Monitoring of boreal forests in Siberia : integrating ground and satellite (C- and L-band SAR) data

Anthropogenic climate change constitutes one of the main global crises in the 21st century. It manifests itself distinctly in global warming and its effects. Forests play an essential role in mitigating the effects of climate change, improving our knowledge of the distribution and changes of terrestrial carbon stocks is vital to mitigate its consequences. Therefore, remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. Forests in the Russian Federation are of particular importance as it is the most forested country in the world and at the same time, it is the country with the highest uncertainty when calculating global carbon stocks. Remote sensing is recommended as one of the tools to ensure systematic and operational forest monitoring. It can acquire data over large areas with a high repetition rate and at a relatively low cost. In particular, microwave sensors are recommended as they can provide weather and sun independent, systematic observations with high temporal frequency. The main goal of this cumulative dissertation was to develop methods using new algorithms for estimating parameters for boreal forests from remote sensing data acquired with Synthetic Aperture Radar (SAR). Using the SAR data acquired by the sensor with the longest wavelength available at the moment of writing this dissertation in space, the L-band, methods for estimating the above-ground forest biomass were developed. For this purpose, algorithms for machine learning (ML) were applied and validated. These methods were chosen because they are recommended for large data sets and an incomplete theoretical understanding of processes, e.g., the interaction between the forest and the radar signal, and are relatively new in forest monitoring studies. In addition, efforts have been made to establish improved mapping of large-scale forest cover change.


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