Bestimmung langjähriger stündlicher Zeitreihen und räumlich hochaufgelöster Karten der Direkt-Normal-Strahlung auf der Basis von Meteosat-Daten und Atmosphärenparametern für die Nutzung in konzentrierenden Solarkraftwerken

Konzentrierende Solarsysteme können einen sehr großen Teil der zukünftigen Strom- und Wärmeerzeugung leisten. Die Technik dieser Systeme steht kurz vor der Markteinführung, da die Stromgestehungskosten beinahe konkurrenzfähig geworden sind. Eine zentrale Größe ist dabei die Direkt-Normal-Strahlung, sozusagen der "Brennstoff" solcher Kraftwerke, die einen sehr hohen Einfluss auf deren Leistung und Stromerträge hat. Konzentrierende Solarkraftwerke sind eine Großinvestition, bei der schon in der Planungsphase eine Risiko- und Kostenreduktion angestrebt wird. Daher müssen heute Aussagen über die vorhandenen Strahlungsressourcen gemacht werden, um in Zukunft Solarenergie wirtschaftlich nutzen zu können. Die vorliegende Arbeit erarbeitet ein operationelles Verfahren, das mit Hilfe von sichtabaren und infraroten Daten des geostationären Satelliten Meteosat und Atmosphärendaten für Ozon, Wasserdampf und Aerosol mittels eines Parametrisierungsmodells die stündliche Direkt-Normal-Strahlung berechnet. Diese kann für große Regionen, Länder oder einzelne Standorte innerhalb des Sichtbereiches des Satelliten Meteosat in einer räumlichen Auflösung von bis zu 5km x 5km für Zeitreihen bis zu 20 Jahren bestimmt werden. Die so gewonnenen Informationen der verfügbaren Strahlungsressource sind ein wichtiger Bestandteil bei der Planung und Auslegung von konzentrierenden Solarkraftwerken und bei der Abschätzung des Strahlungspotenzials einer Region

Das Rain Area Delineation Scheme RADS - Ein neues Verfahren zur satellitengestützten Erfassung der Niederschlagsfläche über Mitteleuropa

Die Arbeit stellt die Entwicklung eines Verfahrens zur Erkennung der Niederschlagsfläche in optischen Satellitendaten dar. Die Abgrenzung regnender Wolken beruht dabei auf dem Modellkonzept, dass diese über eine ausreichend große Kombination aus optischer Dicke und effektivem Tropfenradius verfügen müssen

Monitoring Oil Exploitation Infrastructure and Dirt Roads with Object-Based Image Analysis and Random Forest in the Eastern Mongolian Steppe

Information on the spatial distribution of human disturbance is important for assessing and monitoring land degradation. In the Eastern Mongolian Steppe Ecosystem, one of the major driving factors of human-induced land degradation is the expansion of road networks mainly due to intensifications of oil exploration and exploitation. So far, neither the extents of road networks nor the extent of surrounding grasslands affected by the oil industry are monitored which is generally labor consuming. This causes that no information on the changes in the area which is affected by those disturbance drivers is available. Consequently, the study aim is to provide a cost-effective methodology to classify infrastructure and oil exploitation areas from remotely sensed images using object-based classifications with Random Forest. By combining satellite data with different spatial and spectral resolutions (PlanetScope, RapidEye, and Landsat ETM+), the product delivers data since 2005. For the classification variables, segmentation, spectral characteristics, and indices were extracted from all above mentioned imagery and used as predictors. Results show that overall accuracies of land use maps ranged 73%–93% mainly depending on satellites’ spatial resolution. Since 2005, the area of grassland disturbed by dirt roads and oil exploitation infrastructure increased by 88% with its highest expansion by 47% in the period 2005–2010. Settlements and croplands remained relatively constant throughout the 13 years. Comparison of multiscale classification suggests that, although high spatial resolutions are clearly beneficial, all datasets were useful to delineate linear features such as roads. Consequently, the results of this study provide an effective evaluation for the potential of Random Forest for extracting relatively narrow linear features such as roads from multiscale satellite images and map products that are possible to use for detailed land degradation assessments

Extreme climatic events down-regulate the grassland biomass response to elevated carbon dioxide

Terrestrial ecosystems are considered as carbon sinks that may mitigate the impacts of increased atmospheric CO2 concentration ([CO2]). However, it is not clear what their carbon sink capacity will be under extreme climatic conditions. In this study, we used long-term (1998–2013) data from a C3 grassland Free Air CO2 Enrichment (FACE) experiment in Germany to study the combined effects of elevated [CO2] and extreme climatic events (ECEs) on aboveground biomass production. CO2 fertilization effect (CFE), which represents the promoted plant photosynthesis and water use efficiency under higher [CO2], was quantiffied by calculating the relative differences in biomass between the plots with [CO2] enrichment and the plots with ambient [CO2]. Down-regulated CFEs were found when ECEs occurred during the growing season, and the CFE decreases were statistically significant with p well below 0.05 (t-test). Of all the observed ECEs, the strongest CFE decreases were associated with intensive and prolonged heat waves. These findings suggest that more frequent ECEs in the future are likely to restrict the mitigatory effects of C3 grassland ecosystems, leading to an accelerated warming trend. To reduce the uncertainties of future projections, the atmosphere-vegetation interactions, especially the ECEs effects, are emphasized and need to be better accounted

Hyperspectral Data Analysis in R: The hsdar Package

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new hsdar package for R statistical software, which performs a variety of analysis steps taken during a typical hyperspectral remote sensing approach. The package introduces a new class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. In addition, the package provides methods to directly use the functionality of the caret package for machine learning tasks. Two case studies demonstrate the package's range of functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the human larynx is detected from hyperspectral data

