Current issue: 53(3)

Under compilation: 53(4)

Impact factor 1.683
5-year impact factor 1.950
Silva Fennica 1926-1997
1990-1997
1980-1989
1970-1979
1960-1969
Acta Forestalia Fennica
1953-1968
1933-1952
1913-1932

Articles containing the keyword 'photogrammetry'.

Category: Research article

article id 7721, category Research article
Sakari Tuominen, Andras Balazs, Eija Honkavaara, Ilkka Pölönen, Heikki Saari, Teemu Hakala, Niko Viljanen. (2017). Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fennica vol. 51 no. 5 article id 7721. https://doi.org/10.14214/sf.7721
Highlights: Hyperspectral imagery and photogrammetric 3D point cloud based on RGB imagery were acquired under weather conditions changing from cloudy to sunny; Calibration of hyperspectral imagery was required for compensating the effect of varying weather conditions; The combination of hyperspectral imagery and photogrammetric point cloud data resulted in accurate forest estimates, especially for volumes per tree species.

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 ORCID ID: http://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi (email)
  • Balazs, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 2, FI-00791 Helsinki, Finland ORCID ID:E-mail: andras.balazs@luke.fi
  • Honkavaara, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland ORCID ID:E-mail: eija.honkavaara@nls.fi
  • Pölönen, University of Jyväskylä, Faculty of Information Technology, P.O. Box 35, FI-40014 Jyväskylä, Finland ORCID ID:E-mail: ilkka.polonen@jyu.fi
  • Saari, VTT Microelectronics, P.O. Box 1000, FI-02044 VTT, Finland ORCID ID:E-mail: heikki.saari@vtt.fi
  • Hakala, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland ORCID ID:E-mail: teemu.hakala@nls.fi
  • Viljanen, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02430 Masala, Finland ORCID ID:E-mail: niko.viljanen@nls.fi
article id 7743, category Research article
Sakari Tuominen, Timo Pitkänen, Andras Balazs, Annika Kangas. (2017). Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging. Silva Fennica vol. 51 no. 4 article id 7743. https://doi.org/10.14214/sf.7743
Highlights: 3D aerial imaging provides a feasible method for estimating forest variables in the form of thematic maps in large area inventories; Photogrammetric 3D data based on aerial imagery that was originally acquired for orthomosaic production was tested in estimating stand variables; Photogrammetric 3D data highly improved the accuracy of forest estimates compared to traditional 2D remote sensing imagery.

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 ORCID ID:E-mail: sakari.tuominen@luke.fi (email)
  • Pitkänen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland ORCID ID:E-mail: timo.p.pitkanen@luke.fi
  • Balazs, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland ORCID ID:E-mail: andras.balazs@luke.fi
  • Kangas, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: Annika.Kangas@luke.fi
article id 2021, category Research article
Jonas Bohlin, Inka Bohlin, Jonas Jonzén, Mats Nilsson. (2017). Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory. Silva Fennica vol. 51 no. 2 article id 2021. https://doi.org/10.14214/sf.2021
Highlights: Image based forest attribute map generated using NFI plots show similar accuracy as currently used LiDAR based forest attribute map; Also similar accuracies were found for different forest types; Aerial images from leaf-off season is not recommended.

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 ORCID ID: http://orcid.org/0000-0002-3318-5967 E-mail: jonas.bohlin@slu.se (email)
  • Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden ORCID ID: 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 ORCID ID:E-mail: jonas.jonzen@slu.se
  • Nilsson, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden ORCID ID: http://orcid.org/0000-0001-7394-6305 E-mail: mats.nilsson@slu.se
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 355, category Research article
Ilkka Korpela. (2006). Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment. Silva Fennica vol. 40 no. 1 article id 355. https://doi.org/10.14214/sf.355
Reconstructing three-dimensional structural changes in the forest over time is possible using archived aerial photographs and photogrammetric techniques, which have recently been introduced to a larger audience with the advent of digital photogrammetry. This paper explores the feasibility of constructing an accurate time-series of archived aerial photographs spanning 42 years using different types of geometric data and estimation methods for image orientation. A recent airborne laser scanning (lidar) data set was combined with the image block and assessed for geometric match. The results suggest that it is possible to establish the multitemporal geometry of an image block to an accuracy that is better than 0.5 m in 3D and constant over time. Even geodetic ground control points can be omitted from the estimation if the most recent images have accurate direct sensor orientation, which is becoming a standard technique in aerial photography. This greatly reduces the costs and facilitates the work. An accurate multitemporal image block combined with recent lidar scanning for the estimation of topography allows accurate monitoring and retrospective analysis of forest vegetation and management operations.
  • Korpela, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: ilkka.korpela@helsinki.fi (email)

Register
Click this link to register for Silva Fennica submission and tracking system.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content
Your selected articles

Committee on Publication Ethics A Trusted Community-Governed Archive