Current issue: 60(3)

Scopus CiteScore 2023: 3.5
Scopus ranking of open access forestry journals: 17th
PlanS compliant
Select issue
Silva Fennica 1926-1997
1990-1997
1980-1989
1970-1979
1960-1969
Acta Forestalia Fennica
1953-1968
1933-1952
1913-1932

Articles by Ari M. Hietala

Category : Research article

article id 10606, category Research article
Benjamin Allen, Michele Dalponte, Ari M. Hietala, Hans Ole Ørka, Erik Næsset, Terje Gobakken. (2022). Detection of Root, Butt, and Stem Rot presence in Norway spruce with hyperspectral imagery. Silva Fennica vol. 56 no. 2 article id 10606. https://doi.org/10.14214/sf.10606
Keywords: Picea abies; Heterobasidion; remote sensing; root rot; hyperspectral imagery; forest pathology
Highlights: Hyperspectral imagery can be used to detect Root, Butt, and Stem Rot in Picea abies with moderate accuracy; Spectral derivatives improved classification accuracy; Bands around 540, 700, and 1650 nm tended to be the most important for classification models.
Abstract | Full text in HTML | Full text in PDF | Author Info

Pathogenic wood decay fungi such as species of Heterobasidion are some of the most serious forest pathogens in Europe, causing rot of tree boles and loss of growth, with estimated economic losses of eight hundred million euros per year. In conifers with low resinous heartwood such as species of Picea and Abies, these fungi are commonly confined to heartwood and thus external infection signs on the bark or foliage of trees are normally absent. Consequently, determining the extent of disease presence in a forest stand with field surveys is not practical for guiding forest management decisions such as optimal rotation time. Remote sensing technologies such as airborne laser scanning and aerial imagery are already used to reduce the reliance on fieldwork in forest inventories. This study aimed to use remote sensing to detect rot in spruce (Picea abies L. Karst.) forests in Norway. An airborne hyperspectral imager provided information for classifying the presence or absence of rot in a single-tree-based framework. Ground reference data showing the presence of rot were collected by harvest machine operators during the harvest of forest stands. Random forest and support vector machine algorithms were used to classify the presence and absence of rot. Results indicate a 64% overall classification accuracy for presence-absence classification of rot, although additional work remains to make the classifications usable for practical forest management.

  • Allen, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: benjamin.allen@nmbu.no (email)
  • Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38098 San Michele all’Adige (TN), Italy E-mail: michele.dalponte@fmach.it
  • Hietala, Norwegian Institute of Bioeconomy Research, Innocamp Steinkjer, Skolegata 22, NO-7713 Steinkjer, Norway E-mail: Ari.Hietala@nibio.no
  • Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: hans-ole.orka@nmbu.no
  • Næsset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no
  • Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: terje.gobakken@nmbu.no

Register
Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content
Your selected articles
Send to email
Katila P., Katila M. (2011) New paradigm for saving the world’s forests? Silva Fennica vol. 45 no. 4 article id 448 (remove) | Edit comment
Harper K. A., Bergeron Y. et al. (2002) Post-fire development of canopy structure and co.. Silva Fennica vol. 36 no. 1 article id 561 (remove) | Edit comment
Mattson S., Bergsten U. et al. (2007) Pinus contorta growth in boreal Sweden as affect.. Silva Fennica vol. 41 no. 4 article id 273 (remove) | Edit comment
Ikonen V.-P., Kellomäki S. et al. (2009) Sawn timber properties of Scots pine as affected.. Silva Fennica vol. 43 no. 3 article id 197 (remove) | Edit comment
Saranpää P., (1983) The influence of basic density and growth ring w.. Silva Fennica vol. 17 no. 4 article id 5199 (remove) | Edit comment
Sarvas R., (1953) Instruction for the selection and registration o.. Silva Fennica vol. no. 80 article id 4638 (remove) | Edit comment
Repola J., Ahnlund Ulvcrona K. (2014) Modelling biomass of young and dense Scots pine .. Silva Fennica vol. 48 no. 5 article id 1190 (remove) | Edit comment
Omwami R. K., (1988) An economic model underlying the choice of capit.. Acta Forestalia Fennica vol. 0 no. 204 article id 7651 (remove) | Edit comment