Abstract
This paper presents a fully automatic method addressing tree mapping and parameter extraction (tree position, stem diameter at breast height, stem curve, and tree height) from terrestrial laser scans in forest inventories. The algorithm is designed to detect trees of various sizes and architectures, produce smooth yet accurate stem curves, and achieve tree height estimates in multi-layered stands, all without employing constraints on the shape of the crown. The algorithm also aims to balance estimation accuracy and computational complexity. The method’s tree detection combines voxel operations and stem surface filtering based on scanning point density. Stem diameters are obtained by creating individual taper models, while tree heights are estimated from the segmentation of tree crowns in the voxel-space. Twenty-four sample plots representing diverse forest structures in the south boreal region of Finland have been assessed from single- and multiple terrestrial laser scans. The mean percentages of completeness in stem detection over all stand complexity categories are 50.9% and 68.5% from single and multiple scans, respectively, while the mean root mean square error (RMSE) of the stem curve estimates ranges from ±1.7 to ±2.3 cm, all of which demonstrates the robustness of the algorithm. Efforts were made to accurately locate tree tops by segmenting individual crowns. Nevertheless, with a mean bias of −2.9 m from single scans and −1.3 m from multiple scans, the algorithm proved conservative in tree height estimates.
Highlights
Data acquisition on forest resources is realized in national forest inventories, which establish a foundation for the planning and monitoring of sustainable forest management.Terrestrial laser scanning (TLS) or terrestrial light detection and ranging (LiDAR) is a promising, non-destructive technique to extract spatially explicit tree metrics for forest inventory purposes [1,2,3,4]
The European Spatial Data Research Organization (EuroSDR) launched the international project “Benchmarking on Terrestrial Laser Scanning for Forestry Applications” in order to evaluate the potential of applying TLS in characterizing forest sample plots and to reveal the capability of the recent algorithms for tree attribute extraction in varying forest conditions and data capture scenarios [7]
The main aim of this study is to introduce an algorithm for automatic extraction of tree stems and tree metrics including Diameters at Breast Height (DBH), tree height, and stem curve from terrestrial laser scans
Summary
Terrestrial laser scanning (TLS) or terrestrial light detection and ranging (LiDAR) is a promising, non-destructive technique to extract spatially explicit tree metrics for forest inventory purposes [1,2,3,4]. Pioneering studies pointed out in the first decade of the millennium that TLS provides highresolution, spatially explicit measures of plot-level forest structure, and tree parameters have the potential for derivation with little manual intervention [5,6]. The European Spatial Data Research Organization (EuroSDR) launched the international project “Benchmarking on Terrestrial Laser Scanning for Forestry Applications” in order to evaluate the potential of applying TLS in characterizing forest sample plots and to reveal the capability of the recent algorithms for tree attribute extraction in varying forest conditions and data capture scenarios [7].
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