Forest management plans have long been used as a tool for individual forest owners to get an overview of the forest in order to plan forestry activities. The forest management plan has traditionally been produced with aerial photographs followed by field inspection. As remote sensing data becomes more available, e.g. through satellites or national laser scanning, forest variable estimates from remote sensing can partly be used instead of field visits, which rationalized the production of forest management plans. Increased possibilities exist today to automatically and continuously update the content of the forest management plan using time series of remote sensing data together with field data, e.g. from harvesters.
This postdoctoral project aims to scientifically produce new knowledge with the objective of developing the next generation’s digital and dynamic forest management plans. The position will be linked to the FRAS II research program, jointly run by Linnaeus University, Skogforsk and the Swedish University of Agricultural Sciences in close cooperation with the forest sector in southern Sweden. The research program will consist of three PhD student projects and three postdoctoral projects that focus on different aspects of forest management. It will also be linked to the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA), focusing its efforts on scientific questions in the collection, analysis, and utilization of large data sets. With its core in computer science, it takes a multidisciplinary approach and collaborates with researchers from all faculties at the university. In addition, the postdoctoral fellow will collaborate with national and international experts in the field. For example, an opportunity is offered to collaborate with researchers in the Horizon 2020 project ForestMap, which aims to produce next-generation forest maps with AI.
Sweden Academic Agriculture Biology Earth Science Geophysics Postdoc Linnaeus University