Abstract
Terrestrial laser scanning (TLS) provides an accurate means of analyzing individual tree attributes, and can be extended to plots using multiple TLS scans. However, multiple TLS scans may reduce the effectiveness of individual tree structure quantification, often due to understory occupation, mutual tree occlusion, and other influences. The procedure to delineate accurate tree attributes from plot scans involves onerous steps and automated integration is challenging in the literature. This study proposes a fully automatic approach composed of ground filtering, stem detection, and stem form extraction algorithms, with emphasis on accuracy and feasibility. The delineated attributes can be useful to analyze terrain, tree biomass and fiber quality. The automation was experimented on a mature pine plot in Finland with both single scan (SS) and multiple scans (MS) datasets. With mensuration as reference, digital terrain models (DTM), stem locations, diameters at breast height (DBHs), stem heights, and stem forms of the whole plot were extracted and validated. Results of this study were best using the multiple scans (MS) dataset, where 76% of stems were detected (n = 49). Height extraction accuracy was 0.68 (r2) and 1.7 m (RMSE), and DBH extraction accuracy was 0.97 (r2) and 0.90 cm (RMSE). Height-wise stem diameter extraction accuracy was 0.76 (r2) and 2.4 cm (RMSE).
Highlights
Decision making in forestry is traditionally dependent on forest inventories, or the means and totals of forest characteristics within a defined area [1,2]
Decisions about habitat conservation and carbon balance are grounded by inventory attributes such as tree height, diameter at breast height (DBH), tree volume, age, and biomass [3,4,5]
The point color is rendered by the distance between extracted digital terrain models (DTM) point and its nearest point in reference DTM
Summary
Decision making in forestry is traditionally dependent on forest inventories, or the means and totals of forest characteristics within a defined area [1,2]. Mammoth delineation efforts have been made, generally to conform to two purposes: maximizing delineation accuracy and minimizing manual cost For the former purpose, in-situ surveying of individual trees is usually necessary as a reliable and accurate quantification of inventory attributes such as species, DBH, basal area, tree height, and biomass. Based on a control survey, Berger et al [12] points out that the Australian national forest inventories of conifers contains an average of 1.1% relative standard deviation in DBH measurement and 3.3% in height measurement. To fulfill the latter purpose, sampling strategies such as stratified sampling, plot sampling, and transection are adopted
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