Evaluation of pushbroom DAP relative to frame camera DAP and lidar for forest modeling

Abstract

There is growing interest in using Digital Aerial Photogrammetry (DAP) for forestry applications. However, the performance of pushbroom DAP relative to frame-based DAP and airborne lidar is not well documented. Interest in DAP stems largely from its low cost relative to lidar. Studies have demonstrated that frame-based DAP generally performs slightly poorer than lidar, but still provides good value due to its reduced cost. In the USA pushbroom imagery can be dramatically less expensive than frame-camera imagery in part because of a nationwide collection program. There is an immediate need then to understand how well pushbroom DAP works as an auxiliary data source in the prediction of key forest attributes including basal area, volume, height, and the number of trees per ha.This study compares point clouds generated from 40 cm pushbroom DAP with point clouds from lidar and 7.5 cm, 15 cm, and 30 cm frame-based DAP. Differences in point clouds from these data sources are readily apparent in visual inspections; e.g. DAP tends to measure canopy gaps poorly, omit individual trees in openings, is typically unable to represent the ground beneath canopy, and is susceptible to commission errors manifested as points above the canopy surface. Frame-based DAP provides greater canopy detail than pushbroom DAP, which becomes more apparent with higher image resolution. Our results indicated that DAP height metrics generally have a strong linear relationship with lidar metrics, with R2 values ranging from 83 – 90% for cover, and 47–80% for height quantiles. Similarly, lidar auxiliary variables explain the greatest variation in forest attributes, e.g., volume (84%), followed closely by 30 cm frame-based DAP (81%), with the poorest results from pushbroom DAP (75%). While DAP resolution had a visible effect on canopy definition, it did not appreciably affect point cloud metrics or model performances. Although pushbroom DAP explained the least variation in forest attributes, it still had sufficient explanatory power to provide good value when frame-based DAP and lidar are not available.