27 September 2024

Publication: Semi-supervised European forest types mapping using high-fidelity satellite data Publication: Semi-supervised European forest types mapping using high-fidelity satellite data

Accurate and up-to-date geospatial maps of forest types are critical to effectively monitor forest damage such as fires, windstorms, disease, and other natural and anthropogenic forest events. Geospatial information makes it possible to accurately determine the location of forest ecosystems and determine their types, aiding resource management and conservation planning. The use of modern technologies of geospatial analysis allows collecting, processing and analyzing data in real time, which is key to timely response to events that may affect the health and sustainability of forests.

In this work, SWIFTT partners from the Space Research Institute of Ukraine introduce an innovative semi-supervised approach for mapping European forest types by harnessing the power of high-resolution Sentinel-1 and Sentinel-2 satellite data from the Copernicus program. The novelty of the approach lies in the integration of various data sources for training dataset creation and the utilization of the Random Forest classifier on the Google Earth Engine cloud computing platform. This innovative combination enables efficient processing and classification of vast amounts of satellite imagery for large-scale forest type mapping.

This study demonstrates the potential of synergizing cutting-edge remote sensing, cloud computing, and machine learning technologies to tackle complex environmental challenges at a continental scale, paving the way for future advancements in forest monitoring and management.

Read the paper in the link below.