Remote Sensing Provides a Rapid Epidemiological Context for the Control of African Swine Fever in Germany.

Transboundary disease control, as for African swine fever (ASF), requires rapid understanding of the locally relevant potential risk factors. Here, we show how satellite remote sensing can be applied to the field of animal disease control by providing an epidemiological context for the implementation of measures against the occurrence of ASF in Germany. We find that remotely sensed observations are of the greatest value at a lower jurisdictional level, particularly in support of wild boar carcass search efforts.

Modelling Green Volume Using Sentinel-1, -2, PALSAR-2 Satellite Data and Machine Learning for Urban and Semi-Urban Areas in Germany.

Urban Green Infrastructure (UGI) provides ecosystem services such as cooling of temperatures and is majorly important for climate change adaptation. Green Volume (GV) describes the 3-D space occupied by vegetation and is highly useful for the assessment of UGI. This research uses Sentinel-2 (S-2) optical data, vegetation indices (VIs), Sentinel-1 (S-1) and PALSAR-2 (P-2) radar data to build machine learning models for yearly GV estimation on large scales. Our study compares random and stratified sampling of reference data, assesses the performance of different machine learning algorithms and tests model transferability by independent validation. The results indicate that stratified sampling of training data leads to improved accuracies when compared to random sampling. While the Gradient Tree Boost (GTB) and Random Forest (RF) algorithms show generally similar performance, Support Vector Machine (SVM) exhibits considerably greater model error. The results suggest RF to be the most robust classifier overall, achieving highest accuracies for independent and inter-annual validation. Furthermore, modelling GV based on S-2 features considerably outperforms using only S-1 or P-2 based features. Moreover, the study finds that underestimation of large GV magnitudes in urban forests constitutes the biggest source of model error. Overall, modelled GV explains around 79% of the variability in reference GV at 10 m resolution and over 90% when aggregated to 100 m resolution. The research shows that accurately modelling GV is possible using openly available satellite data. Resulting GV predictions can be useful for environmental management by providing valuable information for climate change adaptation, environmental monitoring and change detection.

Model-based selection of areas for the restoration of Acrocephalus paludicola habitats in NE Germany.

The global Aquatic Warbler (Acrocephalus paludicola, Vieillot, 1817) population has suffered a major decline due to the large-scale destruction of its natural habitat (fen mires). The species is at risk of extinction, especially in NE Germany/NW Poland. In this study, we developed habitat suitability models based on satellite and environmental data to identify potential areas for habitat restoration on which further surveys and planning should be focused. To create a reliable model, we used all Aquatic Warbler presences in the study area since 1990 as well as additional potentially suitable habitats identified in the field. We combined the presence/absence regression tree algorithm Cubist with the presence-only algorithm Maxent since both commonly outperform other algorithms. To integrate the separate model results, we present a new way to create a metamodel using the initial model results as variables. Additionally, a histogram approach was applied to further reduce the final search area to the most promising sites. Accuracy increased when using both remote sensing and environmental data. It was highest for the integrated metamodel (Cohen's Kappa of 0.4, P < 0.001). The final result of this study supports the selection of the most promising sites for Aquatic Warbler habitat restoration.

Ecosystem service restoration after 10 years of rewetting peatlands in NE Germany.

The restoration of ecosystem services, i.e., production, regulation, and information, is a global challenge, which the federal state of Mecklenburg-Vorpommern in NE Germany addressed in 2000 by rewetting over 20,000 ha of degraded peatlands within the Mire Restoration Program. We evaluated ecosystem services in 23 rewetted sites by assessing the following mire parameters within a ten year period: (a) dominant vegetation at the ecosystem level, (b) peat formation potential at the landscape level, and (c) aboveground biomass and nutrient levels. Seven to 10 years after rewetting, the wetlands formed a mosaic of vegetation types with the highest potential for peat formation and several dominant, peat-forming species accumulated high levels of aboveground biomass and nutrients (C, N, P). Common reed (Phragmites australis) accumulated the most biomass (up to 24 t dry matter/ha), and N+P during the growing season. A future management option is to annually harvest aquatic and wetland plants to reduce nutrient levels in restored mire ecosystems.