation of a Gaussian-bell shaped curve that represents by the full width of half maxima of the target absorption band; 3) mathematical modeling of the target absorption band and highlighting of rare earth elements. REEMAP was further adapted to different hyperspectral sensors (EO-1 Hyperion and EnMAP) and a new test site (Lofdal, Namibia). Additionally, the hyperspectral signatures of associated minerals were investigated to serve as proxy for the host rocks. Finally, the capacity and limitations of spectroscopic rare earth element detection approaches in general and of the REEMAP approach specifically were investigated and discussed. One result of this doctoral thesis is that eight rare earth oxides show robust absorption bands and, therefore, can be used for hyperspectral detection methods. Additionally, the spectral signatures of iron oxides, iron-bearing sulfates, calcite and kaolinite can be used to detect metasomatic alteration zones and highlight the ore zone. One of the key results of this doctoral work is the developed REEMAP approach, which can be applied from near-field to space. The REEMAP approach enables rare earth element mapping especially for noisy images. Limiting factors are a low signal to noise ratio, a reduced spectral resolution, overlaying materials, atmospheric absorption residuals and non-optimal illumination conditions. Another key result of this doctoral thesis is the finding that the future hyperspectral EnMAP satellite (with its currently published specifications, June 2015) will be theoretically capable to detect absorption bands of erbium, dysprosium, holmium, neodymium and europium, thulium and samarium. This thesis presents a new methodology REEMAP that enables a spatially wide and rapid hyperspectral detection of rare earth elements in order to meet the demand for fast, extensive and efficient rare earth exploration (from near-field to space).
Gangschwärme nehmen eine bedeutende Stellung im Verständnis zur kontinentalen Fragmentierung ein. Einerseits markieren sie das Paläo-Spannungsfeld und helfen bei der Rekonstruktion der strukturellen Entwicklung der gedehnten Lithosphäre, andererseits gibt ihre petrologische Beschaffenheit Aufschluß über die Entstehung des Magmas, Aufstieg und Platznahme und schließlich erlaubt ihre Altersbestimmung die Rekonstruktion einer chronologischen Reihenfolge magmatischer und struktureller Ereignisse. Das Arbeitsgebiet im namibianischen Henties Bay-Outjo Dike swarm (HOD) war zur Zeit der Unterkreide einem Rifting mit intensiver Platznahme von überwiegend mafischen Gängen unterworfen. Geochemische Signaturen weisen die Gänge als erodierte Förderkanäle der Etendeka Plateaubasalte aus. Durch den Einsatz von hochauflösenden Aeromagnetik- und Satellitendaten war es möglich, die Geometrie des Gangschwarmes erstmals detailliert synoptisch zu erfassen. Viele zu den Schichten des Grundgebirges foliationsparallel verlaufende magnetische Anomalien können unaufgeschlossenen kretazischen Intrusionen zugeordnet werden. Bei der nach Norden propagierenden Südatlantiköffnung spielte die unterschiedliche strukturelle Vorzeichnung durch die neoproterozoischen Faltengürtel sowie Lithologie und Spannungsfeld des Angola Kratons eine bedeutende Rolle. Im küstennahen zentralen Bereich war dank der Vorzeichnung des Nordost streichenden Damara-Faltengürtels ein Rifting in Nordwest-Südost-Richtung dominierend, bis das Angola Kraton ein weiteres Fortscheiten nach Nordosten hemmte und die Vorzeichnung des Nordwest streichenden Kaoko-Faltengürtels an der Westgrenze den weiteren Riftverlauf und die letztendlich erfolgreiche Öffnung des Südatlantiks bestimmte. Aus diesem Grund kann das Gebiet des HOD als ein failed rift betrachtet werden. Die Entwicklung des Spannungsfeldes im HOD kann folgendermaßen skizziert werden: 1. Platznahme von Gängen bei gleichzeitig hoher Dehnungsrate und hohem Magmenfluß. 2. Platznahme von Zentralvulkanen entlang reaktivierter paläozoischer Lineamente bei Abnahme der Dehnungsrate und fortbestehendem hohen Magmenfluß. 3. Abnahme/Versiegen des Magmenflusses und neotektonische Bewegungen führen zur Bildung von Halbgräben.
