An increasing amount of Brazilian rainforest is being lost or degraded for various reasons, both anthropogenic and natural, leading to a loss of biodiversity and further global consequences. Especially in the Brazilian state of Mato Grosso, soy production and large-scale cattle farms led to extensive losses of rainforest in recent years. We used a spectral mixture approach followed by a decision tree classification based on more than 30 years of Landsat data to quantify these losses. Research has shown that current methods for assessing forest degradation are lacking accuracy. Therefore, we generated classifications to determine land cover changes for each year, focusing on both cleared and degraded forest land. The analyses showed a decrease in forest area in Mato Grosso by 28.8% between 1986 and 2020. In order to measure changed forest structures for the selected period, fragmentation analyses based on diverse landscape metrics were carried out for the municipality of Colniza in Mato Grosso. It was found that forest areas experienced also a high degree of fragmentation over the study period, with an increase of 83.3% of the number of patches and a decrease of the mean patch area of 86.1% for the selected time period, resulting in altered habitats for flora and fauna.

In the early morning hours on Tuesday, February 6, 2023, the southern part of Türkiye was struck by two large and several smaller earthquakes, causing destruction and casualties over a remarkably large area. In such cases, quick response and well-informed coordination is a key factor to successful first aid responses since damage and the number of people buried or in need often remain unclear in the hours after the disaster. The German Aerospace Center (DLR) responded to the earthquake by rapidly providing a number of information products, all above very high-resolution imagery in an easy-to-use web-based application. Next to satellite and drone imagery, damage information and pre-disaster imagery were provided to the users. Drone imagery was acquired in person for Kirikhan, a city in the south of the disaster area. Access to the viewer was granted to authorized users from public authorities, humanitarian aid organisations, and research institutes. Furthermore, DLR generated information products in the fields of settlement pattern, AI based damage assessment and tectonic movements. These data, as scientifically significant as they are, were not part of the web viewer. Within this paper, the reasons will be assessed as well as the general workflow of the activation. The paper will also discuss what steps need to be taken to ensure research outcomes being integrated into information products for users in future and how to prepare for the next disaster to still ensure a quick response but with an enriched product suite.

Many countries, especially in the global south still lack the ability to effectively pursue basic policies, which can lead, in the worst case, to armed conflicts. Access to markets is a key factor for economic growth and an important component in reducing poverty. The SDG 9.1.1 addresses the proportion of the rural population who live within 2km of an all-season road, which can be mapped by the Rural Access Index (RAI), introduced by the World Bank in 2006. This requires the road network of so-called all-season roads, population distribution and rural areas. We developed a fully automated approach, using remote sensing and other open source data to calculate the RAI on an annual basis between 2013 and 2020 for the Lake Chad region. We achieved an overall accuracy between 97.0% and 97.5% in detecting all-season roads using a Random Forest classification. Our method shows similar results to those published by the World Bank. However, our approach provides a higher spatial and temporal resolution measuring the RAI compared to previous studies and is independent of field studies.

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.

During natural hazards and other rapidly evolving crisis situations, the accessibility of evacuation routes and the delivery of emergency supplies strongly depends on road surface type. However, in many regions affected by environmental changes, conflicts, or population displacement, reliable information on road surface conditions is incomplete, outdated, or entirely unavailable, which limits effective disaster response and environmental monitoring. This study presents a satellite-based framework that classifies roads as either paved or unpaved using multispectral Sentinel-2 imagery and volunteered geographic information (VGI) from OpenStreetMap (OSM).OSM road geometries are used to extract spectral samples from Sentinel-2 surface reflectance data, which is used to train a convolutional neural network (CNN) for road surface classification across diverse environmental settings. To improve spatial consistency and practical usability, classification results are aggregated at the road-segment level to produce coherent surface classifications aligned with real-world road infrastructure. The framework is designed to be transferable and applicable across regions with varying climates, land-cover characteristics, and degrees of urbanisation.The approach has been evaluated across multiple target regions and demonstrates consistent performance beyond the training domain, which highlights its potential for cross-regional application. Due to the regular revisit time of Sentinel-2, the framework further supports multi-temporal analysis. This makes it possible to assess changes to the road surface before and after dynamic events, such as flood-induced degradation, sediment coverage or long-term urbanization. By combining freely available satellite data and open VGI, the proposed method provides a scalable tool for infrastructure monitoring, disaster response, and environmental assessment in data-scarce and rapidly changing regions.