My research focuses on studying the interactions between land ecosystems, the global carbon cycle, and the Earth system. I develop and use satellite data, global vegetation models, time series analysis methods, and machine learning approaches to understand and predict vegetation phenology, vegetation dynamics, ecosystem carbon fluxes and stocks, and ecosystem disturbances like fires.
Observations from satellites shown an increasing cover of green vegetation ("greening") during the last ~30 years across wide areas of the global land (Figure). My research focus on mapping such vegetation changes (Forkel et al. 2013) and on identifiying the climatic and socioeconomic drivers behind such changes in global vegetation (Forkel et al. 2015). Furthermore I assess how these changes in vegetation affect the global carbon cycle and hence the climate system (Forkel et al. 2016).
Ecosystems take up carbon dioxide from the atmosphere through photosynthesis and release it again through respiration. Based on several datasets, we were able to estimate how long carbon dioxide resides in average in land ecosystems (Carvalhais et al. 2014). Another puzzling question about the global carbon cycle was why the seasonal amplitude of CO2 in the atmosphere is increasing in the last 40 years (Figure). By combining observations with an improved global vegetation model we found that this increase can be explained by an amplification of plant productivity in northern ecosystems (Forkel et al. 2016). Our current work focus on estimating photosynthesis from microwave satellite data (Teubner et al. 2018).
Phenology is the study of the timing of biological changes within a year. In my research, I develop methods to identify phenological metrics like the start and end of the growing season (Figure) from satellite (Forkel et al. 2015) and ground-based vegetation observations (Filippa et al. 2016). Furthermore, I improved the representation of phenology in a global vegetation model (Forkel et al. 2014) which helped to quantify the relative contributions of light, temperature, and water availability on changes in phenology (Forkel et al. 2015).
Extreme events such as drought or heatwaves can cause a reduced photosynthesis and a release of carbon dioxide from ecosystems to the atmosphere. For example, we found that extreme climate events cause increasing ecosystem productivity in spring but decreasing productivity in summer in Europe (Sippel et al. 2017). Such drought events increase also the mortality in temperate forests while frost events are important in boreal forests (Thurner et al. 2016). Droughts and heatwaves support also the occurrence of wildfires: The Figure shows the average burned fraction per year. Currently, my research focus on the factors that control the occurence and spread of wildfires and how fires can be modelled from satellite data (Forkel et al. 2017).
Ecosystem models or vegetation models are necessary to quantify and predict the carbon and water balances of ecosystems or to estimate how vegetation will change under future climate change. For these purposes different kinds of ecosystem models exist that represent different processes, that are used in diagnostic or prognostic modes, or that are based on empirical or physical descriptions of processes (Figure). The application of ecosystem models requires a careful evaluation against observations (e.g. Forkel et al. 2014, 2015, 2016). Moreover, I also use satellite datasets to improve global vegetation models (Forkel et al. 2014) or to develop new model approaches (Forkel et al. 2017).
Earth observation data or ecosystem model results cover often several years. This allows to analyze long-term changes or trends. When I started analyzing vegetation trends from satellite data, we had in our research group a discussion about what would be the best approach to do this. This discussion resulted in a publication where we compare and test different methods to detect trend changes (Forkel et al. 2013). Later, I also compared several methods to detect vegetation phenology from satellite data (Forkel et al. 2015) and contributed to the development of methods to analyze phenology from PhenoCams (Filippa et al. 2016).