Arctic warming has implications for the functioning of terrestrial Arctic ecosystems, global climate and socioeconomic systems of northern communities. A research gap exists in high spatial resolution monitoring and understanding of the seasonality of permafrost degradation, spring snowmelt and vegetation phenology. This thesis explores the diversity and utility of dense TerraSAR-X (TSX) X-Band time series for monitoring ice-rich riverbank erosion, snowmelt, and phenology of Arctic vegetation at long-term study sites in the central Lena Delta, Russia and on Qikiqtaruk (Herschel Island), Canada. In the thesis the following three research questions are addressed:
• Is TSX time series capable of monitoring the dynamics of rapid permafrost degradation in ice-rich permafrost on an intra-seasonal scale and can these datasets in combination with climate data identify the climatic drivers of permafrost degradation?
• Can multi-pass and multi-polarized TSX time series adequately monitor seasonal snow cover and snowmelt in small Arctic catchments and how does it perform compared to optical satellite data and field-based measurements?
• Do TSX time series reflect the phenology of Arctic vegetation and how does the recorded signal compare to in-situ greenness data from RGB time-lapse camera data and vegetation height from field surveys?
To answer the research questions three years of TSX backscatter data from 2013 to 2015 for the Lena Delta study site and from 2015 to 2017 for the Qikiqtaruk study site were used in quantitative and qualitative analysis complimentary with optical satellite data and in-situ time-lapse imagery.
The dynamics of intra-seasonal ice-rich riverbank erosion in the central Lena Delta, Russia were quantified using TSX backscatter data at 2.4 m spatial resolution in HH polarization and validated with 0.5 m spatial resolution optical satellite data and field-based time-lapse camera data. Cliff top lines were automatically extracted from TSX intensity images using threshold-based segmentation and vectorization and combined in a geoinformation system with manually digitized cliff top lines from the optical satellite data and rates of erosion extracted from time-lapse cameras. The results suggest that the cliff top eroded at a constant rate throughout the entire erosional season. Linear mixed models confirmed that erosion was coupled with air temperature and precipitation at an annual scale, seasonal fluctuations did not influence 22-day erosion rates. The results highlight the potential of HH polarized X-Band backscatter data for high temporal resolution monitoring of rapid permafrost degradation.
The distinct signature of wet snow in backscatter intensity images of TSX data was exploited to generate wet snow cover extent (SCE) maps on Qikiqtaruk at high temporal resolution. TSX SCE showed high similarity to Landsat 8-derived SCE when using cross-polarized VH data. Fractional snow cover (FSC) time series were extracted from TSX and optical SCE and compared to FSC estimations from in-situ time-lapse imagery. The TSX products showed strong agreement with the in-situ data and significantly improved the temporal resolution compared to the Landsat 8 time series. The final combined FSC time series revealed two topography-dependent snowmelt patterns that corresponded to in-situ measurements. Additionally TSX was able to detect snow patches longer in the season than Landsat 8, underlining the advantage of TSX for detection of old snow. The TSX-derived snow information provided valuable insights into snowmelt dynamics on Qikiqtaruk previously not available.
The sensitivity of TSX to vegetation structure associated with phenological changes was explored on Qikiqtaruk. Backscatter and coherence time series were compared to greenness data extracted from in-situ digital time-lapse cameras and detailed vegetation parameters on 30 areas of interest. Supporting previous results, vegetation height corresponded to backscatter intensity in co-polarized HH/VV at an incidence angle of 31°. The dry, tall shrub dominated ecological class showed increasing backscatter with increasing greenness when using the cross polarized VH/HH channel at 32° incidence angle. This is likely driven by volume scattering of emerging and expanding leaves. Ecological classes with more prostrate vegetation and higher bare ground contributions showed decreasing backscatter trends over the growing season in the co-polarized VV/HH channels likely a result of surface drying instead of a vegetation structure signal. The results from shrub dominated areas are promising and provide a complementary data source for high temporal monitoring of vegetation phenology.
Overall this thesis demonstrates that dense time series of TSX with optical remote sensing and in-situ time-lapse data are complementary and can be used to monitor rapid and seasonal processes in Arctic landscapes at high spatial and temporal resolution.
