Deriving strong rain hazard risk maps from geo-morphology

In the last summer a long time stationary rain event struck parts of western Germany leading to massive floodings especially in the Ahr valley. Such long-term stationary weather conditions get actually more and more frequent and can lead to long extreme heat or massive continuous rainfall as shown in a study of the Potsdam-Institut für Klimafolgenforschung (PIK) last year (Rousi, E., Selten, F., Rahmstorf, S., Coumou, D. (2021): Changes in North Atlantic atmospheric circulation in a warmer climate favor winter flooding and summer drought over Europe. Journal of Climate, 34, 6, 2277-2295. https://doi.org/10.1175/JCLI-D-20-0311.1 ). The flood of the Ahr revealed that the existing modelling for flood probabilities is not sufficient. Possible causes may be the comparatively short observation period of the underlying measurements, missing historical data or the dynamics of climate change are not taken into account. For this reason, our approach is based on simulations of individually adapted worst case scenarios to derive possible effects of heavy rainfall more generally and over a wide area just based on satellite data and digital elevation models. So its a simplified model which can be adapted and applied fast to regions all over the world - especially regions with only sparse available data

A new Approach to Hazard Analysis of Heavy Rainfall Events based on the Catchment Area of the Ahr River

In this summer a long time stationary rain event struck parts of western Germany leading to massive floodings - especially in the valley of the Ahr approximately 20 km south of Bonn. Such long-term stationary weather conditions get actually more and more frequent and can lead to long extreme heat or massive continous rainfall as shown in a study of the Potsdam-Institut für Klimafolgenforschung (PIK) this year. The flood of the Ahr revealed that the existing modelling for flood probabilities is not sufficient. Possible causes may be the comparatively short observation period of the underlying measurements, missing historical data or the dynamics of climate change are not taken into account. For this reason, our approach is based on simulations of individually adapted worst case scenarios to derive possible effects of heavy rainfall more generally and over a wide area. In the last years we developed a methodology for classification of strong rain dangers depending only on the terrain. We calculated strong rain danger maps covering hole Germany and Austria estimating a worst case scenario by not taking into account local drains since those are mostly blocked by leaves and branches at such sudden events. But these maps are only based on the influence of the direct surrounding in strong rain events and do not consider water coming from other areas. So we developed an additional component for including water-run-off from up-stream areas. In the presented study we calculate the maximum run-off for a whole water catchment area assuming a massive strong rain event and the following flash flood. For each position in the run-off-map a local height profile perpendicular to the flow direction is calculated and filled up with the maximum estimated water volume at this position. So cross sections along a river in a valley giving a maximum water level for the maximum possible run-off for a given strong rain event are derived. Since some part of the rain will drain away and not contribute to the run-off this is also a worst case estimation. The results are compared to aerial imagery acquired on 2021-07-16 - two days after the flooding struck the Ahr valley -, flood-masks derived from Sentinel-1 imagery and Copernicus damage assessment maps. Based on this imagery and measurements and estimations of water gauge levels we calculate the effective rain-height of the catchment and the simulation is calibrated and adapted to the observed water levels. Based on these results we can derive also an estimation of the flooding situation in the whole catchment area including tributary valleys

Automatic Object Segmentation To Support Crisis Management Of Large-scale Events

The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-RCNN algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations

Ad-hoc situational awareness during floods using remote sensing data and machine learning methods

Recent advances in machine learning and the rise of new large-scale remote sensing datasets have opened new possibilities for automation of remote sensing data analysis that make it possible to cope with the growing data volume and complexity and the inherent spatio-temporal dynamics of disaster situations. In this work, we provide insights into machine learning methods developed by the German Aerospace Center (DLR) for rapid mapping activities and used to support disaster response efforts during the 2021 flood in Western Germany. These include specifically methods related to systematic flood monitoring from Sentinel-1 as well as road-network extraction, object detection and damage assessment from very high-resolution optical satellite and aerial images. We discuss aspects of data acquisition and present results that were used by first responders during the flood disaster

Combining thermal, tri-stereo optical and bi-static InSAR satellite imagery for lava volume estimates: the 2021 Cumbre Vieja eruption, La Palma

Determining outline, volume and effusion rate during an effusive volcanic eruption is crucial as it is a major controlling factor of the lava flow lengths, the prospective duration and hence the associated hazards. We present for the first time a multi-sensor thermal and topographic satellite data analysis for estimating lava effusion rates and volume. At the 2021 lava field of Cumbre Vieja, La Palma, we combine VIIRS + MODIS thermal data-based effusion rate estimates with DSMs analysis derived from optical tri-stereo Pléiades and TanDEM-X bi-static SAR data. This multi-sensor approach allows to overcome limitations of single methodology studies and to achieve both, high-frequent observation of the relative short-term effusion rate trends and precise total volume estimates. We find a final subaerial lava volume of 212 × 10^6 ± 13 × 10^6 m3 with a MOR of 28.8 ± 1.4 m³/s. We identify an initially sharp eruption rate peak, followed by a gradually decreasing trend, interrupted by two short lived peaks in mid/end November. High eruption rate accompanied by weak seismicity was observed during the early stages of the eruption, while during later stage the lava effusion trend coincides with seismicity. This article demonstrates the geophysical monitoring of eruption rate fluctuations, that allows to speculate about changes of an underlying pathway during the 2021 Cumbre Vieja eruption

