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Articles by Pekka Hyvönen

Category: Research article

article id 7710, category Research article
Pekka Hyvönen, Jaakko Heinonen. (2018). Estimating storm damage with the help of low-altitude photographs and different sampling designs and estimators. Silva Fennica vol. 52 no. 3 article id 7710. https://doi.org/10.14214/sf.7710
Highlights: Digital photographs taken from low altitudes are usable for monitoring storm damage; Simple random sampling and ratio estimators resulted in similar standard errors; Characteristics of the storm influence the optimal flight plan and which variance estimator should be used; The developed model for simulations can be modified and utilized with future storms.

Climate change has been estimated to increase the risk of storm damage in forests in Finland. There is a growing need for methods to obtain information on the extent and severity of storm damage after a storm occurrence. The first objective of this study was to test whether digital photographs taken from aircrafts flying at low-altitude can be utilized in locating storm-damaged areas and estimating the need for harvesting of wind-thrown trees. The second objective was to test the performance of selected estimators. Depending on distances between flight lines, plots on lines and the used estimator, the relative standard errors of storm area estimates varied between 7.7 and 48.7%. For the area for harvesting and volume of wind-thrown trees, the relative standard errors of estimates varied between 16.8 and 167.3%. Using forest area information from Multisource National Forest Inventory data improved the accuracy of the estimates. However, performance of a simple random sampling estimator and ratio estimator were quite similar. Lindeberg’s method for variance estimation based on adjacent lines was sensitive to line directions in relation to possible trends in storm-damaged area locations. Our results showed that the tested method could be used in estimating storm-damaged area provided that the network of flight lines and photographs on lines are sufficiently dense. The developed model for simulations can be utilized also with forthcoming storms as model’s parameters can be freely adjusted to meet, e.g., the intensity and extent of the storm.

  • Hyvönen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland ORCID ID:E-mail: pekka.hyvonen@luke.fi (email)
  • Heinonen, ORCID ID:E-mail: jaakkoheinonen@gmail.com
article id 983, category Research article
Sakari Tuominen, Juho Pitkänen, Andras Balazs, Kari T. Korhonen, Pekka Hyvönen, Eero Muinonen. (2014). NFI plots as complementary reference data in forest inventory based on airborne laser scanning and aerial photography in Finland. Silva Fennica vol. 48 no. 2 article id 983. https://doi.org/10.14214/sf.983
Highlights: Using NFI plots in forest management inventories could provide a way for rationalising forest inventory data acquisition; NFI plots were used as additional reference data in laser scanning and aerial image based forest inventory; NFI plots improved the estimates of some forest variables; There are differences between the two inventory types that cause difficulties in combining the data.
In Finland, there are currently two, parallel sample-plot-based forest inventory systems, which differ in their methodologies, sampling designs, and objectives. One is the National Forest Inventory (NFI), aimed at unbiased inventory results at national and regional level. The other is the Forest Centre’s management-oriented forest inventory based on interpretation of airborne laser scanning and aerial images, with the aim of locally accurate stand-level forest estimates. The National Forest Inventory utilises relascope sample plots with systematic cluster sampling. This inventory method is optimised for accuracy of regional volume estimates. In contrast, the management-oriented forest inventory utilises circular sample plots with an allocation system covering certain pre-defined forest classes in the inventory area. This method is optimised to produce reference data for interpretation of the remote-sensing materials in use. In this study, we tested the feasibility of the National Forest Inventory sample plots in provision of additional reference data for the management-oriented inventory. Various combinations of NFI plots and management inventory plots were tested in the interpretation of the laser and aerial-image data. Adding NFI plots in the reference data generally improved the accuracy of the volume estimates by tree species but not the estimates of total volume or stand mean height and diameter. The difference between the plot types in the NFI and management inventories causes difficulties in combination of the two datasets.
  • Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: sakari.tuominen@metla.fi (email)
  • Pitkänen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: juho.pitkanen@metla.fi
  • Balazs, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: andras.balazs@metla.fi
  • Korhonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: kari.t.korhonen@metla.fi
  • Hyvönen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: pekka.hyvonen@metla.fi
  • Muinonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: eero.muinonen@metla.fi
article id 345, category Research article
Pekka Hyvönen, Perttu Anttila. (2006). Change detection in boreal forests using bi-temporal aerial photographs. Silva Fennica vol. 40 no. 2 article id 345. https://doi.org/10.14214/sf.345
Increased need for timely forest information is leading to continuous updating of stand databases. In continuous updating, stand attributes are estimated in the field after an operation and stored in databases. To find the changes caused by operations and forest damage, a semi-automatic method based on bi-temporal aerial photographs was developed. The test data were classified into three classes: No-change (952 stands), Moderate-change (163 stands) and Considerable-change (44 stands). The aerial photographs were acquired in years 2001 and 2004 with almost the same image specifications. Altogether 110 features at stand level were extracted and used in change detection analysis. The test data were classified with stepwise discriminant analysis. The overall accuracy of classification varied between 75.3 and 84.7%. The considerable changes were found without error, whereas the Moderate-change and No-change classes were often confused. However, 84.2% of thinned stands were classified correctly. The best accuracy in classification was obtained by using the histogram and textural features extracted from the original, uncorrected images. Radiometric correction did not improve the accuracy of classification. Soil type, characteristics of the growing stock and the location of a stand in an image were found to affect the change detection. Before the method can be applied operationally, issues related to, e.g., confusion between No-change and Moderate-change must be solved.
  • Hyvönen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: pekka.hyvonen@metla.fi (email)
  • Anttila, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:

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