TY - GEN A1 - Milewski, Robert A1 - Chabrillat, Sabine A1 - Bookhagen, Bodo T1 - Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 988 KW - salt pan KW - playa KW - spectral analysis KW - crust KW - saline pan cycle KW - evaporites KW - time-series mapping Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-475685 SN - 1866-8372 IS - 988 ER - TY - JOUR A1 - Milewski, Robert A1 - Chabrillat, Sabine A1 - Bookhagen, Bodo T1 - Analyses of Namibian Seasonal Salt Pan Crust Dynamics and Climatic Drivers Using Landsat 8 Time-Series and Ground Data JF - Remote Sensing N2 - Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization. KW - salt pan KW - playa KW - spectral analysis KW - crust KW - saline pan cycle KW - evaporites KW - time-series mapping Y1 - 2019 U6 - https://doi.org/10.3390/rs12030474 SN - 2072-4292 IS - 3 PB - MDPI CY - Basel ER - TY - THES A1 - Schmidt, Silke Regina T1 - Analyzing lakes in the time frequency domain T1 - Analyse von Seen in der Zeit-Frequenz-Domäne N2 - The central aim of this thesis is to demonstrate the benefits of innovative frequency-based methods to better explain the variability observed in lake ecosystems. Freshwater ecosystems may be the most threatened part of the hydrosphere. Lake ecosystems are particularly sensitive to changes in climate and land use because they integrate disturbances across their entire catchment. This makes understanding the dynamics of lake ecosystems an intriguing and important research priority. This thesis adds new findings to the baseline knowledge regarding variability in lake ecosystems. It provides a literature-based, data-driven and methodological framework for the investigation of variability and patterns in environmental parameters in the time frequency domain. Observational data often show considerable variability in the environmental parameters of lake ecosystems. This variability is mostly driven by a plethora of periodic and stochastic processes inside and outside the ecosystems. These run in parallel and may operate at vastly different time scales, ranging from seconds to decades. In measured data, all of these signals are superimposed, and dominant processes may obscure the signals of other processes, particularly when analyzing mean values over long time scales. Dominant signals are often caused by phenomena at long time scales like seasonal cycles, and most of these are well understood in the limnological literature. The variability injected by biological, chemical and physical processes operating at smaller time scales is less well understood. However, variability affects the state and health of lake ecosystems at all time scales. Besides measuring time series at sufficiently high temporal resolution, the investigation of the full spectrum of variability requires innovative methods of analysis. Analyzing observational data in the time frequency domain allows to identify variability at different time scales and facilitates their attribution to specific processes. The merit of this approach is subsequently demonstrated in three case studies. The first study uses a conceptual analysis to demonstrate the importance of time scales for the detection of ecosystem responses to climate change. These responses often occur during critical time windows in the year, may exhibit a time lag and can be driven by the exceedance of thresholds in their drivers. This can only be detected if the temporal resolution of the data is high enough. The second study applies Fast Fourier Transform spectral analysis to two decades of daily water temperature measurements to show how temporal and spatial scales of water temperature variability can serve as an indicator for mixing in a shallow, polymictic lake. The final study uses wavelet coherence as a diagnostic tool for limnology on a multivariate high-frequency data set recorded between the onset of ice cover and a cyanobacteria summer bloom in the year 2009 in a polymictic lake. Synchronicities among limnological and meteorological time series in narrow frequency bands were used to identify and disentangle prevailing limnological processes. Beyond the novel empirical findings reported in the three case studies, this thesis aims to more generally be of interest to researchers dealing with now increasingly available time series data at high temporal resolution. A set of innovative methods to attribute patterns to processes, their drivers and constraints is provided to help make more efficient use of this kind of data. N2 - See-Ökosysteme sind eine der bedrohtesten Ressourcen der Hydrosphäre. Sie reagieren besonders sensibel auf Veränderungen des Klimas und auf Einflüsse durch Landnutzung, da verschiedene Prozesse im gesamten Einzugsgebiet auf sie einwirken. Daher ist es von besonderer Dringlichkeit, die verschiedenen Prozess-Dynamiken in See-Ökosystemen besser zu verstehen. Die hier vorliegende Doktorarbeit hat zum Ziel, das bestehende Wissen bezüglich der verschiedenen einwirkenden Prozesse in See-Ökosystemen zu erweitern. Die Arbeit stellt ein Forschungsdesign zur Diskussion, das eine Literatur-basierte und auf empirischen Erhebungen beruhende Analyse von Variabilität und Mustern in großen Datensätzen verschiedener