Characterizing Seedling Stands Using Leaf-off and Leaf-on Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle

• Main Authors: Mohammad Imangholiloo, Ninni Saarinen, Lauri Markelin, Tomi Rosnell, Roope Näsi, Teemu Hakala, Eija Honkavaara, Markus Holopainen, Juha Hyyppä, Mikko Vastaranta
• Format: Article
• Language: English
• Published: MDPI AG 2019-05-01
• Series: Forests
• Subjects: seedling stand inventorying; photogrammetric point clouds; hyperspectral imagery; unmanned aerial vehicles; leaf-off; leaf-on
• Online Access: https://www.mdpi.com/1999-4907/10/5/415

Seedling stands are mainly inventoried through field measurements, which are typically laborious, expensive and time-consuming due to high tree density and small tree size. In addition, operationally used sparse density airborne laser scanning (ALS) and aerial imagery data are not sufficiently accurate for inventorying seedling stands. The use of unmanned aerial vehicles (UAVs) for forestry applications is currently in high attention and in the midst of quick development and this technology could be used to make seedling stand management more efficient. This study was designed to investigate the use of UAV-based photogrammetric point clouds and hyperspectral imagery for characterizing seedling stands in leaf-off and leaf-on conditions. The focus was in retrieving tree density and the height in young seedling stands in the southern boreal forests of Finland. After creating the canopy height model from photogrammetric point clouds using national digital terrain model based on ALS, the watershed segmentation method was applied to delineate the tree canopy boundary at individual tree level. The segments were then used to extract tree heights and spectral information. Optimal bands for calculating vegetation indices were analysed and used for species classification using the random forest method. Tree density and the mean tree height of the total and spruce trees were then estimated at the plot level. The overall tree density was underestimated by 17.5% and 20.2% in leaf-off and leaf-on conditions with the relative root mean square error (relative RMSE) of 33.5% and 26.8%, respectively. Mean tree height was underestimated by 20.8% and 7.4% (relative RMSE of 23.0% and 11.5%, and RMSE of 0.57 m and 0.29 m) in leaf-off and leaf-on conditions, respectively. The leaf-on data outperformed the leaf-off data in the estimations. The results showed that UAV imagery hold potential for reliably characterizing seedling stands and to be used to supplement or replace the laborious field inventory methods.