Current issue: 55(3)

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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles by Hans Ole Ørka

Category: Research article

article id 10272, category Research article
Ana de Lera Garrido, Terje Gobakken, Hans O. 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
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.

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 ORCID ID: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 ORCID ID: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 ORCID ID: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 ORCID ID: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 ORCID ID: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
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 ORCID ID: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 ORCID ID:E-mail:
  • Maltamo, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Tokola, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Hyyppä, Finnish Geodetic Institute, Department of Photogrammetry and Remote Sensing, P.O.Box 15, FI-02431 Masala, Finland ORCID ID:E-mail:

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