Current projects of the Geomatics Lab
Reconstructing Forest Disturbance Dynamics in Europe
Project period: 11/2016-04/2018
This DAAD funded project aims at reconstructing the historic (1985-2015) forest disturbance dynamics of central European forests combining Landsat time series analysis and statistical modeling. The project is a collaborative project between the Geomatics Lab and the Institute of Silviculture at the University of Natural Resources and Life Sciences (BOKU) in Vienna. More...
Project period: 01/2015-12/2017
The aim is to develop the Big Data system GeoMultiSens for the management and analysis of large volumes of remote sensing data. Scientists from disciplines such as Big Data infrastructures, parallel computing environments, Visual Analytics and remote sensing work on GeoMultiSens. More...
Project period: 04/2015-03/2017
Combining data from Sentinel2 with Landsat allows generating dense intra-annual time series with high spatial resolution. This provides the opportunity to directly derive detailed phenological information on land surfaces at decameter scales and to improve time series based mapping. In the context of this project, we develop methods that allow exploring the potential of the combined use of Sentinel-2 and Landsat data. More...
UrbanEARS - Urban ecosystem analysis supported by remote sensing
Project period: 01/2015-09/2018
This project aims at exploring the potential of data from existing and future airborne and spaceborne multi- and hyperspectral missions for detailed characterization the (bio)physical urban environment. Remote sensing derived information will be used for modeling and simulating urban climate, hydrology and urban growth. More...
Landsat Science Team
Project period: 2012-2017
We contribute to the Landsat Science Team with a focus on long-term satellite data analysis, regional to sub-continental approaches and cross-sensor integration between Landsat and European satellite missions. Our focus is on rapidly changing land systems, including topics such as REDD+ or global land use intensification. More...
EnMAP Core Science Team
Project period: 01/2013-12/2018
The EnMAP Box
is a license-free and platform-independent software interface designed to process hyperspectral remote sensing data, and particularly developed to handle data from the EnMAP sensor. It includes features such as Support Vector Machines classification or regression of image data and allows easy incorporation of external modules for EnMAP data processing developed by other research groups. More...