Girth-Based Anchor Matching for Handheld SLAM LiDAR Forest Inventory Under Closed Tropical Canopies.
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| Title: | Girth-Based Anchor Matching for Handheld SLAM LiDAR Forest Inventory Under Closed Tropical Canopies. |
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| Authors: | Kaewjampa, Naruemol1 (AUTHOR), Tongdeenok, Piyapong1,2 (AUTHOR), Klabsuk, Renuka1,3 (AUTHOR), Waengsothorn, Surachit2,4 (AUTHOR), Kim, Hyeon Tae3 (AUTHOR), Moukomla, Sitthisak4 (AUTHOR) moukomla@tu.ac.th |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 12, p1920. 19p. |
| Subjects: | SLAM (Robotics), LIDAR, Forest canopies, Units of measurement, Tropical forests, Forest surveys |
| Geographic Terms: | Thailand |
| Abstract: | Highlights: What are the main findings? A few trees marked with ordinary reflective tape are enough to align an entire dense tropical plot, so every other tree can then be linked to the field inventory by its girth alone—no per-tree GNSS needed. The very reflective tape is automatically picked up by the SLAM scanner's intensity channel, so the anchors that align the plot can also be detected without any manual mark-up. What are the implications of the main findings? Closed-canopy forests that were previously too dense for GNSS-based mobile-LiDAR inventory can now be mapped in under an hour by a single person walking with a handheld scanner. The workflow is affordable for tropical national forest inventories and repeat carbon-MRV surveys where high-grade GNSS receivers are out of reach. Per-tree geolocation in closed tropical canopies has typical uncertainties of 5–15 m with GNSS receivers, preventing automated linking of field inventories to point-cloud stem data. We propose an anchor-based matching framework that does not require per-tree GNSS. A handheld SLAM LiDAR scanner maps stems and girths within ≈40 min; field crews record species, girth, and serial numbers without physical markers or tools. Dataset linkage uses a small subset of reflective-tape anchor trees (35 and 43 per hectare, roughly one per 400–500 m2) with approximate GNSS locations. Species identity is transferred using median-based GNSS bias correction and quadrant-partitioned Hungarian matching with global deduplication; accuracy is validated by leave-one-anchor-out (LOAO) tests and exact binomial statistics. Tested in two 1-ha plots of open Dry Dipterocarp Forest (DDF; 280 trees/ha) and dense Dry Evergreen Forest (DEF; ~1054 trees/ha) at the Sakaerat Biosphere Reserve, Thailand, SLAM girth matched tape data with R2 = 0.997, RMSE = 1.82 cm in DDF; in DEF, after correcting a 6.38 m GNSS bias, R2 = 0.986 and RMSE = 7.01 cm, with ≥99% detection for stems ≥30 cm girth (99.2% DDF; 100% DEF). LOAO accuracy was 35/35 in DDF and 40/43 in DEF. Retroreflective-tape anchors were additionally detected automatically from the SLAM intensity channel in 71.4% of DDF anchors (95% CI 53.7–85.4) and 76.7% of DEF anchors (95% CI 61.4–88.2) at intensity ≥ 150 DN, with up to 59-fold enrichment over matched non-anchor controls at I ≥ 250 in DEF (Fisher's exact p < 1 × 10−15), enabling a fully automated anchor-detection pipeline. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A few trees marked with ordinary reflective tape are enough to align an entire dense tropical plot, so every other tree can then be linked to the field inventory by its girth alone—no per-tree GNSS needed. The very reflective tape is automatically picked up by the SLAM scanner's intensity channel, so the anchors that align the plot can also be detected without any manual mark-up. What are the implications of the main findings? Closed-canopy forests that were previously too dense for GNSS-based mobile-LiDAR inventory can now be mapped in under an hour by a single person walking with a handheld scanner. The workflow is affordable for tropical national forest inventories and repeat carbon-MRV surveys where high-grade GNSS receivers are out of reach. Per-tree geolocation in closed tropical canopies has typical uncertainties of 5–15 m with GNSS receivers, preventing automated linking of field inventories to point-cloud stem data. We propose an anchor-based matching framework that does not require per-tree GNSS. A handheld SLAM LiDAR scanner maps stems and girths within ≈40 min; field crews record species, girth, and serial numbers without physical markers or tools. Dataset linkage uses a small subset of reflective-tape anchor trees (35 and 43 per hectare, roughly one per 400–500 m2) with approximate GNSS locations. Species identity is transferred using median-based GNSS bias correction and quadrant-partitioned Hungarian matching with global deduplication; accuracy is validated by leave-one-anchor-out (LOAO) tests and exact binomial statistics. Tested in two 1-ha plots of open Dry Dipterocarp Forest (DDF; 280 trees/ha) and dense Dry Evergreen Forest (DEF; ~1054 trees/ha) at the Sakaerat Biosphere Reserve, Thailand, SLAM girth matched tape data with R2 = 0.997, RMSE = 1.82 cm in DDF; in DEF, after correcting a 6.38 m GNSS bias, R2 = 0.986 and RMSE = 7.01 cm, with ≥99% detection for stems ≥30 cm girth (99.2% DDF; 100% DEF). LOAO accuracy was 35/35 in DDF and 40/43 in DEF. Retroreflective-tape anchors were additionally detected automatically from the SLAM intensity channel in 71.4% of DDF anchors (95% CI 53.7–85.4) and 76.7% of DEF anchors (95% CI 61.4–88.2) at intensity ≥ 150 DN, with up to 59-fold enrichment over matched non-anchor controls at I ≥ 250 in DEF (Fisher's exact p < 1 × 10−15), enabling a fully automated anchor-detection pipeline. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18121920 |