Gegenstand dieser Arbeit ist die Konzeption, Entwicklung und exemplarische Implementierung eines generischen Verfahrens zur Erfassung, Verarbeitung, Auswertung und kartographischen Visualisierung urbaner Strukturen im altweltlichen Trockengürtel mittels hochauflösender operationeller Fernerkundungsdaten. Das Verfahren wird am Beispiel der jemenitischen Hauptstadt Sanaa einer Vertreterin des Typus der Orientalischen Stadt angewandt und evaluiert. Das zu entwickelnde Verfahren soll auf Standardverfahren und Systemen der raumbezogenen Informationsverarbeitung basieren und in seinen wesentlichen Prozessschritten automatisiert werden können. Daten von hochauflösenden operationellen Fernerkundungssystemen (wie z.B. QuickBird, Ikonos u. a.) erlauben die Erkennung und Kartierung urbaner Objekte, wie Gebäude, Straßen und sogar Autos. Die mit ihnen erstellten Karten und den daraus gewonnenen Informationen können zur Erfassung von Urbanisierungsprozessen (Stadt- und Bevölkerungswachstum) herangezogen werden. Sie werden auch zur Generierung von 3D-Stadtmodellen genutzt. Diese dienen z.B. der Visualisierung für touristische Anwendungen, für die Stadtplanung, für Lärmanalysen oder für die Standortplanung von Mobilfunkantennen. Bei dem in dieser Arbeit erzeugten 3D-Visualisierung wurden jedoch keine Gebäudedetails erfasst. Entscheidend war vielmehr die Wiedergabe der Siedlungsstruktur, die im Vorhandensein und in der Anordnung der Gebäude liegt. In dieser Arbeit wurden Daten des Satellitensensors Quickbird von 2005 verwendet. Sie zeigen einen Ausschnitt der Stadt Sanaa in Jemen. Die Fernerkundungsdaten wurden durch andere Daten, u.a. auch Geländedaten, ergänzt und verifiziert. Das ausgearbeitete Verfahren besteht aus der Klassifikation der Satellitenbild-aufnahme, die u.a. pixelbezogen und für jede Klasse einzeln (pixelbezogene Klassifikation auf Klassenebene) durchgeführt wurde. Zusätzlich fand eine visuelle Interpretation der Satellitenbildaufnahme statt, bei der einzelne Flächen und die Straßen digitalisiert und die Objekte mit Symbolen gekennzeichnet wurden. Die aus beiden Verfahren erstellten Stadtkarten wurden zu einer fusioniert. Durch die Kombination der Ergebnisse werden die Vorteile beider Karten in einer vereint und ihre jeweiligen Schwächen beseitigt bzw. minimiert. Die digitale Erfassung der Konturlinien auf der Orthophotomap von Sanaa erlaubte die Erstellung eines Digitalen Geländemodells, das der dreidimensionalen Darstellung des Altstadtbereichs von Sanaa diente. Die 3D-Visualisierung wurde sowohl von den pixelbezogenen Klassifikationsergebnissen auf Klassenebene als auch von der digitalen Erfassung der Objekte erstellt. Die Ergebnisse beider Visualisierungen wurden im Anschluss in einer Stadtkarte vereint. Bei allen Klassifikationsverfahren wurden die asphaltierten Straßen, die Vegetation und einzeln stehende Gebäude sehr gut erfasst. Die Klassifikation der Altstadt gestaltete sich aufgrund der dort für die Klassifikation herrschenden ungünstigen Bedingungen am problematischsten. Die insgesamt besten Ergebnisse mit den höchsten Genauigkeitswerten wurden bei der pixelbezogenen Klassifikation auf Klassenebene erzielt. Dadurch, dass jede Klasse einzeln klassifiziert wurde, konnte die zu einer Klasse gehörende Fläche besser erfasst und nachbearbeitet werden. Die Datenmenge wurde reduziert, die Bearbeitungszeit somit kürzer und die Speicherkapazität geringer. Die Auswertung bzw. visuelle Validierung der pixel-bezogenen Klassifikationsergebnisse auf Klassenebene mit dem Originalsatelliten-bild gestaltete sich einfacher und erfolgte genauer als bei den anderen durch-geführten Klassifikationsverfahren. Außerdem war es durch die alleinige Erfassung der Klasse Gebäude möglich, eine 3D-Visualisierung zu erzeugen. Bei einem Vergleich der erstellten Stadtkarten ergibt sich, dass die durch die visuelle Interpretation erstellte Karte mehr Informationen enthält. Die von den pixelbezogenen Klassifikationsergebnissen auf Klassenebene erstellte Karte ist aber weniger arbeits- und zeitaufwendig zu erzeugen. Zudem arbeitet sie die Struktur einer orientalischen Stadt mit den wesentlichen Merkmalen besser heraus. Durch die auf Basis der 2D-Stadtkarten erstellte 3D-Visualisierung wird ein anderer räumlicher Eindruck vermittelt und bestimmte Elemente einer orientalischen Stadt deutlich gemacht. Dazu zählen die sich in der Altstadt befindenden Sackgassen und die ehemalige Stadtmauer. Auch die für Sanaa typischen Hochhäuser werden in der 3D-Visualisierung erkannt. Insgesamt wurde in der Arbeit ein generisches Verfahren entwickelt, dass mit geringen Modifikationen auch auf andere städtische Räume des Typus orientalische Stadt angewendet werden kann.
