The project aims at inferring vegetation dynamics from long term satellite records (LTSR). LTSR offer a unique long-term perspective on past changes of vegetation cover, such as changes in annual phenological cycles or long-term productivity. That way, LTSR can be used for fingerprinting climate change impacts on vegetation dynamics over time spans and environmental gradients difficult to tackle with other methods. To face the challenge of sparse data streams, which makes the estimation of subtle changes difficult, we use novel tools from Bayesian statistics.
Figure 1: LTSR example (Landsat archive) for inferring vegetation phenology from a broadleaved forest stand in Germany.
Project members and collaborators
Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys)
Senf, C., Pflugmacher, D., Heurich, M. and Krueger T. (2017) A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series. Remote Sensing of Environment, 194, 155-160. https://doi.org/10.1016/j.rse.2017.03.020
Senf, C. and Krueger, T. (2018). Inferring drivers of changing land surface phenology from Landsat time series. EarthArXiv Preprint: https://doi.org/10.31223/osf.io/sx6w2