Treetop detection and tree crown delineation are common tasks in forest-related studies since they are necessary steps for data analysis on an individual tree level. In recent years, related studies have concentrated on Machine Learning approaches for which training data are needed. The results of the methodology presented in this paper and applied to a freely available data basis can be used to train such Machine Learning algorithms.
The National Ecological Observatory Network (NEON) provides data products from 81 American field sites. This study evaluates the suitability of the NEON dataset – especially the Canopy Height Model (CHM) – for the automatic treetop detection and tree crown delineation in natural mixed forests that provide difficult real-world conditions. Both tasks are conducted exemplarily on two NEON field sites, BART and HARV. For comparison, data from a study area located in Meppen, Germany, is used.
The general workflow consists of three steps. First, the data is pre-processed by masking irrelevant pixels. Then, the treetops are detected with a method based on the Top-Hat by Reconstruction operation. Finally, the tree crowns are delineated with a region-growing segmentation method proposed by Dalponte and Coomes (2016). Both methods rely on tree height information to locate the treetops and extract the tree crown boundaries. Achieved results reveal that the NEON CHM is suitable for treetop detection. However, the CHM’s spatial resolution is too coarse for tree crown delineation, i.e. further data has to be considered for an accurate outcome.
