Articles by Sakari Tuominen

In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0–20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

• Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: annika.kangas@luke.fi

The aim of this study was to estimate economic losses, which are caused by forest inventory errors of tree species proportions and site types. Our study data consisted of ground truth data and four sets of erroneous tree species proportions. They reflect the accuracy of tree species proportions in four remote sensing data sets, namely 1) airborne laser scanning (ALS) with 2D aerial image, 2) 2D aerial image, 3) 3D and 2D aerial image data together and 4) satellite data. Furthermore, our study data consisted of one simulated site type data set. We used the erroneous tree species proportions to optimise the timing of forest harvests and compared that to the true optimum obtained with ground truth data. According to the results, the mean losses of Net Present Value (NPV) because of erroneous tree species proportions at an interest rate of 3% varied from 124.4 € ha^–1 to 167.7 € ha^–1. The smallest losses were observed using tree species proportions predicted using ALS data and largest using satellite data. In those stands, respectively, in which tree species proportion errors actually caused economic losses, they were 468 € ha^–1 on average with tree species proportions based on ALS data. In turn, site type errors caused only small losses. Based on this study, accurate tree species identification seems to be very important with respect to operational forest inventory.

• Haara, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: arto.haara@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
• Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 2, FI-00791 Helsinki, Finland https://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi

Remote sensing using unmanned aerial vehicle (UAV) -borne sensors is currently a highly interesting approach for the estimation of forest characteristics. 3D remote sensing data from airborne laser scanning or digital stereo photogrammetry enable highly accurate estimation of forest variables related to the volume of growing stock and dimension of the trees, whereas recognition of tree species dominance and proportion of different tree species has been a major complication in remote sensing-based estimation of stand variables. In this study the use of UAV-borne hyperspectral imagery was examined in combination with a high-resolution photogrammetric canopy height model in estimating forest variables of 298 sample plots. Data were captured from eleven separate test sites under weather conditions varying from sunny to cloudy and partially cloudy. Both calibrated hyperspectral reflectance images and uncalibrated imagery were tested in combination with a canopy height model based on RGB camera imagery using the k-nearest neighbour estimation method. The results indicate that this data combination allows accurate estimation of stand volume, mean height and diameter: the best relative RMSE values for those variables were 22.7%, 7.4% and 14.7%, respectively. In estimating volume and dimension-related variables, the use of a calibrated image mosaic did not bring significant improvement in the results. In estimating the volumes of individual tree species, the use of calibrated hyperspectral imagery generally brought marked improvement in the estimation accuracy; the best relative RMSE values for the volumes for pine, spruce, larch and broadleaved trees were 34.5%, 57.2%, 45.7% and 42.0%, respectively.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 2, FI-00791 Helsinki, Finland http://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
• Honkavaara, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: eija.honkavaara@nls.fi
• Pölönen, University of Jyväskylä, Faculty of Information Technology, P.O. Box 35, FI-40014 Jyväskylä, Finland E-mail: ilkka.polonen@jyu.fi
• Saari, VTT Microelectronics, P.O. Box 1000, FI-02044 VTT, Finland E-mail: heikki.saari@vtt.fi
• Hakala, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: teemu.hakala@nls.fi
• Viljanen, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland E-mail: niko.viljanen@nls.fi

Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km². Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
• Pitkänen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: timo.p.pitkanen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
• Kangas, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: Annika.Kangas@luke.fi

In this paper we examine the feasibility of data from unmanned aerial vehicle (UAV)-borne aerial imagery in stand-level forest inventory. As airborne sensor platforms, UAVs offer advantages cost and flexibility over traditional manned aircraft in forest remote sensing applications in small areas, but they lack range and endurance in larger areas. On the other hand, advances in the processing of digital stereo photography make it possible to produce three-dimensional (3D) forest canopy data on the basis of images acquired using simple lightweight digital camera sensors. In this study, an aerial image orthomosaic and 3D photogrammetric canopy height data were derived from the images acquired by a UAV-borne camera sensor. Laser-based digital terrain model was applied for estimating ground elevation. Features extracted from orthoimages and 3D canopy height data were used to estimate forest variables of sample plots. K-nearest neighbor method was used in the estimation, and a genetic algorithm was applied for selecting an appropriate set of features for the estimation task. Among the selected features, 3D canopy features were given the greatest weight in the estimation supplemented by textural image features. Spectral aerial photograph features were given very low weight in the selected feature set. The accuracy of the forest estimates based on a combination of photogrammetric 3D data and orthoimagery from UAV-borne aerial imaging was at a similar level to those based on airborne laser scanning data and aerial imagery acquired using purpose-built aerial camera from the same study area.

• Tuominen, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@luke.fi
• Balazs, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: andras.balazs@luke.fi
• Saari, VTT Technical Research Centre of Finland Ltd, P.O. Box 1000, FI-02044 VTT, Finland E-mail: Heikki.Saari@vtt.fi
• Pölönen, University of Jyväskylä, Department of Mathematical Information Technology, P.O. Box 35, FI-40014 University of Jyväskylä, Finland E-mail: ilkka.polonen@jyu.fi
• Sarkeala, Mosaicmill Oy, Kultarikontie 1, FI-01300 Vantaa, Finland E-mail: janne.sarkeala@mosaicmill.com
• Viitala, Häme University of Applied Sciences (HAMK), P.O. Box 230, FI-13101 Hämeenlinna, Finland E-mail: Risto.Viitala@hamk.fi

• Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
• Pitkänen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: juho.pitkanen@metla.fi
• Balazs, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: andras.balazs@metla.fi
• Korhonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: kari.t.korhonen@metla.fi
• Hyvönen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: pekka.hyvonen@metla.fi
• Muinonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: eero.muinonen@metla.fi

• Tuominen, Finnish Forest Research Institute, P.O.Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
• Haapanen, Finnish Forest Research Institute, P.O.Box 18, FI-01301 Vantaa, Finland E-mail: reija.haapanen@gmail.com

• Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
• Eerikäinen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ke@nn.fi
• Schibalski, University of Potsdam, Karl-Liebknecht-Strasse 24–25, 14476 Potsdam, Germany E-mail: as@nn.de
• Haakana, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: mh@nn.fi
• Lehtonen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: al@nn.fi

• Tuominen, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: sakari.tuominen@metla.fi
• Haakana, Finnish Forest Research Institute, Unioninkatu 40 A, FI-00170 Helsinki, Finland E-mail: mh@nn.fi

• Tuominen, Finnish Forest Research Institute, Unioninkatu 40 A, FIN-00170 Helsinki, Finland E-mail: sakari.tuominen@metla.fi
• Poso, Department of Forest Resource Management, P.O. Box 27, FIN-00014 University of Helsinki, Finland E-mail: sp@nn.fi

• Poso, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: simo.poso@helsinki.fi
• Wang, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: gw@nn.fi
• Tuominen, Department of Forest Resource Management, P.O. Box 24 (Unioninkatu 40 B), FIN-00014 University of Helsinki, Finland E-mail: st@nn.fi

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 https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@helsinki.fi
• 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 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 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 https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi