Multi-Temporal, Multi-Modal UAV and Machine Learning Framework for Early Detection and Mapping of Bacterial Leaf Blight in Rice

We investigated how the size and diversity of tree patches in Indonesian oil palm landscapes influence the movement of water to the atmosphere and local cooling. Our study shows that larger tree patches increase cooling mainly by supporting greater plant diversity and more complex vegetation structure. These findings suggest that expanding and diversifying tree patches can help manage microclimate and water cycling in agricultural areas.

Europe frequently experiences compound events, with major impacts. We investigate these events’ interactions, characteristics, and changes over time, focusing on socio-economic impacts in Germany and central Europe. Highlighting 2018’s extreme events, this study reveals impacts on water, agriculture, and forests and stresses the need for impact-focused definitions and better future risk quantification to support adaptation planning.

We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.

The objective of this study was to perform a comprehensive, multi-faceted analysis of the 2018 extreme summer in terms of heat and drought in central and northern Europe, with a particular focus on Germany. A combination of favorable large-scale conditions and locally dry soils were related with the intensity and persistence of the events. We also showed that such extremes have become more likely due to anthropogenic climate change and might occur almost every year under +2 °C of global warming.