Tree species mapping using harvester and remote sensing data
Projektet syftar till att förbättra kartläggningen av trädslag med hjälp av skördardata och fjärranalys. Bättre kartläggning bidra till en mer dynamisk industriförsörjning av virke och massaved.
The project aims to improve tree species mapping using harvester and remote sensing, LiDAR and satellite data. The expected impact will be a significant improvement of yield prediction accuracy that will allow for a more dynamic industry supply of timber and pulpwood at the requested specifications. The results will be highly relevant for forest management and planning as well as for mapping of "green infrastructure" and other environmental considerations.
Operational forest management and planning relays on tree species composition retrieved from stand databases that often is inaccurate which largely influence the industry supply. A main bottleneck for accurate tree composition mapping is the scarcity of ground-truth data describing the local variation in tree species composition. The major innovation of the proposal is to use harvester database as an in-situ data to support tree-species composition mapping. Being continuously collected, calibrated harvester data can provide a long-term solution for forest mapping in combination with multitemporal, multisource GIS and satellite imagery data. This will allow to further optimize the supply chain at a tactical planning horizon rather than applying a more reactive operational planning to mitigate the effects of varying accuracy of yield forecasts.