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There is already strong evidence that temperate lakes have been highly vulnerable to human induced climate warming during the last century. Hitherto climate impact studies have mainly focussed on the impacts of the recent long-term warming in winter and spring and little is known on the influence of climate warming on temperate lakes in summer. In the present thesis, I studied some aspects, which may have been strongly involved in determining the response of a lake to climate warming in summer. Thereby I have focussed on climate induced impacts on the thermal characteristics and the phenology and abundance of summer plankton in a shallow polymictic lake (Müggelsee, Germany). First, the influence of climate warming on the phenology and abundance of the lake plankton was investigated across seasons. Fast-growing spring phytoplankton and zooplankton (Daphnia) advanced largely synchronously, whereas long-term changes in the phenology of slow-growing summer zooplankton were clearly species-specific and not synchronised. The phenology and/or abundance of several summer copepod species changed according to their individual thermal requirements at decisive developmental stages such as emergence from diapause in spring. The study emphasises that not only the degree of warming, but also its timing within the annual cycle is of great ecological importance. To analyse the impact of climate change on the thermal characteristics of the lake, I examined the long-term development of the daily epilimnetic temperature extrema during summer. The study demonstrated for the first time for lakes that the daily epilimnetic minima (during nighttime) have increased more rapidly than the daily epilimnetic maxima (during daytime), resulting in a distinct decrease in the daily epilimnetic temperature range. This day-night asymmetry in epilimnetic temperature was likely caused by an increased nighttime emission of long-wave radiation from the atmosphere. This underlines that not only increases in air temperature, but also changes in other meteorological variables such as wind speed, relative humidity and cloud cover may play an important role in determining the lake temperature with respect to further climate change. Furthermore, a short-term analysis on the mixing regime of the polymictic lake was conducted to examine the frequency and duration of stratification events and their impacts on dissolved oxygen, dissolved nutrients and summer phytoplankton. Even during the longest stratification events (heatwaves in 2003 and 2006) the thermal characteristics of the lake differed from those typically found in shallow dimictic lakes, which exhibit a continuous stratification during summer. Particularly, hypolimnetic temperatures were higher, favouring the depletion of oxygen and the accumulation of dissolved nutrient in the hypolimnion. Thermal stratification will be very likely amplified in the future, thus, I conclude that polymictic lakes will be very vulnerable to alterations in the thermal regime with respect to projections of further climate change during summer. Finally, a long-term case study on the long and short-term changes in the development of the planktonic larvae of the freshwater mussel Dreissena polymorpha was performed to analyse the impacts of simultaneous changes in the thermal and in the trophic regime of the lake. Both the climate warming and the decrease in external nutrient load were important in determining the abundance of the pelagic larvae by affecting different features of the life-history of this species throughout the warm season. The long-term increase in the abundance and length of larvae was related to the decrease in external nutrient loading and the change in phytoplankton composition. However, the recent heatwaves in 2003 and 2006 have offset this positive effect on larval abundance, due to unfavourable low oxygen concentrations that had resulted from extremely long stratification events, mimicking the effects of nutrient enrichment. Climate warming may thus induce counteracting effects in productive shallow lakes that underwent lake restoration through a decrease in external nutrient loading. I conclude that not only the nature of climate change and thus the timing of climate warming throughout the seasons and the occurrence of climatic extremes as heatwaves, but also site-specific lake conditions as the thermal mixing regime and the trophic state are crucial factors governing the impacts of climate warming on internal lake processes during summer. Consequently, further climate impact research on lake functioning should focus on how the different lake types respond to the complex environmental forcing in summer, to allow for a comprehensive understanding of human induced environmental changes in lakes.
Natural and potentially hazardous events occur on the Earth’s surface every day. The most destructive of these processes must be monitored, because they may cause loss of lives, infrastructure, and natural resources, or have a negative effect on the environment. A variety of remote sensing technologies allow the recoding of data to detect these processes in the first place, partly based on the diagnostic landforms that they form. To perform this effectively, automatic methods are desirable.
Universal detection of natural hazards is challenging due to their differences in spatial impacts, timing and longevity of consequences, and the spatial resolution of remote-sensing data. Previous studies have reported that topographic metrics such as roughness, which can be captured from digital elevation data, can reveal landforms diagnostic of natural hazards, such as gullies, dunes, lava fields, landslides and snow avalanches, as these landforms tend to be more heterogeneous than the surrounding landscape. A single roughness metric is often limited in such detections; however, a more complex approach that exploits the spatial relation and the location of objects, such as object-based image analysis (OBIA), is desirable.
In this thesis, I propose a topographic roughness measure derived from an airborne laser scanning (ALS) digital terrain model (DTM) and discuss its performance in detecting landforms principally diagnostic of natural hazards. I further develop OBIA-based algorithms for the detection of snow avalanches using near-infrared (NIR) aerial images, and the size (changes) of mountain lakes using LANDSAT satellite images. I quantitatively test and document how the level of difficulty in detecting these very challenging landforms depends on the input data resolution, the derivatives that could be evaluated from images and DTMs, the size, shape and complexity of landforms, and the capabilities of obtaining the information in the data. I demonstrate that surface roughness is a promising metric for detecting different landforms in diverse environments, and that OBIA assists significantly in detecting parts of lakes and snow avalanches that may not be correctly assigned by applying only the thresholding of spectral properties of data and their derivatives.
The curvature-based surface roughness parameter allows the detection of gullies, dunes, lava fields and landslides with a user’s accuracy of 0.63, 0.21, 0.53, and 0.45, respectively. The OBIA algorithms for detecting lakes and snow avalanches obtained user’s accuracy of 0.98, and 0.78, respectively. Most of the analysed landforms constituted only a small part of the entire dataset, and therefore the user’s accuracy is the most appropriate performance measure that should be given in a such classification, because it tells how many automatically-extracted pixels in fact represent the object that one wants to classify, and its calculation does not take the second (background) class into account. One advantage of the proposed roughness parameter is that it allows the extraction of the heterogeneity of the surface without the need for data detrending. The OBIA approach is novel in that it allows the classification of lakes regardless of the physical state of their water, and also allows the separation of frozen lakes from glaciers that have very similar water indices used in purely optical remote sensing applications. The algorithm proposed for snow avalanches allows the detection of release zones, tracks, and deposition zones by verifying the snow heterogeneity based on a roughness metric evaluated from a water index, and by analysing the local relation of segments with their neighbouring objects. This algorithm contains few steps, which allows for the simultaneous classification of avalanches that occur on diverse mountain slopes and differ in size and shape.
This thesis contributes to natural hazard research as it provides automatic solutions to tracking six different landforms that are diagnostic of natural hazards over large regions. This is a step toward delineating areas susceptible to the processes producing these landforms and the improvement of hazard maps.