Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables
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), Article 7721. https://doi.org/10.14214/sf.7721
Julkaistu sarjassa
Silva FennicaTekijät
Päivämäärä
2017Tekijänoikeudet
© the Authors, 2017. This is an open access article distributed under the terms of a Creative Commons License.
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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