Tristan Williams

PhD Researcher in Remote Sensing at Universitat de Valencia, European Space Agency and University of Leipzig.


I am a PhD researcher in Remote Sensing based at the University of Valencia co-hosted by the European Space Agency and the University of Leipzig under the ELLIS Programme. My interests are based around the terrestrial biosphere and the interaction with climate and anthropagentic activities. This page follows my research into terrestrial ecosystems, the methods and code that I use to analyse them. Supporter of Open Science.

Current Research Projects

Characterising Persistance in European Vegitation

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.