Estimation of Forest Structural Attributes Using Spectral Indices and Point Clouds from UAS-Based Multispectral and RGB Imageries

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2019-04, Vol.11 (7), p.800
• Main Authors: Shen, Xin, Cao, Lin, Yang, Bisheng, Xu, Zhong, Wang, Guibin
• Format: Article
• Language: English
• Subjects: Accuracy; Aerial photography; Biodiversity; Clouds; Density; Ecosystems; Forest ecosystems; Forest growth; forest structural attributes; Forestry; Growth models; multispectral imagery; Parameterization; Photogrammetry; point cloud; Regression analysis; Regression models; Remote sensing; Root-mean-square errors; Spectra; Standard deviation; Terrestrial ecosystems; Three dimensional models; Trees; Tropical forests; UAS platform; Unmanned aerial vehicles; Vegetation; Visual effects
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11070800

Forest structural attributes are key indicators for parameterization of forest growth models, which play key roles in understanding the biophysical processes and function of the forest ecosystem. In this study, UAS-based multispectral and RGB imageries were used to estimate forest structural attributes in planted subtropical forests. The point clouds were generated from multispectral and RGB imageries using the digital aerial photogrammetry (DAP) approach. Different suits of spectral and structural metrics (i.e., wide-band spectral indices and point cloud metrics) derived from multispectral and RGB imageries were compared and assessed. The selected spectral and structural metrics were used to fit partial least squares (PLS) regression models individually and in combination to estimate forest structural attributes (i.e., Lorey’s mean height (HL) and volume(V)), and the capabilities of multispectral- and RGB-derived spectral and structural metrics in predicting forest structural attributes in various stem density forests were assessed and compared. The results indicated that the derived DAP point clouds had perfect visual effects and that most of the structural metrics extracted from the multispectral DAP point cloud were highly correlated with the metrics derived from the RGB DAP point cloud (R2 > 0.75). Although the models including only spectral indices had the capability to predict forest structural attributes with relatively high accuracies (R2 = 0.56–0.69, relative Root-Mean-Square-Error (RMSE) = 10.88–21.92%), the models with spectral and structural metrics had higher accuracies (R2 = 0.82–0.93, relative RMSE = 4.60–14.17%). Moreover, the models fitted using multispectral- and RGB-derived metrics had similar accuracies (∆R2 = 0–0.02, ∆ relative RMSE = 0.18–0.44%). In addition, the combo models fitted with stratified sample plots had relatively higher accuracies than those fitted with all of the sample plots (∆R2 = 0–0.07, ∆ relative RMSE = 0.49–3.08%), and the accuracies increased with increasing stem density.