Umweltparameter im Zeit-Frequenz-Raum ermöglicht. Umweltparameter sind häufig charakterisiert durch eine hohe zeitliche Dynamik. Diese Variabilität steht im Zentrum dieser Arbeit. Sie wird durch eine Fülle an periodischen und stochastischen Prozessen innerhalb und außerhalb des Ökosystems getrieben. Diese Prozesse können gleichzeitig und auf sehr unterschiedlichen Zeitskalen, von Sekunden bis hin zu Dekaden, ablaufen. In Messdaten überlagern sich alle diese Signale, und dominante Prozesse können die Signale anderer Prozesse verschleiern, insbesondere wenn Mittelwerte über längere Zeiträume analysiert werden. Dominante Signale werden oft durch Prozesse auf längeren Zeitskalen verursacht, wie z. B. saisonale Zyklen. Diese sind im Allgemeinen in der limnologischen Literatur gut dokumentiert. See-Ökosysteme werden allerdings von Prozessen auf allen Zeitskalen beeinflusst. Insbesondere biologische, chemische und physikalische Prozesse operieren in kürzeren Zeitrahmen. Die Variabilität, die über solche Prozesse in See-Ökosysteme eingebracht wird, ist bisher weit weniger gut erforscht. Neben der Notwendigkeit, Umweltparameter in hoher zeitlicher Auflösung zu messen, erfordert die Untersuchung der kompletten Bandbreite an Variabilität innovative Analysemethoden. Die Berücksichtigung der Zeit-Frequenz-Domäne kann dabei helfen, Dynamiken auf verschiedenen Zeitskalen zu identifizieren und daraus bestimmte Prozesse abzuleiten. Diese Arbeit zeigt die Vorzüge dieser Herangehensweise anhand von drei Fallstudien auf. Die erste Studie zeigt die Bedeutung von Zeitskalen für die Erfassung von Ökosystem-Reaktionen auf klimatische Veränderungen. Diese ereignen sich oft während kritischer Zeitfenster im Jahresverlauf und können durch die Überschreitung von Schwellenwerten in den treibenden Variablen, unter Umständen zeitlich verzögert, verursacht sein. Solche Zusammenhänge können nur erfasst werden, wenn die zeitliche Auflösung der Daten hoch genug ist. In der zweiten Studie wird die Spektralanalyse, basierend auf der Fast Fourier Transformation, auf einen Datensatz täglicher Messungen der Wassertemperatur über zwanzig Jahre hinweg angewendet. Es wird gezeigt, wie zeitliche und räumliche Skalen der Variabilität der Wassertemperatur als Indikator für Mischprozesse in einem polymiktischen See dienen können. In der dritten Studie wird die Wavelet Coherence als Diagnose-Werkzeug für einen multivariaten, hochfrequenten Datensatz genutzt. Dieser wurde zwischen dem Einsetzen einer Eisbedeckung und einer Sommerblüte von Cyanobakteriern in einem polymiktischen See im Jahr 2009 erhoben. Synchronizitäten zwischen limnologischen und meteorologischen Zeitreihen in schmalen Frequenz-Bändern wurden genutzt, um vorherrschende limnologische Prozesse zu identifizieren und analytisch zu trennen. Neben den neuen empirischen Erkenntnissen, die in den drei Fallstudien präsentiert werden, zielt diese Doktorarbeit darauf ab, Forscher*innen, Behörden und politischen Entscheidungsträger*innen eine Grundlage zu liefern, die hohe zeitliche Auflösung der heute vielfach verfügbaren Monitoring-Datensätze effizienter zu nutzen. Innovative Methoden sollen dabei helfen, Muster in den Daten Prozessen zuzuordnen und die entsprechenden Treiber und Limitationen zu identifizieren. KW - variability KW - time scale KW - wavelet KW - coherence KW - spectral analysis KW - time series analysis KW - polymictic lakes KW - process identification KW - Variabilität KW - Zeitskala KW - Spektralanalyse KW - Zeitreihenanalyse KW - polymiktische Seen KW - Prozessidentifikation Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-406955 ER - TY - JOUR A1 - Schmidt, Silke Regina A1 - Gerten, Dieter A1 - Hintze, Thomas A1 - Lischeid, Gunnar A1 - Livingstone, David M. A1 - Adrian, Rita T1 - Temporal and spatial scales of water temperature variability as an indicator for mixing in a polymictic lake JF - Inland waters : journal of the International Society of Limnology N2 - We applied coarse spectral analysis to more than 2 decades of daily near-surface water temperature (WT) measurements from Muggelsee, a shallow polymictic lake in Germany, to systematically characterize patterns in WT variability from daily to yearly temporal scales. Comparison of WT with local air temperature indicates that the WT variability patterns are likely attributable to both meteorological forcing and internal lake dynamics. We identified seasonal patterns of WT variability and showed that WT variability increases with increasing Schmidt stability, decreasing Lake number and decreasing ice cover duration, and is higher near the shore than in open water. We introduced the slope of WT spectra as an indicator for the degree of lake mixing to help explain the identified temporal and spatial scales of WT variability. The explanatory power of this indicator in other lakes with different mixing regimes remains to be established. KW - Lake number KW - polymictic lakes KW - Schmidt stability KW - seasonality KW - spectral analysis KW - variability Y1 - 2018 U6 - https://doi.org/10.1080/20442041.2018.1429067 SN - 2044-2041 SN - 2044-205X VL - 8 IS - 1 SP - 82 EP - 95 PB - Routledge, Taylor & Francis Group CY - Abingdon ER -