Mapping Three-Dimensional Structures of Forest Canopy Using UAV Stereo Imagery: Evaluating Impacts of Forward Overlaps and Image Resolutions With LiDAR Data as Reference

The application of aerial stereo imagery on the measurement of forest three-dimensional structures is growing in recent years due to the rapid development of unmanned aerial vehicle platforms and automatic processing algorithms of stereo images. Yet there is still no clear knowledge about how the description of forest three-dimensional structures is affected by settings of critical acquisition parameters of stereo images. This study systematically addressed the impacts of image resolutions and forward overlaps over a broad range by using LiDAR data as reference. The different combinations of image resolutions and forward overlaps used in this study are produced by image average downsampling and subsetting. Their performances were evaluated from four aspects, including computation loads, point densities, estimation of canopy height indices at forest stand level, and the vertical distribution of point clouds over forest stands and along forest transects with different levels of canopy closure. Results showed that the coupling between image resolutions and forward overlaps in the data processing should be given full consideration. Generally, finer image resolutions require higher forward overlaps; otherwise, much of the area could not be detected and sparse trees were easily missed. It was a better choice to degrade image resolutions while keeping forward overlap in data processing if more blank areas appeared or sparse trees could not be detected. The good match between image resolutions and forward overlaps could dramatically reduce the computation load while keeping the estimation accuracy.