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6 AUG 2026
Science · remote sensing

Taiwan screened 57,065 laser leads to find one tree. The laser still read it low.

Airborne LiDAR flagged tens of thousands of possible giants across Taiwan, most of them ordinary trees perched on cliffs. Volunteers discarded nine in ten. On the giants that survived the filter, the model’s remaining error mostly ran the other way.

84.1m
Height of the tree named the Heaven Sword, measured by tape drop from its crown in 2023
57,065
Laser profile images screened by 372 volunteers in 2022
941
Trees above 65 m verified across the island
How the search worked

A filter built to throw things away

Researchers at Taiwan’s Forestry Research Institute and National Cheng Kung University built an island-wide canopy height model from airborne laser scanning flown between 2010 and 2016, subtracting a bare-earth elevation surface from the canopy surface. A 19-by-19-metre local-maximum filter then picked out every point reading above 65 metres.1

That produced far more leads than anyone could check. An expert analyst needs roughly 20 minutes per candidate to rotate the point cloud into a view that shows both treetop and base.1

Most leads were not tall trees. Steep ground defeats the subtraction, because the laser struggles to sample the true ground surface beneath a cliff edge. The paper’s worked example is a tree under 25 metres growing on top of a cliff, which the model recorded at 90 metres.1

So in 2022 the profile images went online. Volunteers marked treetop and base on each one, three people to an image, working independently. The 57,065 profiles came down to 4,736 candidates — 91.7 per cent discarded — and expert review of the full 3D point cloud confirmed 531 of those. Added to two earlier expert-only surveys, the island’s map now holds 941 trees above 65 metres, most of them on slopes averaging 40 degrees.1

Key finding. The crowdsourced stage removed 91.7 per cent of leads before any expert or climber looked at them. It was a false-positive filter, not a height correction.
What the filter cannot fix

On the confirmed giants, the model reads low

The filter decided which trees were worth walking to. It did not make the model’s heights right. Across the 22 trees the team has climbed and measured since 2014, the root-mean-square error between modelled and field height is 4.55 metres.1

On the ten trees confirmed above 70 metres, the error runs opposite to the cliff artefact. The model read low on six and high on four, averaging 1.57 metres below the measured height. The Heaven Sword is the plainest case: 84.1 metres on the tape, 80.0 in the model. NanKengSi is the sharper one, at 72.9 metres in the field against 66.5 in the model — 1.5 metres above the 65-metre cut that would have deleted it before anyone saw it.1

Exhibit 01
What the laser missed — modelled height against measured height, ten Taiwania above 70 m
Source: Hsu, Wang and Lee (2026), Table 1. Each bar spans the canopy-height-model estimate and the field measurement for one tree; bar length is the gap, colour the direction. Names marked with an asterisk were measured by drone photogrammetry scaled against a climber’s pole rather than by tape drop.1

One reading is that thin laser sampling under-reads a canopy apex, which on a tall conifer is the narrowest and hardest part to hit; the study notes that pulse density is a cost trade-off and that low densities risk under-sampling the apex.1 It does not test that mechanism, and the ten-tree pattern is consistent with several others.

What we don't know

The denominator is not clean. The abstract calls 57,065 the island-wide count of local maxima, but the methods say experts inspected 14,692 profiles in 2019 and 2020, described as about 20 per cent of Taiwan’s candidates — which implies an island-wide pool nearer 72,000, with 57,065 as the remainder handed to volunteers. We use 57,065 only for what volunteers screened, which both passages support.1

Three of the ten heights above 70 metres come from drone photogrammetry rather than tape drop. The Heaven Sword’s modelled height appears as 80.0 metres in the paper’s table and 79.5 in its text; we use the table. The 4.55-metre error figure rests on 22 climbed trees, and there is no independent height check on the other 919 on the map.1

The claim that this is East Asia’s tallest tree depends on where the region’s boundary is drawn. A 102.3-metre Cupressus austrotibetica in the Yarlung Tsangpo canyon in Tibet, China, was measured by drone LiDAR in 2023 and described by its authors as the tallest in Asia — a paper the Taiwan team cites.2 Taiwan’s tree is the tallest confirmed by direct tape drop.

By the numbers
91.7%
Leads discarded at the volunteer stage
4.55m
Model-to-field error, 22 climbed trees
372
Volunteers screening profiles in 2022
22
Trees climbed and measured since 2014
Caveat. The ten trees in Exhibit 01 were selected because they turned out to be giants, so the sample is conditioned on the outcome. The 1.57-metre average shortfall describes those ten; it is not an estimate of the model’s bias in general, and it says nothing about the tens of thousands of leads the filter correctly threw away.
Method. All counts are as stated in the primary paper; derived figures were computed in code from its Table 1 before any prose was written. The exhibit plots each tree’s canopy-height-model estimate against its field height, so bar length equals the gap and colour equals its direction; the mean signed gap across the ten trees is −1.57 m and the mean absolute gap 3.21 m. Percentages in the funnel are computed from the stated counts (4,736 of 57,065 profiles surviving, 91.7 per cent discarded); the paper separately reports the stage as eliminating 92 per cent of the manual verification workload. Where the paper’s table and text disagree on a value, the table is used and the discrepancy is disclosed. Nearly every figure here comes from a single study, which is unavoidable for a first release; the record claim is the one item cross-checked against an independent peer-reviewed source. The team’s giant-tree map and metadata are public at no1tree.tw, per the paper’s data availability statement. No projection or modelled scenario appears in this piece.
Sources. Numbered, highest tier first; each inline citation points here.
  1. T1Hsu R. C.-C., Wang C.-K. and Lee C.-C. (2026). The journey of finding the tallest tree in Formosa Taiwan. Frontiers in Forests and Global Change 9:1746112. Published 5 June 2026, corrected 16 June 2026. doi.org/10.3389/ffgc.2026.1746112 — retrieved 6 Aug 2026.
  2. T1Ren Y., Guan H., Yang H., Su Y., Tao S., Cheng K. et al. (2024). Discovering and measuring giant trees through the integration of multi-platform lidar data. Methods in Ecology and Evolution 15, 1889–1905. doi.org/10.1111/2041-210X.14401 — retrieved 6 Aug 2026.
Corrections. None. Material corrections will be logged here with date.
Figures to 6 Aug 2026. Noodle News · v1.0 · taiwan-heaven-sword-lidar-funnel.