Derivation of diameter at breast height (DBH) and other forest parameters using the cast shadow of trees within UAV images

Kurzfassung

The DBH is an important forest parameter used for estimating wood supply, biomass and stem growing rates among others and therefore of great interest as input data in areas such as forest economy and inventory, ecological monitoring and climate research. Traditionally this data is collected within regular time intervals during field campaigns measuring the DBH manually with calipers. A sample of trees is generally selected for data collection, information about the whole forest stand is obtained by extrapolation. This method is time-consuming and tend to be accompanied by great uncertainties as the sample might not be representative for a heterogenous forest stand. On the other hand, LiDAR techniques as terrestrial laser scanning (TLS) have been shown to provide high-resolution data for deriving forest parameters with good accuracies. Nevertheless, the use of TLS is not feasible for large areas and as the equipment is cost-intensive it is not accessible for every user. Unnamed aerial vehicles (UAV) as a cost-efficient method for deriving different kind of forest parameters are limited in their use for analyzing vertical forest structures, as stems, which results from the nadir flight pattern of UAVs from above the canopy. UAV-derived point clouds for example generally do not provide detailed information about the stems. Within this study the mentioned limitation of UAV data should be overcome by focusing on the cast shadows of tree stems. Data is acquired over a deciduous forest stand near Jena, Germany, during leave-off state and sunny weather conditions - two prerequisites for data collection to detect the shadow on the ground. Using structure-from-motion (SfM) workflows a point cloud is generated from the captured UAV images and normalized with respect to the relief. Points belonging to tree crowns and stems are removed generating an orthomosaic containing only ground information. In a second step methods from the field of deep learning and object-based image analysis (OBIA) are tested to achieve an automatic detection and delineation of cast shadows. As the form of the cast shadow and of the stem are correlated parameters such as DBH can be derived from the detected shadows.