We used lightweight terrestrial laser scanning (TLS) to detect over 3000 stems per hectare across a 12-ha permanent forest plot in French Guiana, 81% of them < 10 cm in trunk diameter. This method retrieved 85% of the trees of a classic inventory. Finally, TLS revealed that stem positions of the classic inventory had geolocation errors of up to 6 m.
Context Accurate position mapping of tropical rainforest trees is crucial for baseline studies of tropical forest ecology but is labor-intensive. Terrestrial lidar scanning (TLS) is broadly used in temperate forest inventories, but its use in rainforests is restricted to the determination of individual tree volumes within small survey areas.
Aims Mapping tree stems across one large (12-ha) rainforest plot, including trees less than 10 cm DBH, and evaluating the precision of traditional mapping approaches.
Methods We used lightweight TLS, co-registered the acquisitions, and developed a new efficient algorithm to process the TLS data.
Results We detected 36,422 stems of which 29,665 (81%) were < 10 cm in diameter at breast height (DBH). Of the trees ≥ 10 cm DBH previously censused in the plot, 85% were identified by TLS. Automatic DBH estimation from TLS data had an RMSE of 6 cm. RMSE was improved to 3 cm by a manual verification of the shape and quality of the stem points. The initial census map had substantial bias in tree geolocation with a maximum value around 6 m.
Conclusion Lightweight TLS technology is a promising tool for the estimation of stem tapering and volume. Here, we show that it also facilitates the establishment of large tropical forest inventories, by improving the positioning of trees, thus increasing the accuracy of forest inventories and their cost-effectiveness.
Forest inventory; Amazon forest; Nouragues; Terrestrial Lidar; Topography; Stem diameter
Tao, S., Labrière, N., Calders, K. et al. Mapping tropical forest trees across large areas with lightweight cost-effective terrestrial laser scanning. Annals of Forest Science 78, 103 (2021). https://doi.org/10.1007/s13595-021-01113-9
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Data and/or Code availability
The datasets generated by this study and data processing codes have been made publicly available at [github.com/TonySl/BLK360_TLS].
John M Lhotka