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Articles by Markus Holopainen

Category: Research article

article id 1615, category Research article
Minna Blomqvist, Päivi Lyytikäinen-Saarenmaa, Tuula Kantola, Maiju Kosunen, Mervi Talvitie, Markus Holopainen. (2016). Impacts of natural enemies and stand characteristics on cocoon mortality of the pine sawfly Diprion pini in a Fennoscandian boreal forest. Silva Fennica vol. 50 no. 5 article id 1615. https://doi.org/10.14214/sf.1615
Highlights: Annual cocoon mortality caused by natural enemies varied between 66% and 80% during the six-year study period, most of it caused by the family Ichneumonidae; Basal area, and coverage of lichen (Lichenes) and lingonberry (Vaccinium vitis-idaea L.) best explained cocoon parasitism and predation; Combination of suitable stand characteristics, abiotic environmental factors, and incomplete control by natural enemies enabled pest species to extend its gradation phase.

We investigated the impact of natural enemies on the cocoon mortality of the common pine sawfly (Diprion pini L.) during a six-year period in eastern Finland. The enemies were classified into parasitoids (insect families Chalcidoidea, Ichneumonidae, and Tachinidae), and predators (birds, small mammals, and insect families Elateridae and Carabidae). The appearance of D. pini was estimated as the intensity of annual defoliation. The impact of stand characteristics on the performance of parasitoids and predators was also investigated. Influence of the natural enemy complex on cocoon mortality of D. pini was nearly stable, but defoliation intensity slowly declined towards the end of the study period. Annual cocoon mortality by natural enemies varied between 66% and 80%. Our results verified that the most significant mortality factors were ichneumonid parasitoids and small mammals. Random Forest classification indicated that stand characteristics, such as basal area, and coverage of lichen (Lichenes) and lingonberry (Vaccinium vitis-idaea L.) affected the performance of parasites and predators. We suggest that a combination of optimal stand characteristics, abiotic environmental factors and mild to moderate control by natural enemies acted as drivers, which drove the pine sawfly population to extended gradation. For future forest health management, detailed information on abiotic and biotic regulating factors, along with long-term monitoring campaigns for conifer sawflies are needed to adapt Fennoscandian forests to altered climatic and silvicultural conditions.

