Characterising Persistance in European Vegetation

Persistence is an important characteristic of many complex systems in nature and of the Earth system in particular. The concept is rather elusive but related to how long the system remains at a certain state before changing to a different one. Characterising persistence in the terrestrial biosphere is very relevant to understand intrinsic properties of the system and thus the legacy effects of extreme events, such as droughts and heatwaves. Such memory effects are challenging to detect in observational records and poorly represented in Earth system models. We use a number of statistical and machine learning methods to extract long and short term persistence in remotely sensed data.

Charcoal Production

The urgency to develop methods capable of identifying specific drivers of forest disturbance events is highlighted in UN REDD+ policy. Characterising drivers is essential to understand the complex socio-economic processes that cause forest loss. Charcoal production across Sub-Saharan Africa is ineffectively monitored and regulated. This contributes to the uncertainties surrounding the ecological impact of the industry and makes it difficult to separate the drivers of forest degradation in the region. In addition, this limits our ability to gasp the effects on local processes and the shifting ecosystem dynamics. High spatio-temporal systematic observations of the copernicus Sentinel-1 (S-1) synthetic aperture radar (SAR), with the intrinsic advantages of radar imagers, make it one of the most applicable sensors for detecting small scale forest disturbances. AI and cloud computing on EO Platforms (such as e.g. the Euro Data Cube) enable scalable exploration of deep stacks of SAR data at regional to continental scale. This project has resulting in an IGARSS2021 talk with accoumpanied conference proceeding, as well as a poster at the LPS2022.