Division of Forest and Forest Resources
PathFinder - Towards an Integrated Consistent European LULUCF Monitoring and Policy Pathway Assessment Framework
End: aug 2026
Start: sep 2022
More information
Official project websiteProject participants
Johannes Schumacher Rasmus Astrup Stefano Puliti Clara Antón Fernandéz Ryan Bright Morgane Merlin Zsofia KomaPartners
Albert-Ludwigs University Freiburg (ALU), Germany
National Institute of Geographic and Forest Information (IGN), France
Vrije Universiteit Amsterdam (VUA), Netherlands
Thünen Institute of Forest Ecosystems (TI), Germany
Croatian Forest Research Institute (CFRI)
Natural Resources Institute Finland (LUKE)
Federal Research and Training Center for Forests, Natural Hazards and Landscape (BFW), Austria
Slovenian Forestry Institute (GIS)
Czech Forest Management Institute (UHUL)
Technical Research Centre of Finland Ltd. (VTT)
Consejo Superior de Investigaciones Científicas (CSIC), Spain
Center for International Climate Research (CICERO), Norway
University of Göttingen (UGOE), Germany
University of Helsinki (UH), Finland
TreeMetrics (TM), Ireland
Eigen Vermogen van het Instituut voor Natuur- en
Bosonderzoek (EVINBO), Belgium
European Landowners Organisation (ELO), Belgium
Institut Européen de la Forêt Cultivée (IEFC), France
Finnish Meteorological Institute (FMI)
Associated partners:
Swiss Federal Research Institute for Forests Snow and Landscape Research (WSL)
University of Bristol (UB), United Kingdom
Joint Research Center (JRC), Belgium
European Environmental Agency (EEA), Denmark
| Status | Active |
| Start - end date | 01.09.2022 - 31.08.2026 |
| Project manager | Johannes Breidenbach |
| Division | Division of Forest and Forest Resources |
| Department | National Forest Inventory |
| Total budget | 57492664 |
Publications in the project
Authors
Johannes BreidenbachAbstract
No abstract has been registered
Authors
Johannes BreidenbachAbstract
No abstract has been registered
Authors
Johannes BreidenbachAbstract
No abstract has been registered
Authors
Johannes BreidenbachAbstract
Presentation
Abstract
1. Field-based vegetation mapping is important for environmental assessments.Often, the area covered by a species is estimated visually within a reference frame.However, such assessments are prone to observer bias and a large variability. 2. We developed a deep learning pipeline relying on YOLOv8 models to segmentspecies and estimate the percentage cover (%) of Vaccinium myrtillus (blueberry)and Vaccinium vitis-idaea (lingonberry), two key understory species in borealforests. We used 138 nadir and downward-looking images of the forest floorcaptured in correspondence with 50 × 50 cm vegetation sub-plots assessedwithin National Forest Inventory (NFI) plots. First, we trained a bounding-boxframe detection model to crop the image to the same area assessed in the field.Second, we trained an instance segmentation model to classify species. Third,we flattened the class values into a semantic raster and estimated the species-specific cover by pixel counting. 3. We evaluated our method against an independent test set of 156 images andfound a root mean squared error (RMSE) of 8.82% for blueberry and 3.49% forlingonberry and no substantial systematic errors. An additional comparison withocular estimation by various field workers for the same plots showed that themodel estimates were within the range of estimates by field workers 8 out of 9times for blueberry and 7 out of 9 times for lingonberry. 4. The developed method shows promise in reducing observer bias and variabilityin vegetation surveys, thereby improving their consistency while significantlyreducing the time needed for species-specific coverage estimation. This isparticularly beneficial for repeated measurements and monitoring vegetationcover dynamics. However, as the method relies on RGB data, it is limited toestimating the percentage of visible species that are not obscured by others.Expanding the method to include a broader range of cover classes (e.g. grasses,rocks, logs) or species could automate the capture of crucial information
Authors
Daniel Moreno-Fernández Johannes Breidenbach Isabel Cañellas Gherardo Chirici Giovanni D’amico Marco Ferretti Francesca Giannetti Stefano Puliti Sebastian Schnell Ross Shackleton Mitja Skudnik Iciar AlberdiAbstract
Forest biodiversity is a multifaceted term encompassing tree and shrub diversity and the diversity of other life forms such as animals or fungi. Extensive forest monitoring networks such as National Forest Inventories or the International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forest plots have implemented biodiversity-monitoring protocols to satisfy increasing information demands. However, these protocols often evaluate biodiversity through potential biodiversity indicators (e.g., stand structure and deadwood), which may not provide sufficient information on other aspects of the current forest biodiversity status. In this study, we present the forest biodiversity monitoring results and lessons from a cross-country study to support large-scale monitoring systems. We developed, evaluated, and discussed harmonized protocols, mainly focused on birds and mammals, which extend beyond the traditional features captured in large-scale forest inventories. We leverage information from 30 intensively monitored plots established in six European countries to achieve these goals. The protocols were helpful in recording data that could be used to reproduce biodiversity-related attributes such as measures of forest structure, regeneration, deadwood features, and bird and mammal diversity. Specifically, field data on trees was used to describe structural features of forests such as stand composition and forest complexity. In contrast, composition and regeneration data provided helpful information for other biodiversity indicators. Data gathering to monitor bird and mammal diversity requires revisiting the plots, which involves greater economic investment and human effort. Once the bird and mammal data have been collected, advanced algorithms could facilitate and enhance the efficiency of the analyses. To optimize the monitoring efficiency, we recommend including these two new biodiversity assessments in a subset of extensive survey plots. Furthermore, using standard guidelines for these new assessments across all countries would facilitate the comparison and reporting of statistical data.