UAV-Based Photogrammetric Tree Height Measurement for Intensive Forest Monitoring

Abstract

The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.

Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

“…Standardization and benchmarking studies focusing on data acquisition parameters are crucial to detailing best practice approaches [17]. Furthermore, one aspect common among most studies is that they implement traditional field-based measurements for validation [18]. These field surveys have also measurement errors, but the level of error is often not declared [19].…”

“…These field surveys have also measurement errors, but the level of error is often not declared [19]. Until now, only a few studies carried out direct measurements of the length of tree trunks to verify remote sensing tree height estimates [18]. Ref.…”

Impact of UAS Image Orientation on Accuracy of Forest Inventory Attributes

“…To date, a number of recent studies have confirmed the great potential of UAS photogrammetry in forest inventory, at both individual tree- [16,18] and plot-levels [14,[40][41][42][43][44][45]. In general, the quality of the obtained forest inventory data depends on the quality and accuracy of the UAS products (e.g., PCs, DSMs) and DTM, while the quality of the UAS products depends on the image block orientation method applied to UAV data, among other factors such as flight characteristics and target properties [62].…”

Aerial and Terrestrial Photogrammetric Point Cloud Fusion for Intensive Forest Monitoring

“…The stand consisted of 286 trees which had an average height of 17.5 m. The understorey is comprised of young European Beech (Fagus sylvatica) and Sessile Oak (Quercus petraea) sparsely dispersed throughout the stand. More information about the Britz research station can be found in Krause, Sanders, Mund, & Greve, 2019;and Krause, Strer, Mund, & Sanders, 2019.…”