Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables

S. Tuominen (Corresponding Author), A. Balazs, E. Honkavaara, I. Pölönen, H. Saari, T. Hakala, N. Viljanen

Research output: Contribution to journalArticleScientificpeer-review

9 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number7721
JournalSilva Fennica
Volume51
Issue number5
DOIs
Publication statusPublished - 1 Jan 2017
MoE publication typeA1 Journal article-refereed

Fingerprint

forest stands
imagery
canopy
hyperspectral imagery
remote sensing
photogrammetry
estimation method
reflectance
cameras
laser
sensors (equipment)
lasers
unmanned aerial vehicles
vehicle
Picea
sensor
weather
Pinus
testing
sampling

Keywords

  • aerial imagery
  • digital photogrammetry
  • forest inventory
  • hyperspectral imaging
  • radiometric calibration
  • stereo-photogrammetric canopy modelling
  • UAVs

Cite this

Tuominen, S., Balazs, A., Honkavaara, E., Pölönen, I., Saari, H., Hakala, T., & Viljanen, N. (2017). Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fennica, 51(5), [7721]. https://doi.org/10.14214/sf.7721
Tuominen, S. ; Balazs, A. ; Honkavaara, E. ; Pölönen, I. ; Saari, H. ; Hakala, T. ; Viljanen, N. / Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables. In: Silva Fennica. 2017 ; Vol. 51, No. 5.
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abstract = "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.",
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Tuominen, S, Balazs, A, Honkavaara, E, Pölönen, I, Saari, H, Hakala, T & Viljanen, N 2017, 'Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables', Silva Fennica, vol. 51, no. 5, 7721. https://doi.org/10.14214/sf.7721

Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables. / Tuominen, S. (Corresponding Author); Balazs, A.; Honkavaara, E.; Pölönen, I.; Saari, H.; Hakala, T.; Viljanen, N.

In: Silva Fennica, Vol. 51, No. 5, 7721, 01.01.2017.

Research output: Contribution to journalArticleScientificpeer-review

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AU - Tuominen, S.

AU - Balazs, A.

AU - Honkavaara, E.

AU - Pölönen, I.

AU - Saari, H.

AU - Hakala, T.

AU - Viljanen, N.

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KW - digital photogrammetry

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KW - radiometric calibration

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