Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables
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 relate...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Finnish Society of Forest Science
2017-01-01
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| Series: | Silva Fennica |
| Online Access: | https://www.silvafennica.fi/article/7721 |
| _version_ | 1811295874803826688 |
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| author | Tuominen, Sakari Balazs, Andras Honkavaara, Eija Pölönen, Ilkka Saari, Heikki Hakala, Teemu Viljanen, Niko |
| author_facet | Tuominen, Sakari Balazs, Andras Honkavaara, Eija Pölönen, Ilkka Saari, Heikki Hakala, Teemu Viljanen, Niko |
| author_sort | Tuominen, Sakari |
| collection | DOAJ |
| description | 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 -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.k |
| first_indexed | 2024-04-13T05:40:03Z |
| format | Article |
| id | doaj.art-11cf487f2dcf47da9528bd9cee286ea0 |
| institution | Directory Open Access Journal |
| issn | 2242-4075 |
| language | English |
| last_indexed | 2024-04-13T05:40:03Z |
| publishDate | 2017-01-01 |
| publisher | Finnish Society of Forest Science |
| record_format | Article |
| series | Silva Fennica |
| spelling | doaj.art-11cf487f2dcf47da9528bd9cee286ea02022-12-22T03:00:08ZengFinnish Society of Forest ScienceSilva Fennica2242-40752017-01-0151510.14214/sf.7721Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variablesTuominen, SakariBalazs, AndrasHonkavaara, EijaPölönen, IlkkaSaari, HeikkiHakala, TeemuViljanen, NikoRemote 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 -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.khttps://www.silvafennica.fi/article/7721 |
| spellingShingle | Tuominen, Sakari Balazs, Andras Honkavaara, Eija Pölönen, Ilkka Saari, Heikki Hakala, Teemu Viljanen, Niko Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables Silva Fennica |
| title | Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables |
| title_full | Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables |
| title_fullStr | Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables |
| title_full_unstemmed | Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables |
| title_short | Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables |
| title_sort | hyperspectral uav imagery and photogrammetric canopy height model in estimating forest stand variables |
| url | https://www.silvafennica.fi/article/7721 |
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