Hartmut Asche prägte über ein Vierteljahrhundert maßgeblich die Forschungsfelder der Geoinformation, Visualisierung und Kartographie. Die vorliegende Festschrift stellt eine würdige Gabe von Mitarbeiterinnen und Mitarbeitern des Institutes für Geographie der Universität Potsdam anlässlich seiner Emeritierung im März 2017 dar. International renommierte, Herrn Asches Karriere begleitende Autorinnen und Autoren, konnten für Fachbeiträge aus den Bereichen Geographie, Geoinformatik, Kartographie und Fernerkundung gewonnen werden. Es werden in fachlich hervorragender Weise Schwerpunkte umrissen, mit welchen Herr Asche sich in seiner von zahlreichen Höhepunkten geprägten wissenschaftlichen Karriere beschäftigte.
The Central Andean region is characterized by diverse climate zones with sharp transitions between them. In this work, the area of interest is the South-Central Andes in northwestern Argentina that borders with Bolivia and Chile. The focus is the observation of soil moisture and water vapour with Global Navigation Satellite System (GNSS) remote-sensing methodologies. Because of the rapid temporal and spatial variations of water vapour and moisture circulations, monitoring this part of the hydrological cycle is crucial for understanding the mechanisms that control the local climate. Moreover, GNSS-based techniques have previously shown high potential and are appropriate for further investigation. This study includes both logistic-organization effort and data analysis. As for the prior, three GNSS ground stations were installed in remote locations in northwestern Argentina to acquire observations, where there was no availability of third-party data.
The methodological development for the observation of the climate variables of soil moisture and water vapour is independent and relies on different approaches. The soil-moisture estimation with GNSS reflectometry is an approximation that has demonstrated promising results, but it has yet to be operationally employed. Thus, a more advanced algorithm that exploits more observations from multiple satellite constellations was developed using data from two pilot stations in Germany. Additionally, this algorithm was slightly modified and used in a sea-level measurement campaign. Although the objective of this application is not related to monitoring hydrological parameters, its methodology is based on the same principles and helps to evaluate the core algorithm. On the other hand, water-vapour monitoring with GNSS observations is a well-established technique that is utilized operationally. Hence, the scope of this study is conducting a meteorological analysis by examining the along-the-zenith air-moisture levels and introducing indices related to the azimuthal gradient.
The results of the experiments indicate higher-quality soil moisture observations with the new algorithm. Furthermore, the analysis using the stations in northwestern Argentina illustrates the limits of this technology because of varying soil conditions and shows future research directions. The water-vapour analysis points out the strong influence of the topography on atmospheric moisture circulation and rainfall generation. Moreover, the GNSS time series allows for the identification of seasonal signatures, and the azimuthal-gradient indices permit the detection of main circulation pathways.
The Arctic tundra, covering approx. 5.5 % of the Earth’s land surface, is one of the last ecosystems remaining closest to its untouched condition. Remote sensing is able to provide information at regular time intervals and large spatial scales on the structure and function of Arctic ecosystems. But almost all natural surfaces reveal individual anisotropic reflectance behaviors, which can be described by the bidirectional reflectance distribution function (BRDF). This effect can cause significant changes in the measured surface reflectance depending on solar illumination and sensor viewing geometries. The aim of this thesis is the hyperspectral and spectro-directional reflectance characterization of important Arctic tundra vegetation communities at representative Siberian and Alaskan tundra sites as basis for the extraction of vegetation parameters, and the normalization of BRDF effects in off-nadir and multi-temporal remote sensing data. Moreover, in preparation for the upcoming German EnMAP (Environmental Mapping and Analysis Program) satellite mission, the understanding of BRDF effects in Arctic tundra is essential for the retrieval of high quality, consistent and therefore comparable datasets. The research in this doctoral thesis is based on field spectroscopic and field spectro-goniometric investigations of representative Siberian and Alaskan measurement grids. The first objective of this thesis was the development of a lightweight, transportable, and easily managed field spectro-goniometer system which nevertheless provides reliable spectro-directional data. I developed the Manual Transportable Instrument platform for ground-based Spectro-directional observations (ManTIS). The outcome of the field spectro-radiometrical measurements at the Low Arctic study sites along important environmental gradients (regional climate, soil pH, toposequence, and soil moisture) show that the different plant communities can be distinguished by their nadir-view reflectance spectra. The results especially reveal separation possibilities between the different tundra vegetation communities in the visible (VIS) blue and red wavelength regions. Additionally, the near-infrared (NIR) shoulder and NIR