Field measurements, eddy-covariance measurements, optical and radar remotely sensed data are just some of the numerous types of data that are continuously collected at different spatial and temporal scales. By means of model-data fusion it is possible to integrate all those sources of information in forest models, with the aim of improving the knowledge about ecosystem processes and refine model projections.
We apply modern computational techniques, such as Bayesian methods, local and global sensitivity analysis and uncertainty analyses, to calibrate forest models, to identify strengths and weaknesses in model structure, to quantify uncertainties in model predictions and to evaluate deficiencies or biases in datasets. In our group, Bayesian statistics is largely used to estimate model parameters (Bayesian calibration), to evaluate model performances (Bayesian model comparison), to combine multiple model predictions (Bayesian model averaging). Bayesian methods are based on probability theory and have the great advantage of accounting for uncertainties in models and data.
Another line of research that has been developed in our group is data assimilation, i.e., the science of combining multiple sources of information to update the state of the forests over time. PREBAS model predictions, in combination with repeated estimates of forest structural variables (DBH, H, BA) derived from earth observations (sentinel 2), are used to monitor the status and the carbon balance of boreal forests at 10x10 meter resolution.
Augustynczik, A.L.D., F. Hartig, F. Minunno, H.-P. Kahle, D. Diaconu, M. Hanewinkel, and R. Yousefpour. 2017. “Productivity of Fagus Sylvatica under Climate Change – A Bayesian Analysis of Risk and Uncertainty Using the Model 3-PG.” Forest Ecology and Management 401. https://doi.org/10.1016/j.foreco.2017.06.061.
Hame, T., T. Mutanen, Y. Rauste, O. Antropov, M. Molinier, S. Quegan, E. Kantzas, et al. 2015. “Enabling Intelligent Copernicus Services for Carbon and Water Balance Modeling of Boreal Forest Ecosystems - North State.” In International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 2015-Novem. https://doi.org/10.1109/IGARSS.2015.7326203.
Hartig, Florian, Francesco Minunno, Stefan Paul, and David Cameron. 2017. “BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics.” Comprehensive R Archive Network (CRAN). https://cran.r-project.org/web/packages/BayesianTools/index.html.
Kalliokoski, Tuomo, Annikki Mäkelä, Stefan Fronzek, Francesco Minunno, and Mikko Peltoniemi. 2018. “Decomposing Sources of Uncertainty in Climate Change Projections of Boreal Forest Primary Production.” Agricultural and Forest Meteorology 262 (November): 192–205. https://doi.org/10.1016/J.AGRFORMET.2018.06.030.
Mäkelä, Jarmo, Francesco Minunno, Tuula Aalto, Annikki Mäkelä, Tiina Markkanen, and Mikko Peltoniemi. 2020. “Sensitivity of 21st Century Simulated Ecosystem Indicators to Model Parameters, Prescribed Climate Drivers, RCP Scenarios and Forest Management Actions for Two Finnish Boreal Forest Sites.” Biogeosciences 17 (10): 2681–2700. https://doi.org/10.5194/bg-17-2681-2020.
Minunno, F., M. van Oijen, D. R. Cameron, S. Cerasoli, J. S. Pereira, and M. Tomé. 2013. “Using a Bayesian Framework and Global Sensitivity Analysis to Identify Strengths and Weaknesses of Two Process-Based Models Differing in Representation of Autotrophic Respiration.” Environmental Modelling and Software 42 (April): 99–115. https://doi.org/10.1016/j.envsoft.2012.12.010.
Minunno, F., M. Peltoniemi, S. Launiainen, M. Aurela, A. Lindroth, A. Lohila, I. Mammarella, K. Minkkinen, and A. Mäkelä. 2016. “Calibration and Validation of a Semi-Empirical Flux Ecosystem Model for Coniferous Forests in the Boreal Region.” Ecological Modelling 341 (December): 37–52. https://doi.org/10.1016/J.ECOLMODEL.2016.09.020.
Minunno, Francesco, Mikko Peltoniemi, Sanna Härkönen, Tuomo Kalliokoski, Harri Makinen, and Annikki Mäkelä. 2019. “Bayesian Calibration of a Carbon Balance Model PREBAS Using Data from Permanent Growth Experiments and National Forest Inventory.” Forest Ecology and Management 440 (May): 208–57. https://doi.org/10.1016/j.foreco.2019.02.041.
Oijen, M. van, C. Reyer, F. J. Bohn, D. R. Cameron, G. Deckmyn, M. Flechsig, S. Härkönen, et al. 2013. “Bayesian Calibration, Comparison and Averaging of Six Forest Models, Using Data from Scots Pine Stands across Europe.” Forest Ecology and Management 289 (February): 255–68. https://doi.org/10.1016/j.foreco.2012.09.043.
Tian, Xianglin, Francesco Minunno, Tianjian Cao, Mikko Peltoniemi, Tuomo Kalliokoski, and Annikki Mäkelä. 2020. “Extending the Range of Applicability of the Semi-Empirical Ecosystem Flux Model PRELES for Varying Forest Types and Climate.” Global Change Biology 26 (5): 2923–43. https://doi.org/10.1111/gcb.14992.
Trotsiuk, Volodymyr, Florian Hartig, Maxime Cailleret, Flurin Babst, David I. Forrester, Andri Baltensweiler, Nina Buchmann, et al. 2020. “Assessing the Response of Forest Productivity to Climate Extremes in Switzerland Using Model–Data Fusion.” Global Change Biology 26 (4): 2463–76. https://doi.org/10.1111/gcb.15011.