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Articles by Hans Ole Ørka

Category: Research article

article id 10695, category Research article
Ana de Lera Garrido, Terje Gobakken, Hans Ole Ørka, Erik Næsset, Ole M. Bollandsås. (2022). Estimating forest attributes in airborne laser scanning based inventory using calibrated predictions from external models. Silva Fennica vol. 56 no. 2 article id 10695. https://doi.org/10.14214/sf.10695
Keywords: forest inventory; LIDAR; calibration; area-based approach; spatial transferability; temporal transferability
Highlights: Three approaches to calibrate temporal and spatial external models using field observations from different numbers of local plots are presented; Calibration produced satisfactory results, reducing the mean difference between estimated and observed values in 89% of all trials; Using few calibration plots, ratio-calibration provided the lowest mean difference; Calibration using 20 plots gave comparable results to a local forest inventory.
Abstract | Full text in HTML | Full text in PDF | Author Info

Forest management inventories assisted by airborne laser scanner data rely on predictive models traditionally constructed and applied based on data from the same area of interest. However, forest attributes can also be predicted using models constructed with data external to where the model is applied, both temporal and geographically. When external models are used, many factors influence the predictions’ accuracy and may cause systematic errors. In this study, volume, stem number, and dominant height were estimated using external model predictions calibrated using a reduced number of up-to-date local field plots or using predictions from reparametrized models. We assessed and compared the performance of three different calibration approaches for both temporally and spatially external models. Each of the three approaches was applied with different numbers of calibration plots in a simulation, and the accuracy was assessed using independent validation data. The primary findings were that local calibration reduced the relative mean difference in 89% of the cases, and the relative root mean squared error in 56% of the cases. Differences between application of temporally or spatially external models were minor, and when the number of local plots was small, calibration approaches based on the observed prediction errors on the up-to-date local field plots were better than using the reparametrized models. The results showed that the estimates resulting from calibrating external models with 20 plots were at the same level of accuracy as those resulting from a new inventory.

  • de Lera Garrido, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ana.de.lera@nmbu.no (email)
  • 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
  • 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
  • Bollandsås, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ole.martin.bollandsas@nmbu.no
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
article id 10272, category Research article
Ana de Lera Garrido, Terje Gobakken, Hans Ole Ørka, Erik Næsset, Ole M. Bollandsås. (2020). Reuse of field data in ALS-assisted forest inventory. Silva Fennica vol. 54 no. 5 article id 10272. https://doi.org/10.14214/sf.10272
Keywords: airborne laser scanning; data reuse; temporal model transferability
Highlights: Six biophysical forest attributes were estimated for small stands without using up-to-date field data; The approaches included reused model relationships and forecasted field data; The accuracy of height estimates was comparable with the accuracy of an ordinary forest inventory with up-to-date field- and ALS data; Both approaches tended to produce estimates systematically different from the ground reference.
Abstract | Full text in HTML | Full text in PDF | Author Info

Forest inventories assisted by wall-to-wall airborne laser scanning (ALS), have become common practice in many countries. One major cost component in these inventories is the measurement of field sample plots used for constructing models relating biophysical forest attributes to metrics derived from ALS data. In areas where ALS-assisted forest inventories are planned, and in which the previous inventories were performed with the same method, reusing previously acquired field data can potentially reduce costs, either by (1) temporally transferring previously constructed models or (2) projecting field reference data using growth models that can serve as field reference data for model construction with up-to-date ALS data. In this study, we analyzed these two approaches of reusing field data acquired 15 years prior to the current ALS acquisition to estimate six up-to-date forest attributes (dominant tree height, mean tree height, stem number, stand basal area, volume, and aboveground biomass). Both approaches were evaluated within small stands with sizes of approximately 0.37 ha, assessing differences between estimates and ground reference values. The estimates were also compared to results from an up-to-date forest inventory relying on concurrent field- and ALS data. The results showed that even though the reuse of historical information has some potential and could be beneficial for forest inventories, systematic errors may appear prominent and need to be overcome to use it operationally. Our study showed systematic trends towards the overestimation of lower-range ground references and underestimation of the upper-range ground references.

  • de Lera Garrido, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ana.maria.lera.garrido@nmbu.no (email)
  • 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
  • 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
  • Bollandsås, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ole.martin.bollandsas@nmbu.no
article id 156, category Research article
Ilkka Korpela, Hans Ole Ørka, Matti Maltamo, Timo Tokola, Juha Hyyppä. (2010). Tree species classification using airborne LiDAR – effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fennica vol. 44 no. 2 article id 156. https://doi.org/10.14214/sf.156
Keywords: airborne laser scanning; ALS; laser; Optech ALTM3100; Leica ALS50-II; canopy; crown modeling; monoplotting; backscatter amplitude; intensity; discriminant analysis
Abstract | View details | Full text in PDF | Author Info
Tree species identification constitutes a bottleneck in remote sensing-based forest inventory. In passive images the differentiating features overlap and bidirectional reflectance hampers analysis. Airborne LiDAR provides radiometric and geometric information. We examined the single-trees-level response of two LiDAR sensors in over 13 000 forest trees in southern Finland. We focused on the commercially important species. Our aims were to 1) explore the relevant LiDAR features and study their dependencies on stand and tree variables, 2) examine two sensors and their fusion, 3) quantify the gain from intensity normalizations, 4) examine the importance of the size of the training set, and 5) determine the effects of stand age and site fertility. A set of 570 semiurban broad-leaved trees and exotic conifers was analyzed to 6) examine the LiDAR signal in the economically less important species. An accuracy of 88 90% was achieved in the classification of Scots pine, Norway spruce, and birch, using intensity variables. Spruce and birch showed the highest levels of confusion. Downsizing the training set from 30% to 2.5% of all trees had only a marginal effect on the performance of classifiers. The intensity features were dependent on the absolute and relative sizes of trees, especially for birch. The results suggest that leaf size, orientation, and foliage density affect the intensity, which is thus not affected by reflectance only. Some of the ecologically important species in Finland may be separable, since they gave rise to high intensity values. Comparison of the sensors implies that performance of the intensity data for species classification varies between sensors for reasons that remained uncertain. Both range and gain receiver normalization improved species classification. Weighting of the intensity values improved the fusion of two LiDAR datasets.
  • Korpela, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ilkka.korpela@helsinki.fi (email)
  • Ørka, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O.Box 5003, NO-1432 Ås, Norway E-mail: hoo@nn.no
  • Maltamo, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
  • Tokola, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tt@nn.fi
  • Hyyppä, Finnish Geodetic Institute, Department of Photogrammetry and Remote Sensing, P.O.Box 15, FI-02431 Masala, Finland E-mail: jh@nn.fi

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