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Articles by Mikko Vastaranta

Category : Climate resilient and sustainable forest management – Research article

article id 23061, category Climate resilient and sustainable forest management – Research article
Noora Tienaho, Ninni Saarinen, Tuomas Yrttimaa, Ville Kankare, Mikko Vastaranta. (2024). Quantifying fire-induced changes in ground vegetation using bitemporal terrestrial laser scanning. Silva Fennica vol. 58 no. 3 article id 23061. https://doi.org/10.14214/sf.23061
Keywords: forest fires; biomass; boreal forest; LIDAR; controlled burning; surface differencing; surface fires
Highlights: Bitemporal terrestrial laser scanning provided a means for identifying surface areas exposed to fire by utilizing a surface differencing method developed in this study; The developed method allowed for the quantification of fire-induced volumetric changes in ground vegetation at high resolution, facilitating the assessment of the impact of surface fires on forest ecosystems.
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Forest fires pose a significant threat to forest carbon storage and sinks, yet they also play a crucial role in the natural dynamics of boreal forests. Accurate quantification of biomass changes resulting from forest fires is essential for damage assessment and controlled burning evaluation. This study utilized terrestrial laser scanning (TLS) to quantify changes in ground vegetation resulting from low-intensity surface fires. TLS data were collected before and after controlled burnings at eight one-hectare test sites in Scots pine (Pinus sylvestris L.) dominated boreal forests in Finland. A surface differencing-based method was developed to identify areas exposed to fire. Validation, based on visual interpretation of 1 × 1 m surface patches (n = 320), showed a recall, precision, and F1-score of 0.9 for the accuracy of identifying burned surfaces. The developed method allowed the assessment of the magnitude of fire-induced vegetation changes within the test sites. The proportions of burned 1 × 1 m areas within the test sites varied between 51–96%. Total volumetric change in ground vegetation was on average –1200 m³ ha-1, with burning reducing the vegetation volume by 1700 m³ ha-1 and vegetation growth increasing it by 500 m³ ha-1. Substantial variations in the volumetric changes within and between the test sites were detected, highlighting the complex dynamics of surface fires, and emphasizing the importance of having observations from multiple sites. This study demonstrates that bitemporal TLS measurements provide a robust means for characterizing fire-induced changes, facilitating the assessment of the impact of surface fires on forest ecosystems.

Category : Research article

article id 1568, category Research article
Jouni Siipilehto, Harri Lindeman, Mikko Vastaranta, Xiaowei Yu, Jori Uusitalo. (2016). Reliability of the predicted stand structure for clear-cut stands using optional methods: airborne laser scanning-based methods, smartphone-based forest inventory application Trestima and pre-harvest measurement tool EMO. Silva Fennica vol. 50 no. 3 article id 1568. https://doi.org/10.14214/sf.1568
Keywords: forest inventory; diameter distribution; Weibull; area-based approach; parameter recovery; k-NN estimation
Highlights: An airborne laser scanning grid-based approach for determining stand structure enabled bi- or multimodal predicted distributions that fitted well to the ground-truth harvester data; EMO and Trestima applications needed stand-specific inventory for sample measurements or sample photos, respectively, and at their best, provided superior accuracy for predicting certain stand characteristics.
Abstract | Full text in HTML | Full text in PDF | Author Info

Accurate timber assortment information is required before cuttings to optimize wood allocation and logging activities. Timber assortments can be derived from diameter-height distribution that is most often predicted from the stand characteristics provided by forest inventory. The aim of this study was to assess and compare the accuracy of three different pre-harvest inventory methods in predicting the structure of mainly Scots pine-dominated, clear-cut stands. The investigated methods were an area-based approach (ABA) based on airborne laser scanning data, the smartphone-based forest inventory Trestima app and the more conventional pre-harvest inventory method called EMO. The estimates of diameter-height distributions based on each method were compared to accurate tree taper data measured and registered by the harvester’s measurement systems during the final cut. According to our results, grid-level ABA and Trestima were generally the most accurate methods for predicting diameter-height distribution. ABA provides predictions for systematic 16 m × 16 m grids from which stand-wise characteristics are aggregated. In order to enable multimodal stand-wise distributions, distributions must be predicted for each grid cell and then aggregated for the stand level, instead of predicting a distribution from the aggregated stand-level characteristics. Trestima required a sufficient sample for reliable results. EMO provided accurate results for the dominating Scots pine but, it could not capture minor admixtures. ABA seemed rather trustworthy in predicting stand characteristics and diameter distribution of standing trees prior to harvesting. Therefore, if up-to-date ABA information is available, only limited benefits can be obtained from stand-specific inventory using Trestima or EMO in mature pine or spruce-dominated forests.

