Articles containing the keyword 'photogrammetry'

• Korpela, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0002-1665-3984 E-mail: ilkka.korpela@helsinki.fi
• Polvivaara, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: –
• Papunen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0001-5383-4314 E-mail: saija.papunen@outlook.com
• Jaakkola, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: laura.jaakkola@helsinki.fi
• Tienaho, University of Eastern Finland, Faculty of Science and Forestry, P.O. Box 111, FI-80101 Joensuu, Finland 0000-0002-6574-5797 E-mail: noora.tienaho@uef.fi
• Uotila, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: johannes.uotila@helsinki.fi
• Puputti, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0003-1972-1636 E-mail: tuomas.puputti@helsinki.fi
• Flyktman, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0002-5235-317X E-mail: aleksi.flyktman@helsinki.fi

In terms of assessing economic impact, one of the most important elements in the wood supply chain is the measurement of round wood. Besides the one-by-one measurement of logs, logs are often measured when stacked at the forest road. The gross stacked volume includes the volume of the wood, bark and airspace and is widely used for industrial wood assortments. The increasing international attention given to photo-optical measurement systems for portable devices is due to their simplicity of use and efficiency. The aim of this study was to compare the gross volumes of hardwood log stacks measured using one widespread photo-optical app with two manual section-wise volume estimations of log stacks based on the German framework agreement for timber trade (RVR). The manual volume estimations were done starting from the left (RVR_left) and right (RVR_right) sides of the log stacks. The results showed an average deviation of the photo-optical gross volume estimation in comparison to the manual estimation of –2.09% (RVR_left) and –3.66% (RVR_right) while the deviation between RVR_left and RVR_right was +2.54%. However, the log stack gross volume had a highly significant effect on the deviation and better accuracy with smaller deviation were reached for larger log stacks. Moreover, results indicated that the gross volume estimations of higher quality log stacks were closer for the three analyzed methods compared to estimations of poor-quality log stacks.

• Berendt, Department of Forest Utilization and Timber Markets, Eberswalde University for Sustainable Development, 16225 Eberswalde, Germany https://orcid.org/0000-0002-6285-7590 E-mail: ferreol.berendt@hnee.de
• Wolfgramm, Landesforst MV Anstalt des öffentlichen Rechts, Forstamt Billenhagen, 18182 Blankenhagen, Germany E-mail: felixwolfgramm@yahoo.de
• Cremer, Department of Forest Utilization and Timber Markets, Eberswalde University for Sustainable Development, 16225 Eberswalde, Germany E-mail: tobias.cremer@hnee.de

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

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

Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid- and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image- and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

• Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0002-3318-5967 E-mail: jonas.bohlin@slu.se
• Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0003-1224-6684 E-mail: inka.bohlin@slu.se
• Jonzén, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden E-mail: jonas.jonzen@slu.se
• Nilsson, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0001-7394-6305 E-mail: mats.nilsson@slu.se

• Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi
• Talvitie, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: mt@nn.fi

• Korpela, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ilkka.korpela@helsinki.fi