Change detection in spatiotemporal SAR data for deforestation monitoring

Forests play a vital role in the wellbeing of our planet. Large and small scale deforestation across the globe is threatening the stability of our climate, forest biodiversity, and therefore the preservation of fragile ecosystems and our natural habitat as a whole. With increasing public interest in climate change issues and forest preservation, a large demand for carbon offsetting, carbon footprint ratings, and environmental impact assessments is emerging. Satellite remote sensing is the only method that can provide global coverage at frequent revisit times and is therefore the standard method for global forest monitoring. Most often, deforestation maps are created from optical data such as Landsat and MODIS. Although such maps are of generally good quality, they cannot quantify biomass and are not typically available at less than annual intervals due to persistent cloud cover in many parts of the world, especially the tropics where most of the world’s forest biomass is concentrated. Synthetic Aperture Radar (SAR) can fill this gap as it penetrates clouds and interacts with the three-dimensional structure on the ground in a way that scales with volume and therefore biomass. While longer wavelengths are better for deeper penetration of the canopy and therefore biomass estimation, one of the most readily available data sources is Sentinel-1, a shorter wavelength C-band radar. In this thesis, the theory behind SAR is discussed to demonstrate its usefulness for forest monitoring. The potential for C-band SAR data to distinguish forest and non-forest is then assessed empirically in different regions of the world and existing change detection algorithms for deforestation are reviewed. The effect of spatial and temporal context on change detection accuracy is investigated, leading to the development of a robust method for deforestation detection in the absence of reliable reference data which often constitutes the largest practical hurdle. This method achieves a change detection sensitivity (producer’s accuracy) above 99%, although false positives lead to a low user’s accuracy of about 60%. The mean change detection delay amounts to about two to three months. While further work is required to reduce the false positive rate, improve detection delay, and validate this method in different biomes, the results show that Sentinel-1 data have the potential to advance global deforestation monitoring