Analysis and Discussion of Atmospheric Precursor of European Heat Summers

The prediction of summers with notable droughts and heatwaves on the seasonal scale is challenging, especially in extratropical regions, since their development is not yet fully understood. Thus, monitoring and analysis of such summers are important tasks to close this knowledge gap. In a previous paper, the authors presented hints that extreme summers are connected with specific conditions during the winter-spring transition season. Here, these findings are further discussed and analysed in the context of the Earth’s circulation systems. No evidence for a connection between the North Atlantic Oscillation or the Arctic Oscillation during the winter-spring transition and extremely hot and dry summers is found. However, inspection of the geopotential at 850 hPa shows that a Greenland-North Sea-Dipole is connected with extreme summers in Central Europe. This motivated the introduction of the novel Greenland-North Sea-Dipole-Index, GNDI. However, using this index as predictor would lead to one false alarm and one missed event in the time series analysed (1958–2011). Hints are found that the disturbance of the “dipole-summer” connection is due to El Niño/Southern Oscillation (ENSO). To consider the ENSO effect, the novel Central European Drought Index (CEDI) has been developed, which is composed of the GNDI and the Bivariate ENSO Time Series Index. The CEDI enables a correct indication of all extremely hot and dry summers between 1958 and 2011 without any false alarm

Nutrient cycling drives plant community trait assembly and ecosystem functioning in a tropical mountain biodiversity hotspot

- Community trait assembly in highly diverse tropical rainforests is still poorly understood. Based on more than a decade of field measurements in a biodiversity hotspot of southern Ecuador, we implemented plant trait variation and improved soil organic matter dynamics in a widely used dynamic vegetation model (the Lund-Potsdam-Jena General Ecosystem Simulator, LPJ-GUESS) to explore the main drivers of community assembly along an elevational gradient. - In the model used here (LPJ-GUESS-NTD, where NTD stands for nutrient-trait dynamics), each plant individual can possess different trait combinations, and the community trait composition emerges via ecological sorting. Further model developments include plant growth limitation by phosphorous (P) and mycorrhizal nutrient uptake. - The new model version reproduced the main observed community trait shift and related vegetation processes along the elevational gradient, but only if nutrient limitations to plant growth were activated. In turn, when traits were fixed, low productivity communities emerged due to reduced nutrient-use efficiency. Mycorrhizal nutrient uptake, when deactivated, reduced net primary production (NPP) by 61–72% along the gradient. - Our results strongly suggest that the elevational temperature gradient drives community assembly and ecosystem functioning indirectly through its effect on soil nutrient dynamics and vegetation traits. This illustrates the importance of considering these processes to yield realistic model predictions

Validation of AVHRR Land Surface Temperature with MODIS and In Situ LST—A TIMELINE Thematic Processor

Land Surface Temperature (LST) is an important parameter for tracing the impact of changing climatic conditions on our environment. Describing the interface between long- and shortwave radiation fluxes, as well as between turbulent heat fluxes and the ground heat flux, LST plays a crucial role in the global heat balance. Satellite-derived LST is an indispensable tool for monitoring these changes consistently over large areas and for long time periods. Data from the AVHRR (Advanced Very High-Resolution Radiometer) sensors have been available since the early 1980s. In the TIMELINE project, LST is derived for the entire operating period of AVHRR sensors over Europe at a 1 km spatial resolution. In this study, we present the validation results for the TIMELINE AVHRR daytime LST. The validation approach consists of an assessment of the temporal consistency of the AVHRR LST time series, an inter-comparison between AVHRR LST and in situ LST, and a comparison of the AVHRR LST product with concurrent MODIS (Moderate Resolution Imaging Spectroradiometer) LST. The results indicate the successful derivation of stable LST time series from multi-decadal AVHRR data. The validation results were investigated regarding different LST, TCWV and VA, as well as land cover classes. The comparisons between the TIMELINE LST product and the reference datasets show seasonal and land cover-related patterns. The LST level was found to be the most determinative factor of the error. On average, an absolute deviation of the AVHRR LST by 1.83 K from in situ LST, as well as a difference of 2.34 K from the MODIS product, was observed

Land Cover Change in the Andes of Southern Ecuador — Patterns and Drivers

In the megadiverse tropical mountain forest in the Andes of southern Ecuador, a global biodiversity hotspot, the use of fire to clear land for cattle ranching is leading to the invasion of an aggressive weed, the bracken fern, which is threatening diversity and the provisioning of ecosystem services. To find sustainable land use options adapted to the local situation, a profound knowledge of the long-term spatiotemporal patterns of land cover change and its drivers is necessary, but hitherto lacking. The complex topography and the high cloud frequency make the use of remote sensing in this area a challenge. To deal with these conditions, we pursued specific pre-processing steps before classifying five Landsat scenes from 1975 to 2001. Then, we quantified land cover changes and habitat fragmentation, and we investigated landscape changes in relation to key spatial elements (altitude, slope, and distance from roads). Good classification results were obtained with overall accuracies ranging from 94.5% to 98.5% and Kappa statistics between 0.75 and 0.98. Forest was strongly fragmented due to the rapid expansion of the arable frontier and the even more rapid invasion by bracken. Unexpectedly, more bracken-infested areas were converted to pastures than vice versa, a practice that could alleviate pressure on forests if promoted. Road proximity was the most important spatial element determining forest loss, while for bracken the altitudinal range conditioned the degree of invasion in deforested areas. The annual deforestation rate changed notably between periods: ~1.5% from 1975 to 1987, ~0.8% from 1987 to 2000, and finally a very high rate of ~7.5% between 2000 and 2001. We explained these inconstant rates through some specific interrelated local and national political and socioeconomic drivers, namely land use policies, credit and tenure incentives, demography, and in particular, a severe national economic and bank crisis