reflectance plateau, despite their relatively low values due to the low structure of tundra vegetation, are still valuable information sources and can separate communities according to their biomass and vegetation structure. In general, all different tundra plant communities show: (i) low maximum NIR reflectance; (ii) a weakly or nonexistent visible green reflectance peak in the VIS spectrum; (iii) a narrow “red-edge” region between the red and NIR wavelength regions; and (iv) no distinct NIR reflectance plateau. These common nadir-view reflectance characteristics are essential for the understanding of the variability of BRDF effects in Arctic tundra. None of the analyzed tundra communities showed an even closely isotropic reflectance behavior. In general, tundra vegetation communities: (i) usually show the highest BRDF effects in the solar principal plane; (ii) usually show the reflectance maximum in the backward viewing directions, and the reflectance minimum in the nadir to forward viewing directions; (iii) usually have a higher degree of reflectance anisotropy in the VIS wavelength region than in the NIR wavelength region; and (iv) show a more bowl-shaped reflectance distribution in longer wavelength bands (>700 nm). The results of the analysis of the influence of high sun zenith angles on the reflectance anisotropy show that with increasing sun zenith angles, the reflectance anisotropy changes to azimuthally symmetrical, bowl-shaped reflectance distributions with the lowest reflectance values in the nadir view position. The spectro-directional analyses also show that remote sensing products such as the NDVI or relative absorption depth products are strongly influenced by BRDF effects, and that the anisotropic characteristics of the remote sensing products can significantly differ from the observed BRDF effects in the original reflectance data. But the results further show that the NDVI can minimize view angle effects relative to the contrary spectro-directional effects in the red and NIR bands. For the researched tundra plant communities, the overall difference of the off-nadir NDVI values compared to the nadir value increases with increasing sensor viewing angles, but on average never exceeds 10 %. In conclusion, this study shows that changes in the illumination-target-viewing geometry directly lead to an altering of the reflectance spectra of Arctic tundra communities according to their object-specific BRDFs. Since the different tundra communities show only small, but nonetheless significant differences in the surface reflectance, it is important to include spectro-directional reflectance characteristics in the algorithm development for remote sensing products.
Studies on the unsustainable use of groundwater resources are still considered incipient since it is frequently a poorly understood and managed, devalued and inadequately protected natural resource. Groundwater Recharge (GWR) is one of the most challenging elements to estimate since it can rarely be measured directly and cannot easily be derived from existing data. To overcome these limitations, many hydro(geo)logists have combined different approaches to estimate large-scale GWR, namely: remote sensing products, such as IMERG product; Water Budget Equation, also in combination with hydrological models, and; Geographic Information System (GIS), using estimation formulas. For intermediary-scale GWR estimation, there exist: Non-invasive Cosmic-Ray Neutron Sensing (CRNS); wireless networks from local soil probes; and soil hydrological models, such as HYDRUS. Accordingly, this PhD thesis aims, on the one hand, to demonstrate a GIS-based model coupling for estimating the GWR distribution on a large scale in tropical wet basins. On the other hand, it aims to use the time series from CRNS and invasive soil moisture probes to inversely calibrate the soil hydraulic properties, and based on this, estimating the intermediary-scale GWR using a soil hydrological model. For such purpose, two tropical wet basins located in a complex sedimentary aquifer in the coastal Northeast region of Brazil were selected. These are the João Pessoa Case Study Area and the Guaraíra Experimental Basin. Several satellite products in the first area were used as input to the GIS-based water budget equation model for estimating the water balance components and GWR in 2016 and 2017. In addition, the point-scale measurement and CRNS data were used in the second area to determine the soil hydraulic properties, and to estimate the GWR in the 2017-2018 and 2018-2019 hydrological years. The resulting values of GWR on large- and intermediary-scale were then compared and validated by the estimates obtained by groundwater table fluctuations. The GWR rates for IMERG- and rain-gauge-based scenarios showed similar coefficients between 68% and 89%, similar mean errors between 30% and 34%, and slightly-different bias between -13% and 11%. The results of GWR rates for soil probes and CRNS soil moisture scenarios ranged from -5.87 to -61.81 cm yr-1, which corresponds to 5% and 38% of the precipitation. The calculations of the mean GWR rates on large-scale, based on remote sensing data, and on intermediary-scale, based on CRNS data, held similar results for the Podzol soil type, namely 17.87% and 17% of the precipitation. It is then concluded that the proposed methodologies allowed for estimating realistically the GWR over the study areas, which can be a ground-breaking step towards improving the water management and decision-making in the Northeast of Brazil.
Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
In this study, an in situ application for identifying neodymium (Nd) enriched surface materials that uses multitemporal hyperspectral images is presented (HySpex sensor). Because of the narrow shape and shallow absorption depth of the neodymium absorption feature, a method was developed for enhancing and extracting the necessary information for neodymium from image spectra, even under illumination conditions that are not optimal. For this purpose, the two following approaches were developed: (1) reducing noise and analyzing changing illumination conditions by averaging multitemporal image scenes and (2) enhancing the depth of the desired absorption band by deconvolving every image spectrum with a Gaussian curve while the rest of the spectrum remains unchanged (Richardson-Lucy deconvolution). To evaluate these findings, nine field samples from the Fen complex in Norway were analyzed using handheld X-ray fluorescence devices and by conducting detailed laboratory-based geochemical rare earth element determinations. The result is a qualitative outcrop map that highlights zones that are enriched in neodymium. To reduce the influences of non-optimal illumination, particularly at the studied site, a minimum of seven single acquisitions is required. Sharpening the neodymium absorption band allows for robust mapping, even at the outer zones of enrichment. From the geochemical investigations, we found that iron oxides decrease the applicability of the method. However, iron-related absorption bands can be used as secondary indicators for sulfidic ore zones that are mainly enriched with rare earth elements. In summary, we found that hyperspectral spectroscopy is a noninvasive, fast and cost-saving method for determining neodymium at outcrop surfaces
Landslides are one of the biggest natural hazards in Georgia, a mountainous country in the Caucasus. So far, no systematic monitoring and analysis of the dynamics of landslides in Georgia has been made. Especially as landslides are triggered by extrinsic processes, the analysis of landslides together with precipitation and earthquakes is challenging. In this thesis I describe the advantages and limits of remote sensing to detect and better understand the nature of landslide in Georgia. The thesis is written in a cumulative form, composing a general introduction, three manuscripts and a summary and outlook chapter. In the present work, I measure the surface displacement due to active landslides with different interferometric synthetic aperture radar (InSAR) methods. The slow landslides (several cm per year) are well detectable with two-pass interferometry. In same time, the extremely slow landslides (several mm per year) could be detected only with time series InSAR techniques. I exemplify the success of InSAR techniques by showing hitherto unknown landslides, located in the central part of Georgia. Both, the landslide extent and displacement rate is quantified. Further, to determine a possible depth and position of potential sliding planes, inverse models were developed. Inverse modeling searches for parameters of source which can create observed displacement distribution. I also empirically estimate the volume of the investigated landslide using displacement distributions as derived from InSAR combined with morphology from an aerial photography. I adapted a volume formula for our case, and also combined available seismicity and precipitation data to analyze potential triggering factors. A governing question was: What causes landslide acceleration as observed in the InSAR data? The investigated area (central Georgia) is seismically highly active. As an additional product of the InSAR data analysis, a deformation area associated with the 7th September Mw=6.0 earthquake was found. Evidences of surface ruptures directly associated with the earthquake could not be found in the field, however, during and after the earthquake new landslides were observed. The thesis highlights that deformation from InSAR may help to map area prone landslides triggering by earthquake, potentially providing a technique that is of relevance for country wide landslide monitoring, especially as new satellite sensors will emerge in the coming years.
Extreme flooding displaces an average of 12 million people every year. Marginalized populations in low-income countries are in particular at high risk, but also industrialized countries are susceptible to displacement and its inherent societal impacts. The risk of being displaced results from a complex interaction of flood hazard, population exposed in the floodplains, and socio-economic vulnerability. Ongoing global warming changes the intensity, frequency, and duration of flood hazards, undermining existing protection measures. Meanwhile, settlements in attractive yet hazardous flood-prone areas have led to a higher degree of population exposure. Finally, the vulnerability to displacement is altered by demographic and social change, shifting economic power, urbanization, and technological development. These risk components have been investigated intensively in the context of loss of life and economic damage, however, only little is known about the risk of displacement under global change.
This thesis aims to improve our understanding of flood-induced displacement risk under global climate change and socio-economic change. This objective is tackled by addressing the following three research questions. First, by focusing on the choice of input data, how well can a global flood modeling chain reproduce flood hazards of historic events that lead to displacement? Second, what are the socio-economic characteristics that shape the vulnerability to displacement? Finally, to what degree has climate change potentially contributed to recent flood-induced displacement events?
