During recent years, synthetic aperture radar (SAR) data have been increasingly used for flood mapping. New radar satellites especially, such as TerraSAR-X, Radarsat-2 and COSMO-SkyMed, provide high-resolution data with high potential for fast and reliable detection of inundated areas. This article compares three simple approaches to derive water areas from SAR data in relation to the German–Vietnamese project, Water-related Information System for the Sustainable Development of the Mekong Delta (WISDOM). Two methods are pixel based and use histogram-based grey-level thresholds, as well as a homogeneity criterion for classification. The third approach is object based and applies characteristic attributes of water objects such as grey value, texture and relations to neighbouring objects. Further discussed are the influence of a variation of the thresholds and the challenges to validate water masks derived from active remote-sensing data. We implemented one of the introduced approaches for surface water derivation in a water mask processor for automatic water mask calculation from radar satellite imagery (WaMaPro). This fully automatic processing chain was developed to process TerraSAR-X and Environmental Satellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) imagery in order to meet the demands for automatic flood monitoring.

Large-scale events represent a special challenge for crisis management. To ensure that participants can enjoy an event safely and carefree, it must be comprehensively prepared and attentively monitored. Remote sensing can provide valuable information to identify potential risks and take appropriate measures in order to prevent a disaster, or initiate emergency aid measures as quickly as possible in the event of an emergency. Especially, three-dimensional (3D) information that is derived using photogrammetry can be used to analyze the terrain and map existing structures that are set up at short notice. Using aerial imagery acquired during a German music festival in 2016 and the celebration of the German Protestant Church Assembly of 2017, the authors compare two-dimensional (2D) and novel fusion-based 3D change detection methods, and discuss their suitability for supporting large-scale events during the relevant phases of crisis management. This study serves to find out what added value the use of 3D change information can provide for on-site crisis management. Based on the results, an operational, fully automatic processor for crisis management operations and corresponding products for end users can be developed.

Analyzing night lights images allows identifying and monitoring affected areas during conflicts, as an overall decrease in brightness indicates abandoned or destroyed buildings, curfews, or power shortages. While detecting decrease in lights intensity is an established technique to identify critical areas during conflicts, the causes of appearing bright lights have not been extensively studied. An increase in brightness may be related to extreme events, such as fires resulting from bombing. This paper shows several examples supporting this new use of these images, validated using high-resolution optical earth observation data and ancillary information sources in the frame of the conflict in Ukraine which has been ongoing since February 2022.

As a national research institution, the German Aerospace Center (DLR) has the largest fleet of research aircraft in Europe and uses optical imaging systems to provide a valuable research basis for a wide range of applications in the fields of environment and climate, as well as global disaster management. Airborne remote sensing provides an independent database and is essential for developing and validating methods for satellite-based systems. This article gives a brief insight into the current research topics of DLR’s Earth Observation Center (EOC) with a focus on airborne remote sensing with regard to methane source analysis, water remote sensing, forestry, urban energy management and current disaster management missions, and shows how airborne remote sensing can contribute to the understanding and management of climate change.

This paper explores the application of aerial image sequences to analyze vehicle flows at urban intersections, with the goal of generating data that can inform adaptive traffic signal timing and improve urban mobility in complex, interconnected road networks. Using deep learning techniques, we detect vehicles in aerial images taken at different times of the day and week at a given urban intersection. This approach allows us to infer vehicle density, identify queuing patterns, and analyze traffic light cycles. Such analysis can assess the robustness of signal timing under different traffic flows and the factors that influence intersection performance. In addition, the use of aerial imagery allows for the derivation of often inaccessible signal timing plans while providing extensive coverage to evaluate surrounding traffic conditions that may impact the target intersections. Ultimately, this research contributes to improved traffic management strategies and supports the development of smart city infrastructure.

With the progression of climate change, Central Europe faces an increasing risk of extreme weather events, particularly floods and wildfires. One of the latest events took place in June 2024, when continuous rainfall in southern Germany caused water levels to rise. This paper presents the German Aerospace Center’s (DLR) operational response to the flood incident, focusing on the integration of Earth observation data and the dissemination of information via a web-based platform. DLR combined high-resolution aerial imagery, very high-resolution radar and optical satellite imagery, and utilized flood extent masks derived from Copernicus Sentinel-1 and -2 data to support the flood response. The collected datasets were refined with ancillary geospatial layers such as building footprints and infrastructure data, before being integrated into a web-based viewer. The viewer allowed a dynamic visualization and data exploration by public authorities and disaster response organizations, facilitating efficient coordination and decision-making. The first information layers were published within hours of acquisition, and the web-based viewer was accessed more than 800 times during the first week. User feedback from emergency stakeholders highlighted the platform’s usability, relevance, and value in crisis coordination. While certain limitations were identified such as the reliance on internet access and challenges in urban radar interpretation, the workflow proved highly effective. This case study demonstrates how a national EO agency can leverage integrated geospatial data, automation, and dissemination tools to support rapid flood response. The approach presented in this paper provides a framework that can potentially be replicated in other countries and disaster scenarios.