Fabian Gans is a project group leader at the Max-Planck-Institute for Biogeochemistry in Jena, Germany. For the last 10 years he has been working as a Research software engineer and dealing with data cubes from a variety of sources and ever growing in size. He is excited about the Julia Programming language, and is creator/maintainer/comtributor in several Julia packages for geospatial data analysis.
Central Europe experienced a series of droughts and heat waves between 2018 and 2020 which severely effected the forest ecosystems.The canopy cover loss has been mapped for Germany by  via the use of high spatial optical images from the Sentinel-2 and Landsat-8 satellites.In this contribution we want to present the results of assessing deforestation with a complementary approach using Sentinel-1 C-Band SAR data. We use the Recurrence Quantification Analysis (RQA) to derive a change metric which takes the order of the time series into account . This approach provides high resolution yearly forest loss maps based on a continuous data stream.
In addition to the scientific results we showcase the processing pipeline on the European Open Science Cloud. The amount of high resolution earth observation data processed in this study was too large to do all analysis on local computers or even local cluster systems. To achieve high performance computations for out-of-memory datasets we develop the YAXArrays.jl package in the Julia programming language. YAXArrays.jl provides both an abstraction over chunked n-dimensional arrays with labelled axes and efficient multi-threaded and multi-process computation on these arrays.
: Thonfeld, F.; Gessner, U.; Holzwarth, S.; Kriese, J.; da Ponte, E.; Huth, J.; Kuenzer, C.A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years.
Remote Sens. 2022, 14, 562. https://doi.org/10.3390/rs14030562
:F. Cremer, M. Urbazaev, J. Cortés, J. Truckenbrodt, C. Schmullius and C. Thiel,
"Potential of Recurrence Metrics from Sentinel-1 Time Series for Deforestation Mapping,"
in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5233-5240, 2020, https://doi.org/10.1109/JSTARS.2020.3019333