  • Blomqvist, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID: http://orcid.org/0000-0003-2328-8839 E-mail: minna.blomqvist@helsinki.fi (email)
  • Lyytikäinen-Saarenmaa, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail: paivi.lyytikainen-saarenmaa@helsinki.fi
  • Kantola, Knowledge Engineering Laboratory, Department of Entomology, Texas A&M University, College Station, TX 77843-2475, USA ORCID ID:E-mail: tuula.kantola@helsinki.fi
  • Kosunen, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail: maiju.kosunen@helsinki.fi
  • Talvitie, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail: mervi.talvitie@dnainternet.net
  • Holopainen, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail: markus.holopainen@helsinki.fi
article id 1218, category Research article
Mikko Niemi, Mikko Vastaranta, Jussi Peuhkurinen, Markus Holopainen. (2015). Forest inventory attribute prediction using airborne laser scanning in low-productive forestry-drained boreal peatlands. Silva Fennica vol. 49 no. 2 article id 1218. https://doi.org/10.14214/sf.1218
Highlights: Following current forest inventory practises, stem volume was predicted in low-productive drained peatlands (LPDPs) with a root mean square error (RMSE) of 13.7 m3 ha–1; When 30 reference plots measured from LPDPs were added to the prediction, RMSE was decreased to 10.0 m3 ha–1; Additional reference plots from LPDPs did not affect the forest inventory attribute predictions in productive forests.
Nearly 30% of Finland’s land area is covered by peatlands. In Northern parts of the country there is a significant amount of low-productive drained peatlands (LPDPs) where the average annual stem volume growth is less than 1 m3 ha–1. The re-use of LPDPs has been considered thoroughly since Finnish forest legislation was updated and the forest regeneration prerequisite was removed from LPDPs in January 2014. Currently, forestry is one of the re-use alternatives, thus detailed forest resource information is required for allocating activities. However, current forest inventory practices have not been evaluated for sparse growing stocks (e.g., LPDPs). The purpose of our study was to evaluate the suitability of airborne laser scanning (ALS) for mapping forest inventory attributes in LPDPs. We used ALS data with a density of 0.8 pulses per m2, 558 field-measured reference plots (500 from productive forests and 58 from LPDPs) and k nearest neighbour (k-NN) estimation. Our main aim was to study the sensitivity of predictions to the number of LPDP reference plots used in the k-NN estimation. When the reference data consisted of 500 plots from productive forest stands, the root mean square errors (RMSEs) for the prediction accuracy of Lorey’s height, basal area and stem volume were 1.4 m, 2.7 m2 ha–1 and 13.7 m3 ha–1 in LPDPs, respectively. When 30 additional reference plots were allocated to LPDPs, the respective RMSEs were 1.1 m, 1.7 m2 ha–1 and 10.0 m3 ha–1. Additional reference plot allocation did not affect the predictions in productive forest stands.
  • Niemi, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland ORCID ID:E-mail: mikko.t.niemi@helsinki.fi (email)
  • Vastaranta, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland ORCID ID:E-mail: mikko.vastaranta@helsinki.fi
  • Peuhkurinen, Arbonaut Oy Ltd., Latokartanontie 7 A, FI-00700, Finland ORCID ID:E-mail: jussi.peuhkurinen@arbonaut.com
  • Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland & Centre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, Geodeetinrinne 2, FI-02430, Finland ORCID ID:E-mail: markus.holopainen@helsinki.fi
article id 335, category Research article
Markus Holopainen, Mervi Talvitie. (2006). Effect of data acquisition accuracy on timing of stand harvests and expected net present value. Silva Fennica vol. 40 no. 3 article id 335. https://doi.org/10.14214/sf.335
Modern remote sensing provides cost-efficient spatial digital data that are more accurate than before. However, the influence of increased accuracy and cost-efficiency on simulations of forest management planning has not been evaluated. The aim of the present study was to analyse the effect of data acquisition accuracy on standwise forest inventory by comparing the accuracy and cost of traditional compartmentwise inventory methods with 2D and 3D measurements of digital aerial photographs and airborne laser scanning. Comparison was based on the expected net present value (NPV), i.e. economic losses that consisted of the inventory costs and incorrect timings of treatments. The reference data, totalling 700 ha, were measured from Central Park in the city of Helsinki, Finland. The data were simulated to final cut with a MOTTI simulator, which is a stand-level analysis tool that can be used to assess the effects of alternative forest management practices on growth and timber yield. The results showed that when inventory costs were not considered there were no significant differences between the expected NPV losses in 3D measurements of digital aerial photographs, laser scanning and the compartmentwise method. When inventory costs were taken into account, the compartmentwise method was still the most efficient inventory method in the study area. Forest inventories, however, are usually directed to larger areas when the costs per hectare of remote-sensing methods decrease. As a result of better accuracies, 3D and compartmentwise methods always produce better results than the 2D method when NPV losses are accounted. Simulations of this type are based on the accuracies and costs of the 3D data available today, assuming that the data can be used in tree-level measurements.
  • Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: markus.holopainen@helsinki.fi (email)
  • Talvitie, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
article id 354, category Research article
Mervi Talvitie, Olli Leino, Markus Holopainen. (2006). Inventory of sparse forest populations using adaptive cluster sampling. Silva Fennica vol. 40 no. 1 article id 354. https://doi.org/10.14214/sf.354
In many studies, adaptive cluster sampling (ACS) proved to be a powerful tool for assessing rare clustered populations that are difficult to estimate by means of conventional sampling methods. During 2002 and 2003, severe drought-caused damage was observed in the park forests of the City of Helsinki, Finland, especially in barren site pine and spruce stands. The aim of the present study was to examine sampling and measurement methods for assessing drought damage by analysing the effectiveness of ACS compared with simple random sampling (SRS). Horvitz-Thompson and Hansen-Hurwitz estimators of the ACS method were used for estimating the population mean and variance of the variable of interest. ACS was considerably more effective than SRS in assessing rare clustered populations such as those resulting from drought damage. The variances in the ACS methods were significantly smaller and the inventory efficiency in the field better than in SRS.
  • Talvitie, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: mervi.talvitie@helsinki.fi (email)
  • Leino, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
article id 682, category Research article
Guangxing Wang, Simo Poso, Mark-Leo Waite, Markus Holopainen. (1998). The use of digitized aerial photographs and local operation for classification of stand development classes. Silva Fennica vol. 32 no. 3 article id 682. https://doi.org/10.14214/sf.682
The increasing capacity of modern computers has created the opportunity to routinely process the very large data sets derived by digitizing aerial photographs. The very fine resolution of such data sets makes them better suited than satellite imagery for some applications; however, there may be problems in implementation resulting from variation in radial distortion and illumination across an aerial photograph. We investigated the feasibility of using local operators (e.g., non-overlapping moving window means and standard deviations) as auxiliary data for generating stand development classes via three steps: (i) derive 6 local operators intended to represent texture for a 16 by 16 m window corresponding to a forest inventory sampling unit, (ii) apply a calibration process (e.g., accounting for location relative to a photo's principal point and solar position) to these local operators, and (iii) apply the calibrated local operators to classify the forest for stand development. Results indicate that calibrated local operators significantly improve the classification compared to what is possible using uncalibrated local operators and satellite images.
  • Wang, Department of Natural Resources and Environmental Sciences, University of Illinois, Urbana, IL, USA ORCID ID:E-mail: wang12@staff2.cso.uiuc.edu (email)
  • Poso, Department of Forest Resource Management, P.O. Box 24, FIN-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Waite, Department of Forest Resource Management, P.O. Box 24, FIN-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Holopainen, Department of Forest Resource Management, P.O. Box 24, FIN-00014 University of Helsinki, Finland ORCID ID:E-mail:

