UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTS
Biodiversity is commonly referred to as species diversity but in forest ecosystems variability in structural and functional characteristics can also be treated as measures of biodiversity. Small unmanned aerial vehicles (UAVs) provide a means for characterizing forest ecosystem with high spatial res...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2017-10-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W3/171/2017/isprs-archives-XLII-3-W3-171-2017.pdf |
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| author | N. Saarinen N. Saarinen M. Vastaranta M. Vastaranta R. Näsi T. Rosnell T. Hakala E. Honkavaara M. A. Wulder V. Luoma V. Luoma A. M. G. Tommaselli N. N. Imai E. A. W. Ribeiro R. B. Guimarães M. Holopainen M. Holopainen J. Hyyppä J. Hyyppä |
| author_facet | N. Saarinen N. Saarinen M. Vastaranta M. Vastaranta R. Näsi T. Rosnell T. Hakala E. Honkavaara M. A. Wulder V. Luoma V. Luoma A. M. G. Tommaselli N. N. Imai E. A. W. Ribeiro R. B. Guimarães M. Holopainen M. Holopainen J. Hyyppä J. Hyyppä |
| author_sort | N. Saarinen |
| collection | DOAJ |
| description | Biodiversity is commonly referred to as species diversity but in forest ecosystems variability in structural and functional characteristics can also be treated as measures of biodiversity. Small unmanned aerial vehicles (UAVs) provide a means for characterizing forest ecosystem with high spatial resolution, permitting measuring physical characteristics of a forest ecosystem from a viewpoint of biodiversity. The objective of this study is to examine the applicability of photogrammetric point clouds and hyperspectral imaging acquired with a small UAV helicopter in mapping biodiversity indicators, such as structural complexity as well as the amount of deciduous and dead trees at plot level in southern boreal forests. Standard deviation of tree heights within a sample plot, used as a proxy for structural complexity, was the most accurately derived biodiversity indicator resulting in a mean error of 0.5 m, with a standard deviation of 0.9 m. The volume predictions for deciduous and dead trees were underestimated by 32.4 m<sup>3</sup>/ha and 1.7 m<sup>3</sup>/ha, respectively, with standard deviation of 50.2 m<sup>3</sup>/ha for deciduous and 3.2 m<sup>3</sup>/ha for dead trees. The spectral features describing brightness (i.e. higher reflectance values) were prevailing in feature selection but several wavelengths were represented. Thus, it can be concluded that structural complexity can be predicted reliably but at the same time can be expected to be underestimated with photogrammetric point clouds obtained with a small UAV. Additionally, plot-level volume of dead trees can be predicted with small mean error whereas identifying deciduous species was more challenging at plot level. |
| first_indexed | 2024-12-21T01:47:08Z |
| format | Article |
| id | doaj.art-556ba70c60c34033843c1c0a7504fb94 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-21T01:47:08Z |
| publishDate | 2017-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-556ba70c60c34033843c1c0a7504fb942022-12-21T19:19:59ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342017-10-01XLII-3-W317117510.5194/isprs-archives-XLII-3-W3-171-2017UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTSN. Saarinen0N. Saarinen1M. Vastaranta2M. Vastaranta3R. Näsi4T. Rosnell5T. Hakala6E. Honkavaara7M. A. Wulder8V. Luoma9V. Luoma10A. M. G. Tommaselli11N. N. Imai12E. A. W. Ribeiro13R. B. Guimarães14M. Holopainen15M. Holopainen16J. Hyyppä17J. Hyyppä18Dept. of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 University of Helsinki, FinlandCentre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 04310 Masala, FinlandDept. of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 University of Helsinki, FinlandCentre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 04310 Masala, FinlandDept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey, Geodeetinrinne 2, 04310 Masala, FinlandDept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey, Geodeetinrinne 2, 04310 Masala, FinlandDept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey, Geodeetinrinne 2, 04310 Masala, FinlandDept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey, Geodeetinrinne 2, 04310 Masala, FinlandPacific Forestry Centre, National Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, CanadaDept. of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 University of Helsinki, FinlandCentre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 04310 Masala, FinlandDept. of Cartography, São Paulo State University, Roberto Simonsen 305, 19060-900 Presidente Prudente, BrazilDept. of Cartography, São Paulo State University, Roberto Simonsen 305, 19060-900 Presidente Prudente, BrazilCatarinense Federal Institute, Rodovia Duque de Caxias – km 6 – s/n, 89240-000 São Francisco do Sul, BrazilDept. of Geography, São Paulo State University, Roberto Simonsen 305, 19060-900 Presidente Prudente, BrazilDept. of Forest Sciences, University of Helsinki, P.O. Box 27, 00014 University of Helsinki, FinlandCentre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 04310 Masala, FinlandDept. of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey, Geodeetinrinne 2, 04310 Masala, FinlandCentre of Excellence in Laser Scanning Research, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 04310 Masala, FinlandBiodiversity is commonly referred to as species diversity but in forest ecosystems variability in structural and functional characteristics can also be treated as measures of biodiversity. Small unmanned aerial vehicles (UAVs) provide a means for characterizing forest ecosystem with high spatial resolution, permitting measuring physical characteristics of a forest ecosystem from a viewpoint of biodiversity. The objective of this study is to examine the applicability of photogrammetric point clouds and hyperspectral imaging acquired with a small UAV helicopter in mapping biodiversity indicators, such as structural complexity as well as the amount of deciduous and dead trees at plot level in southern boreal forests. Standard deviation of tree heights within a sample plot, used as a proxy for structural complexity, was the most accurately derived biodiversity indicator resulting in a mean error of 0.5 m, with a standard deviation of 0.9 m. The volume predictions for deciduous and dead trees were underestimated by 32.4 m<sup>3</sup>/ha and 1.7 m<sup>3</sup>/ha, respectively, with standard deviation of 50.2 m<sup>3</sup>/ha for deciduous and 3.2 m<sup>3</sup>/ha for dead trees. The spectral features describing brightness (i.e. higher reflectance values) were prevailing in feature selection but several wavelengths were represented. Thus, it can be concluded that structural complexity can be predicted reliably but at the same time can be expected to be underestimated with photogrammetric point clouds obtained with a small UAV. Additionally, plot-level volume of dead trees can be predicted with small mean error whereas identifying deciduous species was more challenging at plot level.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W3/171/2017/isprs-archives-XLII-3-W3-171-2017.pdf |
| spellingShingle | N. Saarinen N. Saarinen M. Vastaranta M. Vastaranta R. Näsi T. Rosnell T. Hakala E. Honkavaara M. A. Wulder V. Luoma V. Luoma A. M. G. Tommaselli N. N. Imai E. A. W. Ribeiro R. B. Guimarães M. Holopainen M. Holopainen J. Hyyppä J. Hyyppä UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTS |
| title_full | UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTS |
| title_fullStr | UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTS |
| title_full_unstemmed | UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTS |
| title_short | UAV-BASED PHOTOGRAMMETRIC POINT CLOUDS AND HYPERSPECTRAL IMAGING FOR MAPPING BIODIVERSITY INDICATORS IN BOREAL FORESTS |
| title_sort | uav based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W3/171/2017/isprs-archives-XLII-3-W3-171-2017.pdf |
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