Generation of Reference Vehicle Trajectories in real-world Situations using Aerial Imagery from a Helicopter

Highly accurate reference vehicle trajectories are required in the automotive domain e.\,g. for testing mobile GNSS devices. Common methods used to determine reference trajectories are based on the same working principles as the device under test and suffer from the same underlying error problems. In this paper, a new method to generate reference vehicle trajectories in real-world situations using simultaneously acquired aerial imagery from a helicopter is presented. This method requires independent height information which is coming from a LIDAR DTM and the relative height of the GNSS device. The reference trajectory is then derived by forward intersection of the vehicle position in each image with the DTM. In this context, the influence of all relevant error sources were analysed, like the error from the LIDAR DTM, from the sensor latency, from the semi-automatic matching of the vehicle marking, and from the image orientation. Results show that the presented method provides a tool for creating reference trajectories that is independent of the GNSS reception at the vehicle. Moreover, it can be demonstrated that the proposed method reaches an accuracy level of 10 cm, which is defined as necessary for certification and validation of automotive GNSS devices

3D Informationen aus Fernerkundungsdaten für den Bevölkerungsschutz - Nutzungsmöglichkeiten am Beispiel zweier Großveranstaltungen

Um Gefahren- oder Schadenslagen bei Großveranstaltungen vorzubeugen und im Notfall schnell und effektiv handeln zu können, benötigen Entscheidungsträger Informationen mit Raumbezug für ein möglichst realitätsnahes Lagebild vor und während der Veranstaltung. Aufgrund der steigenden Verfügbarkeit von immer höher aufgelösten Fernerkundungsdaten und dem zunehmenden Bewusstsein über die Möglichkeit flächendeckende Informationen daraus abzuleiten, werden diese inzwischen immer häufiger in die Abläufe des Katastrophenmanagements eingebunden. Detaillierte, aus Fernerkundungsdaten abgeleitete 3D Informationen mit Höhenbezug bieten dem Lagemanagement wertvolle Zusatzinformationen zu Gelände- und Objekteigenschaften, sie werden jedoch bislang kaum operationell genutzt. Die vorliegende Arbeit analysiert und bewertet die Anwendung von 3D Change Detection (im Folgenden als Veränderungsanalyse bezeichnet) von Gelände- und Objektinformationen im Rahmen der Prävention und Sicherung von Großveranstaltungen. Als Anwendungsbeispiele werden aus hochaufgelösten Luftbildern zweier Großveranstaltungen, dem Wacken Open Air 2016 und der Feier zum Evangelischen Kirchentag 2017, zunächst Oberflächenmodelle berechnet. Aus diesen Oberflächenmodellen, die sowohl vor als auch während der Veranstaltung aufgenommen wurden, werden 2D und 3D Veränderungen, also Veränderungen ohne und mit Berücksichtigung von Höheninformationen auf der Basis eines neuen und automatischen Verfahrens berechnet. Der potentielle Mehrwert der gewonnenen Gelände- und Veränderungsinformationen für die Praxis wird anhand von Anwendungsmöglichkeiten diskutiert. Ein Beispiel ist ein flächendeckendes und automatisiertes Monitoring von Verkehrs- und Rettungswegen, sowie die Möglichkeit erfasste Hindernisse schnell und präzise zu analysieren und so besser über Maßnahmen der Räumung entscheiden zu können. Ein weiterer Aspekt ist die Erkennung und Klassifikation von Fahrzeugen und mobilen Unterkünften, wie z.B. Zelten, da diese Aufschluss über die Personenzahl vor Ort geben, die im Notfall versorgt oder evakuiert werden müssen. Forschungsarbeiten zu Veranstaltungen wie diesen, die eine Errichtung mobiler Infrastruktur und Unterbringungsmöglichkeiten mit sich bringen, sind zudem auf den Zivilschutzfall übertragbar und somit ein Mehrwert für den Bevölkerungsschutz allgemein

Multisensorale Ableitung von Wasserflächen aus SAR-Daten: Ein Methodenvergleich zur automatischen und halbautomatischen Ableitung von überfluteten Flächen aus SAR-Daten für ein Testgebiet in Vietnam

Das Mekong-Delta in Vietnam zählt zu den Regionen, die in den vergangenen Jahren mit zunehmender Häufigkeit von extremen Hochwasserereignissen betroffen waren, einhergehend mit gravierenden Folgen für die Bevölkerung. Mit Hilfe von aktiven Fernerkundungssensoren kann die räumliche Wasserausbreitung kurzfristig erfasst und langfristig beobachtet werden und somit einen hilfreichen Beitrag zum Krisenmanagement vor Ort leisten. Diese Arbeit wurde innerhalb des deutsch-vietnamesischen Projektes WISDOM (Water-related Information System for the Sustainable Development of the Mekong Delta) in Kooperation mit der Universität Innsbruck am Deutschen Zentrum für Luft- und Raumfahrt in Oberpfaffenhofen angefertigt. Sie stellt drei Methoden zur Ableitung von Wasserflächen aus Radardaten in Hinblick auf Genauigkeit, Übertragbarkeit und Automatisierbarkeit gegenüber. Neben einer objektbasierten Klassifikation wurden Homogenitätskriterien zur Analyse herangezogen, sowie ein Schwellwertverfahren untersucht, das inzwischen innerhalb des Projektes eine vollständig automatisierte Erfassung der Wasserflächen ermöglicht