Markus Neteler
Markus Neteler, PhD, is cofounder and senior consultant at mundialis, a geospatial analysis and remote sensing business located in Bonn, Germany (https://www.mundialis.de/). Prior to joining mundialis he spent 15 years as a researcher in Italy with a focus on eco-health, biodiversity, GIS and Earth observation. Since 2015 he puts his energy into the company mundialis, overseeing numerous Earth observation, GIS and cloud computing related projects (esp. actinia). His main interests are remote sensing, analysis of big geodata in the cloud and Free and Open Source software GIS development. He is release manager of GRASS GIS (https://grass.osgeo.org/) since 1997 and founding member of several FOSS4G related associations.
Sessions
Introduction info OGC WPS
OWSLib Python API
OGC WPS QGIS plugin
Actinia cloud computing (linked to GRASS 8 GIS training session: Introduction and new features)
Software requirements: GRASS GIS 8
Content:
Introduction to the new version 8 of GRASS GIS, a few concepts
Showcase the heavily redesigned graphical user interface
Interaction with data (visualization, styling, map elements)
Analysis of data from different domains
Introduction to automated processing
Hints on Python 3 scripting, spatio-temporal data analysis, and more.
Candidate datasets: improved ERA5 land air temperature, surface temperature and precipitation (daily data)
Software requirements: GRASS GIS 8
Content:
GRASS GIS supports time series processing for vector, raster, and volume data
Micro-introduction to Landsat and Sentinel satellite data archives, and the various ways to access them
Introduction to the i.sentinel toolset which allows querying Sentinel data coverage for a region of interest, downloading from multiple data sources, performing atmospheric and topographic corrections, and cloud/shadow masking
Preparation of data for multitemporal analyses is enabled in the t.sentinel and t.rast.mosaic extensions through automatic creation of space-time raster datasets (strds) and temporal aggregation to obtain cloud-free temporal mosaics of arbitrary granularity.
Computation of NDVI time series
Use Sentinel, ERA5 land air temperature, surface temperature and precipitation (daily data)