Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain

In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance

Corporate crisis and relevant communication measures in the context of financial markets : an empirical analysis of "dieselgate"

Die vorliegende Arbeit beschäftigt sich mit dem Thema Unternehmenskrisen im Allgemeinen und Krisenkommunikation im Speziellen als Teil des Krisenmanagements, um die Bedeutung von Letzterem in Zeiten der Unsicherheit spezifisch aus Perspektive verschiedener Finanzmarktinvestoren hervorzuheben. Dabei wird auf Basis des aktuellen Forschungsstandes der im Jahr 2015 ans Licht gekommene Abgasskandal von Volkswagen mit Blick auf Krisenverlauf, Ursachen und Konsequenzen analysiert. Zudem werden verschiedene von VW an die Öffentlichkeit gerichtete Kommunikationsmaßnahmen mit Hilfe der Situational Crisis Communication Theory auf Ihre strategische Ausrichtung, Intention und Angemessenheit im Rahmen von erfolgreichem Krisenmanagement untersucht. Im Anschluss wird anhand einer empirischen Analyse von Primärdaten der Frage nachgegangen, welchen Einfluss Unternehmenskrisen und Krisenkommunikation an den Finanzmärkten haben. Dabei spiegeln die Finanzmärkte in der Kursentwicklung unterschiedlicher Instrumente verschiedene Phasen von Unternehmenskrisen wieder und zeigen je nach Situation mehr oder weniger signifikante Reaktionen auf risenkommunikation