To answer the first question, a global flood modeling chain is evaluated by comparing simulated flood extent with satellite-derived inundation information for eight major flood events. A focus is set on the sensitivity to different combinations of the underlying climate reanalysis datasets and global hydrological models which serve as an input for the global hydraulic model. An evaluation scheme of performance scores shows that simulated flood extent is mostly overestimated without the consideration of flood protection and only for a few events dependent on the choice of global hydrological models. Results are more sensitive to the underlying climate forcing, with two datasets differing substantially from a third one. In contrast, the incorporation of flood protection standards results in an underestimation of flood extent, pointing to potential deficiencies in the protection level estimates or the flood frequency distribution within the modeling chain.
Following the analysis of a physical flood hazard model, the socio-economic drivers of vulnerability to displacement are investigated in the next step. For this purpose, a satellite- based, global collection of flood footprints is linked with two disaster inventories to match societal impacts with the corresponding flood hazard. For each event the number of affected population, assets, and critical infrastructure, as well as socio-economic indicators are computed. The resulting datasets are made publicly available and contain 335 displacement events and 695 mortality/damage events. Based on this new data product, event-specific displacement vulnerabilities are determined and multiple (national) dependencies with the socio-economic predictors are derived. The results suggest that economic prosperity only partially shapes vulnerability to displacement; urbanization, infant mortality rate, the share of elderly, population density and critical infrastructure exhibit a stronger functional relationship, suggesting that higher levels of development are generally associated with lower vulnerability.
Besides examining the contextual drivers of vulnerability, the role of climate change in the context of human displacement is also being explored. An impact attribution approach is applied on the example of Cyclone Idai and associated extreme coastal flooding in Mozambique. A combination of coastal flood modeling and satellite imagery is used to construct factual and counterfactual flood events. This storyline-type attribution method allows investigating the isolated or combined effects of sea level rise and the intensification of cyclone wind speeds on coastal flooding. The results suggest that displacement risk has increased by 3.1 to 3.5% due to the total effects of climate change on coastal flooding, with the effects of increasing wind speed being the dominant factor.
In conclusion, this thesis highlights the potentials and challenges of modeling flood- induced displacement risk. While this work explores the sensitivity of global flood modeling to the choice of input data, new questions arise on how to effectively improve the reproduction of flood return periods and the representation of protection levels. It is also demonstrated that disentangling displacement vulnerabilities is feasible, with the results providing useful information for risk assessments, effective humanitarian aid, and disaster relief. The impact attribution study is a first step in assessing the effects of global warming on displacement risk, leading to new research challenges, e.g., coupling fluvial and coastal flood models or the attribution of other hazard types and displacement events. This thesis is one of the first to address flood-induced displacement risk from a global perspective. The findings motivate for further development of the global flood modeling chain to improve our understanding of displacement vulnerability and the effects of global warming.
The global carbon cycle is closely linked to Earth’s climate. In the context of continuously unchecked anthropogenic CO₂ emissions, the importance of natural CO₂ bond and carbon storage is increasing. An important biogenic mechanism of natural atmospheric CO₂ drawdown is the photosynthetic carbon fixation in plants and the subsequent longterm deposition of plant detritus in sediments.
The main objective of this thesis is to identify factors that control mobilization and transport of plant organic matter (pOM) through rivers towards sedimentation basins. I investigated this aspect in the eastern Nepalese Arun Valley. The trans-Himalayan Arun River is characterized by a strong elevation gradient (205 − 8848 m asl) that is accompanied by strong changes in ecology and climate ranging from wet tropical conditions in the Himalayan forelad to high alpine tundra on the Tibetan Plateau. Therefore, the Arun is an excellent natural laboratory, allowing the investigation of the effect of vegetation cover, climate, and topography on plant organic matter mobilization and export in tributaries along the gradient.
Based on hydrogen isotope measurements of plant waxes sampled along the Arun River and its tributaries, I first developed a model that allows for an indirect quantification of pOM contributed to the mainsetm by the Arun’s tributaries. In order to determine the role of climatic and topographic parameters of sampled tributary catchments, I looked for significant statistical relations between the amount of tributary pOM export and tributary characteristics (e.g. catchment size, plant cover, annual precipitation or runoff, topographic measures). On one hand, I demonstrated that pOMsourced from the Arun is not uniformly derived from its entire catchment area. On the other, I showed that dense vegetation is a necessary, but not sufficient, criterion for high tributary pOM export. Instead, I identified erosion and rainfall and runoff as key factors controlling pOM sourcing in the Arun Valley. This finding is supported by terrestrial cosmogenic nuclide concentrations measured on river sands along the Arun and its tributaries in order to quantify catchment wide denudation rates. Highest denudation rates corresponded well with maximum pOM mobilization and export also suggesting the link between erosion and pOM sourcing.
