The results showed that correlations between the estimated and reference DBHs were very strong at the plot level (r=0.83-0.99, p>hard plots>easy plots<<. The estimates were then compared against the reference data obtained by field measurements from six forest sample plots. To this end, many algorithms (e.g., cylinder / circle/ellipse fitting) and machine learning models (e.g., random forest classifier) were used in the data processing stage for estimation of the tree diameter at breast height (DBH) and the number of trees. Here we present an automated approach for deriving key inventory data using the HMLS method in natural forest areas.
Besides, these studies consisted of numerous data processing steps, which were challenging when employed by manual means. However, most pilot studies were performed in domesticated landscapes, where the environmental settings were far from those presented by (near)natural forest ecosystems. Handheld mobile laser scanning (HMLS), in particular, has come into prominence as a cost-effective data collection method for forest inventories. Many dendrometric parameters have been estimated by light detection and ranging (LiDAR) technology over the last two decades.
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