Change detection in spatiotemporal SAR data for deforestation monitoring
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Date
22/08/2022Item status
Restricted AccessEmbargo end date
22/08/2023Author
Hansen, Johannes
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Abstract
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.