EGU24-22372, updated on 11 Mar 2024
https://doi.org/10.5194/egusphere-egu24-22372
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

ForestMap: The next generation of forest maps - adapting a Nordic success story

Johan E. S. Fransson1, Shafiullah Soomro2, Anton Holmström3, Mats Nilsson4, Jari Salo5, Maurizio Santoro6, Elif Sertel7, Jörgen Wallerman8, Cem Ünsalan9, and Juris Zariņš10
Johan E. S. Fransson et al.
  • 1Linnaeus University, Department of Forestry and Wood Technology, SE-351 95 Växjö, Sweden
  • 2Linnaeus University, Department of Computer Science and Media Technology, SE-351 95 Växjö, Sweden
  • 3Katam Technologies, SE-222 21 Lund, Sweden
  • 4Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-901 83 Umeå, Sweden
  • 5University of Helsinki, Department of Economics and Management, FI-00014 Helsinki, Finland
  • 6Gamma Remote Sensing, CH-3073 Gümligen, Switzerland
  • 7Istanbul Technical University, Department of Geomatics Engineering, TU-34469 Maslak/Istanbul, Turkey
  • 8Swedish University of Agricultural Sciences, Department of Forest Resource Management, SE-901 83 Umeå, Sweden
  • 9Yeditepe University, Department of Electrical and Electronics Engineering, TU-34469 Maslak/Istanbul, Turkey
  • 10Latvian State Forest Research Institute, LV-2169 Riga, Latvia

Building on the positive experiences with open forest map data in Scandinavia, it is evident that extending a similar solution globally has the potential to revolutionize forest management and business on a worldwide scale. While forest management in the Nordic countries can certainly be enhanced, the most rapid solution for climate change mitigation involves providing other nations with opportunities akin to those that have benefited the forestry sector in Sweden during the initial stages of digitalization.

In the proposed project, we aim to create a novel hierarchical decision-making system for efficient forest mapping, leveraging a diverse range of remote sensing data sources with varying resolutions. This hierarchical system will be developed using state-of-the-art AI methods, complemented by results from traditional computer vision techniques such as texture analysis, saliency, and probabilistic object representation. A significant strength of the project lies in using the forest data and maps of Sweden and Finland as test beds to benchmark the methodology developed.

We are confident that this project will make substantial contributions to climate change mitigation, biodiversity enhancement, and other societal values. Moreover, it aims to foster the creation of new business models by developing an innovative methodology for the next generation of forest maps. Our vision is to adapt the success story of open forest map data from the Nordic region globally, harnessing the power of advanced AI technology and integrated use of remote sensing and field data.

How to cite: Fransson, J. E. S., Soomro, S., Holmström, A., Nilsson, M., Salo, J., Santoro, M., Sertel, E., Wallerman, J., Ünsalan, C., and Zariņš, J.: ForestMap: The next generation of forest maps - adapting a Nordic success story, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22372, https://doi.org/10.5194/egusphere-egu24-22372, 2024.