|
Project properties |
|
| Title | Understanding drivers of "Arctic greening" in the Siberian tundra |
| Group | Plant Ecology and Nature Conservation Group |
| Project type | thesis |
| Credits | 30 - 39 |
| Supervisor(s) | Rúna + 2nd supervisor from GIS group |
| Examiner(s) | Rúna Magnússon |
| Contact info | runa.magnusson@wur.nl |
| Begin date | 2024/01/01 |
| End date | 2027/01/01 |
| Description | A large proportion of arctic tundra and permafrost ecosystems is situated in the Russian Arctic. As collaboration and field monitoring data from Russia becomes scarce, remote sensing becomes an even more crucial tool to understand and monitor ecosystem developments.
In the past decades, Russian ecosystems have show very contrasting trends in NDVI or "greenness" of ecosystems in summer; some areas are greening and thereby likely increasing in photosynthetically active biomass. others are browning dramatically, the reasons of which are often poorly understood. Do large datasets and six-dimensional puzzles make you happy? Great! In this project, you will work with tundra ecologists and remote sensing experts to extract timeseries of vegetation greenness, climate and environmental conditions (eg. water presence or surface moisture) for a large amount of sites in the Russian Arctic, via state of the art cloud-based platforms such as google earth engine. You will then use multivariate analysis (e.g. clustering, variance partitioning, partial least squares) to identify spatial patterns in the "climate fingerprint" of tundra greenness of the Russian Arctic. You're encouraged to consider publication, but this is not required. |
| Used skills | R/google earth engine/python (your choice!), time series analysis, multivariate analysis, GIS |
| Requirements | Affinity with large datasets and GIS. Experience with Google Earth Engine and R (or equivalent) is recommended. |