LiDAR Canopy Transect: Beer–Lambert Foliage Height Diversity (2D)

Forest Transect

Understory weight 0

Midstory weight 0

Canopy weight 100

Emergent weight 0

Tree density 120 trees

Canopy cover 70%

Extinction coefficient k 0.18

Vertical bins (h) 8

Scan columns 60

Live Readouts

Foliage Height Diversity

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Mean return height

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Returns captured

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Est. bird richness

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0%of maximum possible entropy ln(bins)

How it works

Instead of flying a 3D point-cloud camera over the plot, this transect scanner solves the classic Beer–Lambert / MacArthur–Horn canopy gap-probability equation column by column along a 2D cross-section. Each tree contributes a Gaussian foliage-density bump in (x, height) space; summed across nearby trees this gives a foliage density profile ρ(h) at every scan column. A downward pulse of intensity 1 loses energy passing through foliage:

dI/dh = -k · ρ(h) · I
return energy at h  =  I(h⁺) - I(h)   (energy intercepted in that slice)
remaining I(0)      =  ground return

The intercepted-energy profile is a genuine probability distribution over return height (it sums to exactly 1 by construction), so return heights are sampled directly from it — no nearest-neighbor lookup. Binning returns into h vertical layers gives the same Shannon-entropy Foliage Height Diversity index as the 3D version:

p_i = n_i / N
FHD = − Σ p_i · ln(p_i)

The four layer-weight sliders let you sculpt any canopy structure directly (not just three presets) — spread weight across all four bands for maximum FHD, or pile it into one band for a plantation-like signal.
Canopy cover and k control how much of the pulse is intercepted before reaching the ground — sparse, low-k canopies let more pulses reach bare earth (h = 0m).
Drag the transect canvas to pan along the flight line, or scroll to zoom, just like panning a real point-cloud viewer.

Scan progress: 0% · Returns: 0

drag to pan · scroll to zoom

Trunk Foliage density LiDAR return