Forests are of paramount importance for the Earth's ecosystem since they play a crucial role in reducing carbon dioxide concentration in the atmosphere and in controlling climate changes. The study of deforestation, global forest coverage, and biomass development is fundamental for assessing forests' impact on the ecosystem. Remote sensing represents a powerful tool for constant monitoring of vegetated areas on a worldwide scale. In particular, given the daylight independence and the capability to penetrate clouds, space-borne synthetic aperture radar (SAR) systems represent a unique solution for mapping and monitoring forests. Sentinel-1, with its broad coverage and short revisit-time, is a breakthrough technology, ideal for the generation of updated forest coverage map products and the rapid monitoring of large-scale areas, aiming at detecting ongoing deforestation activities and forest disturbance.

The proposed research project's main objective is to develop and implement advanced image-processing methods and strategies for the generation of high-resolution maps of forest coverage and deforestation from Sentinel-1 interferometric SAR data. Even though the detected SAR backscatter already provides useful information on forest coverage and structure, the use of SAR interferometry adds valuable and reliable information to the classification method. In particular, the interferometric coherence's temporal dynamic, with a sampling period of 6 or 12 days, is investigated and modeled for different types of land cover.

The accurate estimation of InSAR parameters is of fundamental importance for approaching this analysis. The proposed methodology exploits nonlocal estimation methods to retrieve reliable information about InSAR parameters of the full-resolution SAR image. Different classification approaches are compared, from classical pixel/region-based classifiers to more recent machine learning models, such as Deep Convolutional Neural Networks.