M.Sc. defense of Sarah Nolting

M.Sc. defense of Sarah Nolting

Sarah Nolting will defend her M.Sc. thesis “Risk Assessment for Flood Events based on Geo- and Socioeconomic Data – A Case Study for North-Rhine Westphalia, Germany” on Wednesday 27th 2pm in room 0.004 OKW 86.

from her abstract: “The world’s population has doubled during the last seventy years and is expected to increase further. Simultaneously extreme natural events like storms, floods or earthquakes seem to heap up. Many people lost their lives and their homes due to these severe hazardous events globally. Therefore, the protection of the population and infrastructure presents a huge challenge for policy makers. However, the assessment of human and economic loss is mostly conducted in the post-event phase. Past studies point out that risk cannot be fully characterized without taking into account the pre-event exposure and vulnerability to natural hazards. Risk assessment for flood events and other hazards would benefit from population distribution mapping of areas at risk. The largest limitation for a detailed population mapping is the data availability. Detailed population distribution mapping by the dasymetric approach needs high resolution socioeconomic and spatial data. In order to overcome this issue, this study aims to explore the possibilities of very high-resolution geodata and the combination with detailed and dynamic population data.

The building database “Level of Detail 1” provides information about the physical characteristics of all buildings in Germany and a usage function which classifies the predominate use of each building. Additionally, a new approach for the inclusion of TripAdvisor attractions data has been developed in order to refine the population mapping. Socioeconomic data was taken from extrapolated census data of 2016. The focus lies on the variability of population distribution on different spatiotemporal scales. The resulting population distribution and economic value map on building level was used for a quantitative and qualitative exposure assessment for three different flood scenarios in North-Rhine Westphalia in Germany. The exposure assessment shows that considering variations in population distribution leads to more accurate results than concentrating on static and undynamic population counts. The exposure assessment additionally demonstrated the importance of exposure analysis in terms of risk assessment in the pre-event phase in order to develop protection strategies for humans, buildings and critical infrastructure.”

M.Sc. defense by Johannes Löw

M.Sc. defense by Johannes Löw

You are all invited to join the M.Sc. presentation by Johannes Löw. He will defend his M.Sc. thesis on Wednesday 20th of March at 2pm in room 0.004 in OKW 86.

from his abstract:
Since Sentinel-1 A and B have become fully operational, it is now possible to generate dense time series (six-day interval) for areas, which have been difficult to monitor by optical data due to cloud cover issues. In addition, over the last ten to twenty years, the derivation of phenological information from Synthetic Aperture Radar (SAR) data has been researched intensively. This study utilised dense time series of interferometric (InSAR) and polarimetric (PolSAR) features, which were temporally smoothed by GAM or LOESS-algorithms to create crop specific signatures for wheat, sugar beet, rapeseed and grassland for the growing season of 2017. In a first step, by computing descriptive statistics for crop parameters and SAR-features the discriminatory potential as well as the temporal stability of the data basis were assessed. Secondly, a qualitative analysis linking phenological stages to the behaviour of these signatures was conducted. The last step consisted of a correlation and regression analysis of wheat fields encompassing SAR features and the following crop parameter: plant height, crop cover and BBCH-values. By investigating the descriptive statistics VV backscatter intensity, entropy and K0 exhibited the highest discriminatory potential. The qualitative analyses allowed for the distinction of vegetative and reproductive stages for wheat and rapeseed. Furthermore, certain BBCH-stages like the leaf and the rosette development of sugar beet or the main shoot and the ripening of wheat were detected. The regression and correlation analysis revealed moderate negative correlation coefficients
(Pearson) for VV and VH coherence and plant height (-0.57 and -0.61). By conducting a regression analysis for each time step a decline in R2 during midseason was discovered. In the first half of the season R2 above 0.9 occurred for a pairing of Alpha, VH coherence and K0 with plant height and crop cover, whereas in the second half R2 did not surpass value above 0.6. Especially the findings of the qualitative analysis demonstrated that a composite of InSAR and PolSAR data displays the potential for feasible crop phenology monitoring.

Pilar Endara Pinillos handed in her M.Sc. thesis

Pilar Endara Pinillos handed in her M.Sc. thesis

Pilar Endara Pinillos handed in her M.Sc. thesis “Flooding patterns and vegetation developments in the Orinoco flooded savannas of Colombia.”

Her M.Sc. defense will be on Wednesday 13th at 2pm in room 0.004 (OKW 86).

The ecosystems that are present within Colombian Orinoco flooded savannas are currently being threatened by conversion of natural systems into intensive rangelands with introduced pastures, croplands and palm oil plantations. The loss of natural floodplain ecosystems has serious negative impacts on several important ecosystem services such as habitat quality for biodiversity, long-term carbon sequestration and water regulation.

In addition, this region’s natural vegetation endures strong intra-annual hydrological regimes, which in turn may affect the productivity, and consequently, the region’s carbon dynamics.

In order to understand the dynamics of vegetation productivity and its relationship with hydro-logical regimes this study presents an approach which uses Sentinel-1 and Sentinel-2 data for mapping temporal flood patterns and analyzing vegetation development according to these pat-terns and land cover in the year 2017. A flood map was created based on a combination of water masks derived with WaMaPro processor for Sentinel-1 data and with multi-threshold analysis for Sentinel-2. This map was used to assess potential differences in vegetation development -with NDVI as a proxy- of areas with differing flooding patterns and a Corine Land Cover map allowed to analyze these differences also by land cover.

The flood map allowed the separation of temporal water bodies and the understanding of the variability of vegetation development according to the highly variable water dynamics of the study region of Casanare in Colombia. Differences in the vegetation development were found among agriculture and natural grasslands. The NDVI of grasslands in areas that were flooded tended to maintain high values during the dry season while for agricultural areas the opposite behavior was found. These results demonstrate the importance of the savannas and grasslands of the Orinoco for the stability of the vegetation and its dependency on the flood dynamics.