A separate ticket is required to attend this masterclass. If you already have a conference ticket and would like to attend but haven't yet purchased a masterclass ticket, please contact us at [email protected]Heat waves in Europe are increasing in frequency and intensity. People and economies are under pressure: extreme heat is costly for agriculture and deadly for people. At the same time, floods are among the most frequent and damaging natural disasters in Europe – yet understanding their true impact remains difficult.
In this session, participants will learn the skills necessary to make use of satellite images to analyse extreme heat or to systematically track flood damage. After a morning introduction to the topic, tools, and data/satellite imagery sources, participants will spend the afternoon working on one of two hands-on tracks:
Track 1: Flooding
Participants will learn how to use Copernicus Emergency Management Service (EMS) to retrieve flood data manually and via the Copernicus EMS API, clean and structure the data, and calculate flood extent and impact across agriculture, infrastructure, ecosystems, and population areas. They will learn how to link impacted areas to the EU’s statistical regions (using NUTS classifications).
Track 2: Extreme heat
Participants will learn to navigate USGS Earth Explorer to find and download imagery for land surface temperature (LST) analysis. Using R for spatial analysis, they’ll identify which neighborhoods in their region are most affected by heat. They will also use auxiliary data to examine the impact of different land types on heat. Participants are welcome to bring their own socioeconomic or location data (e.g., nursing homes, kindergartens) for investigation.
We will assume you have some experience with data in spreadsheets, but you do not need any prior knowledge of coding in R or Python or satellite imagery. You will leave with the skills (and the data!) needed to work on a hyper-local or national stories about the effects of extreme heat and flooding. These methodologies will also help you create a blueprint for other investigations, which would make good use of satellite imagery.
Key skills learned:
Learn the basics of R (navigating RStudio, importing data, tidyverse, ggplot) and Python (using the pandas library to load, filter, and analyze data, combine datasets, and export your results);
Basics of geodata (file types, projections, NUTS system);
Where to access free, high-quality satellite imagery, and common limitations of using it in investigations
Navigating satellite imagery portals and databases for natural disasters (depends on the choice of Copernicus EMS or Landsat).