Category: Research note

article id 9986, category Research note
Ninni Saarinen, Joanne C. White, Michael A. Wulder, Annika Kangas, Sakari Tuominen, Ville Kankare, Markus Holopainen, Juha Hyyppä, Mikko Vastaranta. (2018). Landsat archive holdings for Finland: opportunities for forest monitoring. Silva Fennica vol. 52 no. 3 article id 9986. https://doi.org/10.14214/sf.9986
Highlights: The 45-year Landsat archive contained 30 076 images for Finland by December 31, 2017; 16.3% of these were acquired within ±30 days of August 1 (northern hemisphere summer), have <70% cloud cover, and a 30 m spatial resolution; Using time series analyses, these data provide unique information that complements other datasets available for forest monitoring and assessment in Finland.

There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30 076 images (1972–2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 ±30 days, <70% cloud cover, 30 m spatial resolution). Data from the Landsat archive can augment forest monitoring efforts in Finland, provide new information for science and applications, and enable retrospective, systematic analyses to characterize the development of Finnish forests over the past three decades. The capacity to monitor trends based upon this multi-decadal record with the addition of new measurements is of benefit to multisource inventories and offers nationally comprehensive spatially-explicit datasets for a wide range of stakeholders and applications.

  • Saarinen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID: https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@helsinki.fi (email)
  • White, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada ORCID ID: http://orcid.org/0000-0003-4674-0373 E-mail: joanne.white@canada.ca
  • Wulder, Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada ORCID ID: https://orcid.org/0000-0002-6942-1896 E-mail: mike.wulder@canada.ca
  • Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland ORCID ID:E-mail: annika.kangas@luke.fi
  • Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland ORCID ID:E-mail: sakari.tuominen@luke.fi
  • Kankare, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: ville.kankare@helsinki.fi
  • Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: markus.holopainen@helsinki.fi
  • Hyyppä, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02431 Masala, Finland ORCID ID:E-mail: juha.hyyppa@nls.fi
  • Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID: https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi

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