Assessment of recent trends of wildfire activity in Europe

DLR's Earth Observation Center (EOC) is operating a burnt area monitoring service for Europe. It is based on mid-resolution Sentinel-3 OLCI (Ocean and Land Color Instrument) satellite imagery, research of methodologies and developments of processing chains (Nolde et al. 2020) and provides burnt area information twice a day in near-real time. The service is fully automated and targeted at supporting both, rapid mapping activities and timely post fire damage assessment. It is designed incrementally, in a way that generated results are refined and optimized as soon as new satellite data becomes available. Besides the burn perimeter and detection date, the output data also contains detailed information regarding the burn severity of each detected burnt area While the service is primarily intended for continental-scale monitoring of wildfire occurrence, the accumulated results allows the analysis of multi-year development trends regarding the mentioned parameters in addition. This study, firstly, demonstrates the capabilities of the wildfire monitoring service, and secondly, analyses trends regarding fire extent, seasonality, and burn severity for the region of Europe regarding the recent years. The results are set in relation with findings derived for study areas outside Europe, namely California / USA and New South Wales / Australia. The focus of the study is put on fire severity, since this information is not present in most common, large scale burnt area datasets. Yet, fire severity is a critical aspect of fire regimes, determining fire impacts on ecosystem attributes and associated post-fire recovery. In addition to the analysis of large-scale wildfire activity, the results of the burnt area monitoring service can be utilized to monitor the spatio-temporal evolution of large lava flow events in near-real time, as for example the 2018 Lower East Rift Zone eruption at Kilauea Volcano, Hawaii or the 2021 eruption on La Palma. Reference: Nolde, M., Plank, S., & Riedlinger, T. (2020). An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time. Remote Sensing, 12(13), 2162

nmandery/h3ronpy: v0.19.2

<h2>0.19.2 - 2023-11-16</h2> <ul> <li>Fix bug which required geopandas geometry columns to be named "geometry" in <code>geodataframe_to_cells</code>.</li> <li>Warn about possible memory exhaustion when encountering a <code>ArrowIndexError</code> in <code>explode_table_include_null</code> / <code>geodataframe_to_cells</code>.</li> </ul&gt

Satellitenbasiertes Brandflächenmonitoring

Vorstellung des am DLR entwickelten Systems zum europaweiten Brandflächenmonitoring

Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition

Automated flood detection using earth observation data is a crucial task for efficient flood disaster management. Current solutions to identify flooded areas usually rely on calculating the difference between new observations and static, pre-calculated water extents derived by either single acquisitions or timely aggregated products. Such pre-calculated datasets, however, lack representation of real-world seasonality and short-term changes in trend.In this paper we present a complete workflow to automatically detect hydrological extreme events and their spatial extent, which automatically adapts to local seasonality and trend. For that we rely on a novel combination of well-established algorithms and tools to detect anomalies in time-series of water extent across large study areas. The data is binned into a discrete global grid system H3, which greatly simplifies aggregation across spatial and temporal resolutions. For each grid cell of an H3 resolution we perform a time-series decomposition using Seasonal and Trend decomposition using Loess (STL) of the cell’s proportion which is covered with surface water. All cells receive an anomaly score, calculated with extended isolation forest (EIF) on the residuals for each step in time. A burst of anomalies represents a hydrological extreme event like a flood or low water level.The presented methodology is applied on Sentinel-1/2 data for two study areas, one near Sukkur, Pakistan and the other one in Mozambique. The detected anomalies correlate with reported floods and seasonal variations of the study areas. The performance of the process and the possibility to use different H3 resolutions make the proposed methodology suitable for large scale monitoring

Scalable Processing Of Copernicus Sentinel satellite Images Using Argo Workflows

With the goal of using a pool of resources dynamically for different applications we migrated scientific Earth Observation processors from dedicated Virtual Machines into a centralized cloud. Processing in this environment works using off-the-shelf components like Kubernetes, Docker and Argo Workflows. We were able to benefit from sharing data, tools and infrastructure in between processors. To showcase the new workflow or pipeline by means of an example we use our burnt area monitoring processor based on Sentinel-3 satellite data. A new architecture based on existing components was established, which we plan to extend it in the future

Enabling global processing of reference water products for flood mapping using Kubernetes and STAC

A key factor in successful flood mapping is the fast and easy access to water extent information at normal, non-flood conditions. In this study, we propose a fully automated and scalable methodology for generating and publishing information on permanent and seasonal surface water extent by leveraging cloud technologies such as Docker and Kubernetes. Products can be computed at global scale and made accessible free of cost via standardized interfaces. With this approach, the disaster response community benefits from easy data access and flexible integration into automated rapid mapping workflows. To showcase our approach, we generate reference water information in the scope of the 2022 flooding in Pakistan