2025/02/25 by Ville Vähä-Konka, Lauri Korhonen, Kalle Kärhä +1 · 1 voice
Environmental Science · #Remote Sensing and LiDAR Applications #Forest ecology and management #Forest Management and Policy
paper · doi:10.1016/j.tfp.2025.100811
openalex publication_date 2025/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/23
This study aimed to estimate timber assortment reduction and sawlog proportions by integrating in situ tree quality information and remote sensing-based forest management inventory estimates in clear-cutting areas. Using harvester data from operational forestry, we analysed sawlog recoveries from 683 stands collected throughout eastern Finland. The k-Nearest Neighbour (k-NN) method was used to estimate both timber assortments and species-specific sawlog proportions. Remote-sensing based forest attribute maps, satellite image composites and additional geodata were used as predictors. In addition, a Random Forest (RF) model was employed to predict total sawlog proportions. Absolute sawlog volumes were derived by multiplying the model-derived sawlog proportions by volumes in the Metsään.fi forest data repository. Our results showed that the root mean square error (RMSE) values associated with sawlog volumes of Norway spruce ( Picea abies (L.) Karst.) were between 40.8–41.0 %, and were between 54.7–59.5 % for Scots pine ( Pinus sylvestris L.). The RMSE value associated with total sawlog volume varied from 25.3 % to 27.6 %, i.e. the accuracy was considerably better when species was ignored. Our spruce-dominated dataset yielded more precise results for spruce compared to the other species. The RF model showed better performance in predicting total sawlog proportions than the k-NN approach. Integration of harvester measurements with forest databases and other sources of big geodata can provide substantially improved estimates of sawlog recoveries compared to the current state-of-the-art approach.