  • Siipilehto, Natural Research Institute Finland (Luke), Management and Production of Renewable Resources, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: jouni.siipilehto@luke.fi (email)
  • Lindeman,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano E-mail: harri.lindeman@luke.fi
  • Vastaranta, University of Helsinki, Department of Forest Sciences, P.O. Box 62 (Viikinkaari 11), FI-00014 University of Helsinki E-mail: mikko.vastaranta@helsinki.fi
  • Yu, Finnish Geospatial Research Institute (FGI), Department of Remote Sensing and Photogrammetry, National Land Survey of Finland, P.O. Box 15 (Geodeetinrinne 2), FI-02431, Masala, Finland E-mail: xiaowei.yu@maanmittauslaitos.fi
  • Uusitalo,  Natural Research Institute Finland, Green Technology, Kaironiementie 15, 39700 Parkano E-mail: jori.uusitalo@luke.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
Keywords: remote sensing; forest technology; forest management planning; mapping; k-NN estimation; random forests
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.
Abstract | Full text in HTML | Full text in PDF | Author Info
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 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 E-mail: mikko.vastaranta@helsinki.fi
  • Peuhkurinen, Arbonaut Oy Ltd., Latokartanontie 7 A, FI-00700, Finland 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 E-mail: markus.holopainen@helsinki.fi

Category : Research note

article id 10197, category Research note
Ville Kankare, Ville Luoma, Ninni Saarinen, Jussi Peuhkurinen, Markus Holopainen, Mikko Vastaranta. (2019). Assessing feasibility of the forest trafficability map for avoiding rutting – a case study. Silva Fennica vol. 53 no. 3 article id 10197. https://doi.org/10.14214/sf.10197
Keywords: remote sensing; open data; preharvest information; stand trafficability
Highlights: A static trafficability map was developed to provide information about suitable harvesting season; The majority (91.7%) of the evaluated thinning stands were harvested without causing rutting damage if operations were timed correctly in relation to the static trafficability map information; The static trafficability map provides reliable and slightly conservative estimation of the forest trafficability for supporting forest operations.
Abstract | Full text in HTML | Full text in PDF | Author Info

Information on forest trafficability (i.e. carrying capacity of the forest floor) is required before harvesting operations in Southern Boreal forest conditions. It describes the seasons when harvesting operations may take place without causing substantial damage to the forest soil using standard logging machinery. The available trafficability information have been based on subjective observations made during the wood procurement planning. For supporting forest operations, an open access map product has been developed to provide information on trafficability of forests. The forest stands are distributed into classes that characterize different harvesting seasons based on topographic wetness index, amount of vegetation, ground water height and ditch depth. The main goal of this case study was to evaluate the information of the static forest trafficability map in relation to the detected rutting within logging tracks measured in the field. The analysis concentrated on thinning stands since the effect of rutting is significant on the growth of the remaining trees. The results showed that the static trafficability map provided reliable and slightly conservative estimation of the forest trafficability. The majority (91.7%) of the evaluated stands were harvested without causing significant damage if harvesting was timed correctly compared to the trafficability information. However, it should be pointed out that the weather history at small scale, the skills of a driver, and effects of used machinery are not considered in the map product although they can have a considerable impact on the rutting.

  • Kankare, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland; Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland ORCID https://orcid.org/0000-0001-6038-1579 E-mail: ville.kankare@uef.fi (email)
  • Luoma, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland E-mail: ville.luoma@helsinki.fi
  • Saarinen, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland; School of Forest Sciences, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland E-mail: ninni.saarinen@helsinki.fi
  • Peuhkurinen, Arbonaut Oy, Malminkaari 13–19, FI-00700 Helsinki, Finland E-mail: jussi.peuhkurinen@arbonaut.com
  • Holopainen, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi
  • Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland E-mail: mikko.vastaranta@uef.fi
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
Keywords: National Forest Inventory; satellite; Landsat time series
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.
Abstract | Full text in HTML | Full text in PDF | Author Info

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 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 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 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 E-mail: annika.kangas@luke.fi
  • Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
  • Kankare, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ville.kankare@helsinki.fi
  • Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland 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 E-mail: juha.hyyppa@nls.fi
  • Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland ORCID https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi

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