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Articles by Michele Dalponte

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
article id 10244, category Research article
Hans Ole Ørka, Endre H. Hansen, Michele Dalponte, Terje Gobakken, Erik Næsset. (2021). Large-area inventory of species composition using airborne laser scanning and hyperspectral data. Silva Fennica vol. 55 no. 4 article id 10244. https://doi.org/10.14214/sf.10244
Keywords: airborne laser scanning; Dirichlet regression; hyperspectral; species proportions; species-specific forest inventory
Highlights: A methodology for using hyperspectral data in the area-based approach is presented; Hyperspectral data produced satisfactory results for species composition in 90% of the cases; Parametric Dirichlet regression is an applicable method to predicting species proportions; Normalization and a tree-based selection of pixels provided the overall best results; Both visible to near-infrared and shortwave-infrared sensors gave acceptable results.
Abstract | Full text in HTML | Full text in PDF | Author Info

Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m2 collected over 350 km2 of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.

  • Ørka, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway ORCID https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no (email)
  • Hansen, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway; Norwegian Forest Extension Institute, Honnevegen 60, NO-2836 Biri, Norway ORCID https://orcid.org/0000-0001-5174-4497 E-mail: eh@skogkurs.no
  • Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige, TN, Italy ORCID https://orcid.org/0000-0001-9850-8985 E-mail: michele.dalponte@fmach.it
  • Gobakken, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway ORCID https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
  • Næsset, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

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