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

Abstract

Hakala T., Viljanen N. (2017). Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fennica vol. 51 no. 5 article id 7721. 21 p. 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. AbstractRemote 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.

Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

“…The method was developed in a previous study [12,32], and it has been used to correct datasets captured in various environmental remote sensing applications [12,[33][34][35][36]. In this study, we implemented and assessed the combined adjustment model including the absolute calibration using reflectance panels and option to process images captured with different solar elevations simultaneously.…”

“…The image-wise relative correction parameters (a rel ) have been significant in the studies carried out under varying weather conditions [12,[34][35][36], while when capturing images in sunny conditions, the BRDF corrections have been significant [12,33]. The importance of utilizing the onboard irradiance recordings were shown in particular in the investigation concerning tree species classification and estimating forest stand variables, where the datasets were captured over a test area covering over 10 km during two days under highly variable weather conditions [35,36]. Comparisons of analysis with and without radiometric correction have shown that the radiometric correction improved the estimation results [12,36].…”

“…In recent studies, the radiometric correction of UAV imagery and spectrometry has mostly emphasized the atmospheric correction using reflectance panels [6,[12][13][14][15][31][32][33][34][35][36][37], atmospheric radiative transfer modeling [2,4] or normalization of UAV based radiance observations using field based radiance observations of a reference panel or irradiance [7,8,31]. Aasen et al [13] and Yang et al [15] calibrated the Cubert UHD 185-Firefly hyperspectral imagery; they emphasized the sensor calibration and performed reflectance calibration using single or multiple reflectance panels.…”

“…These studies have reported about challenges related to the irradiance sensor calibration and tilting during the flights. Disturbances due to the object reflectance anisotropy have been corrected from 2D frame format images using statistical approaches, such as dodging [37,38], or using the model based BRDF correction [12,[32][33][34][35][36]. Jakob et al [14] presented a processing toolbox for 2D frame format images suitable for a mine exploration environment; the radiometric corrections included the sensor corrections as well as the topographic correction that accounted for the influences of terrain topography; the reflectance transformation was carried out using the empirical line method.…”

“…Similar approaches have previously been used with aircraft images [39][40][41][42]. We have used the radiometric block adjustment method already in various applications, including for agricultural and forest use [12,[32][33][34][35][36]. In those studies, the weather included different combinations of sunny, cloudy or variable conditions.…”