We are the research group for Geocomputation and Earth Observation (GECO) at the Institute of Geography, University of Bern, Switzerland.
… a team of researchers driven by our curiosity to understand how the terrestrial biosphere operates.
… capacity to detect, quantify, and predict responses of plants and ecosystems to climate change and forecast the impact of extremes.
This is my research group’s website. Here, you can learn about who we are, and about our research. And you can explore our open access tools for the community, and our freely accessible learning materials.
As a group, we are a collection of climate, ecosystem, and data scientists with a special interest in interactions between global environmental change and terrestrial ecology and biogeochemistry.
Our work yields insights into climate change impacts on land ecosystems and provides data-informed predictions of how forests and grasslands respond to a climatic extreme events, rising CO2 and changes in nutrient cycles. We use Earth observation data and develop process-based models that are founded on eco-evolutionary optimality principles to explain plant traits and their adaptation and acclimation to the environment. In more data-driven approaches, we apply machine learning and data assimilation techniques using diverse ecological data (ecosystem flux measurements, forest inventories, remote sensing, and manipulation experimental data, etc.). In brief, we are building models, as simple as possible and as complex as necessary to learn the most. All open access, of course.
We are motivated to gain a better understanding of issues that are becoming increasingly pressing to society and policy and that are key to creating a sustainable future.
Overview of some past and ongoing research.
The advent of abundant Earth observation data enables the development of novel predictive methods for forecasting climate impacts on the state and health of terrestrial ecosystems. Here, we predict the spatial and temporal variations of land surface reflectance and vegetation greenness, measuring the density of green vegetation and active foliage area, conditioned on current and past weather and the local topography. We train two alternative recurrent deep learning models that combine Long Short-Term Memory cells with convolutional layers (ConvLSTM) for forecasting the spatially resolved deviation of surface reflectance across a heterogeneous landscape from a specified initial state. Using data from diverse ecosystems and land cover types across Europe and following a standardized model evaluation framework (EarthNet2021 Challenge), our results indicate increased performance in predicting surface greenness during extreme drought events of the models presented here, compared to currently published benchmarks. This demonstrates how deep learning methods for optical Earth observation time series enable an early-warning of vegetation responses to the impacts of climatic extreme events, such as the drought-related loss of green foliage.
Theory predicts that rising CO2 increases global photosynthesis, a process known as CO2 fertilization, and that this is responsible for much of the current terrestrial carbon sink. The estimated magnitude of the historic CO2 fertilization, however, differs by an order of magnitude between long-term proxies, remote sensing-based estimates and terrestrial biosphere models. Here we constrain the likely historic effect of CO2 on global photosynthesis by combining terrestrial biosphere models, ecological optimality theory, remote sensing approaches and an emergent constraint based on global carbon budget estimates. Our analysis suggests that CO2 fertilization increased global annual terrestrial photosynthesis by 13.5 ± 3.5% or 15.9 ± 2.9 PgC (mean ± s.d.) between 1981 and 2020. Our results help resolve conflicting estimates of the historic sensitivity of global terrestrial photosynthesis to CO2 and highlight the large impact anthropogenic emissions have had on ecosystems worldwide.
Mechanistic vegetation models serve to estimate terrestrial carbon fluxes and climate impacts on ecosystems across diverse biotic and abiotic conditions. Systematically informing them with data is key for enhancing their predictive accuracy and estimate uncertainty. Here we present the Simulating Optimal FUNctioning rsofun R package, providing a computationally efficient and parallelizable implementation of the P-model for site-scale simulations of ecosystem photosynthesis, complemented with functionalities for Bayesian model-data integration and estimation of parameters and uncertainty. We provide a use case to demonstrate the package functionalities for modelling ecosystem gross CO2 uptake at one flux measurement site, including model sensitivity analysis, Bayesian parameter calibration, and prediction uncertainty estimation. rsofun lowers the bar of entry to ecosystem modelling and model-data integration and serves as an open access resource for model development and dissemination.