The second part of this thesis focusses on the applicability of stable isotope records such as plant wax n-alkanes in sediment archives as qualitative and quantitative proxy for the variability of past Indian Summer Monsoon (ISM) strength. First, I determined how ISM strength affects the hydrogen and oxygen stable isotopic composition (reported as δD and δ18O values vs. Vienna Standard Mean Ocean Water) of precipitation in the Arun Valley and if this amount effect (Dansgaard, 1964) is strong enough to be recorded in potential paleo-ISM isotope proxies. Second, I investigated if potential isotope records across the Arun catchment reflect ISM strength dependent precipitation δD values only, or if the ISM isotope signal is superimposed by winter precipitation or glacial melt. Furthermore, I tested if δD values of plant waxes in fluvial deposits reflect δD values of environmental waters in the respective catchments.
I showed that surface water δD values in the Arun Valley and precipitation δD from south of the Himalaya both changed similarly during two consecutive years (2011 & 2012) with distinct ISM rainfall amounts (~20% less in 2012). In order to evaluate the effect of other water sources (Winter-Westerly precipitation, glacial melt) and evapotranspiration in the Arun Valley, I analysed satellite remote sensing data of rainfall distribution (TRMM 3B42V7), snow cover (MODIS MOD10C1), glacial coverage (GLIMSdatabase, Global Land Ice Measurements from Space), and evapotranspiration (MODIS MOD16A2). In addition to the predominant ISM in the entire catchment I found through stable isotope analysis of surface waters indications for a considerable amount of glacial melt derived from high altitude tributaries and the Tibetan Plateau. Remotely sensed snow cover data revealed that the upper portion of the Arun also receives considerable winter precipitation, but the effect of snow melt on the Arun Valley hydrology could not be evaluated as it takes place in early summer, several months prior to our sampling campaigns. However, I infer that plant wax records and other potential stable isotope proxy archives below the snowline are well-suited for qualitative, and potentially quantitative, reconstructions of past changes of ISM strength.
Properties of Arctic aerosol in the transition between Arctic haze to summer season derived by lidar
(2023)
During the Arctic haze period, the Arctic troposphere consists of larger, yet fewer, aerosol particles than during the summer (Tunved et al., 2013; Quinn et al., 2007). Interannual variability (Graßl and Ritter, 2019; Rinke et al., 2004), as well as unknown origins (Stock et al., 2014) and properties of aerosol complicate modeling these annual aerosol cycles. This thesis investigates the modification of the microphysical properties of Arctic aerosols in the transition from Arctic haze to the summer season. Therefore, lidar measurements of Ny-Ålesund from April 2021 to the end of July 2021 are evaluated based on the aerosols’ optical properties. An overview of those properties will be provided. Furthermore, parallel radiosonde data is considered for indication of hygroscopic growth.
The annual aerosol cycle in 2021 differs from expectations based on previous studies from Tunved et al. (2013) and Quinn et al. (2007). Developments of backscatter, extinction, aerosol depolarisation, lidar ratio and color ratio show a return of the Arctic haze in May. The haze had already reduced in April, but regrew afterwards.
The average Arctic aerosol displays hygroscopic behaviour, meaning growth due to water uptake. To determine such a behaviour is generally laborious because various meteorological circumstances need to be considered. Two case studies provide further information on these possible events. In particular, a day with a rare ice cloud and with highly variable water cloud layers is observed.
Temporal variation of natural light sources such as airglow limits the ability of night light sensors to detect changes in small sources of artificial light (such as villages). This study presents a method for correcting for this effect globally, using the satellite radiance detected from regions without artificial light emissions. We developed a routine to define an approximate grid of locations worldwide that do not have regular light emission. We apply this method with a 5 degree equally spaced global grid (total of 2016 individual locations), using data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB). This code could easily be adapted for other future global sensors. The correction reduces the standard deviation of data in the Earth Observation Group monthly DNB composites by almost a factor of two. The code and datasets presented here are available under an open license by GFZ Data Services, and are implemented in the Radiance Light Trends web application.