Geospatial and EO data analysis
Thanks to the unique composition of the team at mundialis we are able to offer more than a decade of experience in the analysis of massive geodata and Earth observation data. Over the past years we gained notable knowledge in gap-filling and aggregation of daily MODIS Land Surface Temperature time series. This knowledge our team members apply to environmental modelling, especially related to invasive species like the tiger mosquito expansion in Europe and other topics.
Which data processing does mundialis offer?
Analysis of GIS data, preferably open data:
- transport networks used for routing and flow analysis
- elevation models, merging of heteorogeneous data used for terrain, erosion, flood risk analysis, solar radiation estimation
- sensor network data from meteorological stations
- cadastral information which are needed for detailed risk assessment of potential property loss in case of hazards
- LiDAR data from laserscan flights in order to assess urban morphometry
Analysis of Earth observation data:
- images from UAV like octocopters in the visible, infrared or thermal spectrum
- aerial imagery, from historical aerial data to modern multispectral imagery
- satellite data with focus on time series processing used for the extraction of NDVI over time, assessment of urban heat islands, landuse/landcover identification with OBIA and machine learning approaches. We explore Landsat, Sentinel-2, Pleiades, Worldview and other data
For us combining geospatial and Earth observation data is the key!
Our approach of combining geospatial and remote sensing data enables us to not only e.g. identify urban heat islands but to also estimate the number of people being affected. This leads to informed decisions which will be more cost effective and strategically targeting the problem.
Download: mundialis flyer DE (PDF)
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|Soil moisture time series
Visualization of time series analysis (soil moisture t1)
Visualization of time series analysis (soil moisture t2)
NDVI analysis of center pivot irrigation
NDVI to determinate agricultural land use
|Dehazing of Landsat data in Borneo, Indonesia||
Zoomed area – original RGB natural color composite