Current issue: 54(5)
Under compilation: 55(1)
Cherry-spruce rust caused by Thekopsora areolata (Fr.) Magnus is a serious cone pathogen of Norway spruce [Picea abies (L.) Karst.]. The rust causes great economical losses in seed orchards specialized in the production of high quality seeds. Germination range of T. areolata aeciospores from rust populations (spore sources) in seven Finnish Norway spruce seed orchards was tested on water agar and malt agar at nine temperatures varying between 6–30 °C. The temperature range of spore germination was high varying between 6 °C and 27 °C, while germination was retarded at 30 °C. The peak in germination rate of all spore sources occurred between 15–24 °C. In a model with fixed effects of agar media, temperature and spore source, temperature had the most significant effect on germination. Spore source had a less significant effect, while agar media had a non-significant effect on germination. The rust was able to germinate at low temperatures corresponding to temperatures when the thermal growing season starts at 5 °C in the spring. As spores from cones from both the spruce canopy and the ground showed very similar germination ranges, it indicated the great capacity of all spores of the rust to germinate early in the spring. Hot temperatures with over 30 °C drastically reduced germination of the rust.
Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.