Browsing by Subject "pest insects"

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  • Väisänen, Rauno; Heliövaara, Kari (The Society of Forestry in Finland - The Finnish Forest Research Institute, 1994)
    The presence/absence data of twenty-seven forest insect taxa (e.g. Retinia resinella, Formica spp., Pissodes spp., several scolytids) and recorded environmental variation were used to investigate the applicability of modelling insect occurrence based on satellite imagery. The sampling was based on 1800 sample plots (25 m by 25 m) placed along the sides of 30 equilateral triangles (side 1 km) in a fragmented forest area (approximately 100 km2) in Evo, S Finland. The triangles were overlaid on land use maps interpreted from satellite images (Landsat TM 30 m multispectral scanner imagery 1991) and digitized geological maps. Insect occurrence was explained using either environmental variables measured in the field or those interpreted from the land use and geological maps. The fit of logistic regression models varied between species, possibly because some species may be associated with the characteristics of single trees while other species with stand characteristics. The occurrence of certain insect species at least, especially those associated with Scots pine, could be relatively accurately assessed indirectly on the basis of satellite imagery and geological maps. Models based on both remotely sensed and geological data better predicted the distribution of forest insects except in the case of Xylechinus pilosus, Dryocoetes sp. and Trypodendron lineatum, where the differences were relatively small in favour of the models based on field measurements. The number of species was related to habitat compartment size and distance from the habitat edge calculated from the land use maps, but logistic regressions suggested that other environmental variables in general masked the effect of these variables in species occurrence at the present scale.
  • Heliövaara, Kari; Terho, Eero; Annila, Erkki (Suomen metsätieteellinen seura, 1983)
  • Löyttyniemi, Kari (Suomen metsätieteellinen seura, 1983)
  • Heliövaara, Kari; Väisänen, Rauno (Suomen metsätieteellinen seura, 1988)
  • Junttila, Samuli; Nasi, Roope; Koivumaki, Niko; Imangholiloo, Mohammad; Saarinen, Ninni; Raisio, Juha; Holopainen, Markus; Hyyppa, Hannu; Hyyppa, Juha; Lyytikainen-Saarenmaa, Paeivi; Vastaranta, Mikko; Honkavaara, Eija (2022)
    Climate change is increasing pest insects' ability to reproduce as temperatures rise, resulting in vast tree mortality globally. Early information on pest infestation is urgently needed for timely decisions to mitigate the damage. We investigated the mapping of trees that were in decline due to European spruce bark beetle infestation using multispectral unmanned aerial vehicles (UAV)-based imagery collected in spring and fall in four study areas in Helsinki, Finland. We used the Random Forest machine learning to classify trees based on their symptoms during both occasions. Our approach achieved an overall classification accuracy of 78.2% and 84.5% for healthy, declined and dead trees for spring and fall datasets, respectively. The results suggest that fall or the end of summer provides the most accurate tree vitality classification results. We also investigated the transferability of Random Forest classifiers between different areas, resulting in overall classification accuracies ranging from 59.3% to 84.7%. The findings of this study indicate that multispectral UAV-based imagery is capable of classifying tree decline in Norway spruce trees during a bark beetle infestation.
  • Heliövaara, Kari; Väisänen, Rauno (Suomen metsätieteellinen seura, 1986)
  • Bhat, K. M. (Suomen metsätieteellinen seura, 1980)
  • Långström, Bo (Suomen metsätieteellinen seura, 1984)