Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions

Abstract

With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

“…However, in the context of CCF, and owing to occlusion, the precision drops off with smaller trees such as those from regeneration [50]. Below-canopy remote sensing techniques-such as TLS, MLS, and photogrammetry-are better suited to the accurate mapping of regeneration [59,72,[82][83][84], and it has been shown in irregular tropical forests that MLS can identify small-diameter understory trees with far greater geospatial positioning accuracy, 6 cm, than methods using aerial data, which had 6-m positioning error [91]. The development of tree detection algorithms for use with below-canopy point clouds is happening rapidly, and there now are several solutions available which can accurately locate, identify, and measure trees and saplings from point cloud data [95][96][97][98].…”