Estimating Forest Attributes from Spherical Images
Haozhou Wang, John A. Kershaw
The Faculty of Forestry and Environmental Management, University of New Brunswick
Introduction

Stand Basal Area

Plant (Gap) Fraction

Tree Height | Diameter
Brightness（Value）

Hue

Trunk Info.

Clinometer

Measuring tapeSingle Tree
Attributes

HSV color space
is easier topresent various
sky color ranges
than RGB

Saturation

HeightDiameter
Diameter tape

Angel gauge

Crown Info.
Fisheye camera

Gap Fraction

1Objectives

Revisit required forchecking

Repeatability and Reproducibility Analysis□ Regressions indicate an under-estimation ofField BA, except at the very lowest BA values,
which is consistent with the hypothesis ofvisibility bias resulting from occluded(hidden or missing) “in” trees□ Edge marking resulting ingreater underestimation
than target counting□Target counting wasmore consistent thanedge marking amongdifferent users
and closer tothe 1:1 line

Newfoundland(NL, 45 plots)

3 Digital sample
locations

Instrument
Ricoh Theta S
Spherical
CameraExtension
Rod𝑯𝟐
𝜶𝟐E'X

𝑃ℎ(𝑥 ′ , 𝑦′ሻX1YHeight Steps

2.Distortion Correction
𝑅𝑖𝑚𝑔
𝑓=2𝑟𝑜𝑙𝑑𝑟𝑛𝑒𝑤 = 𝑓 ∙ 𝑡𝑎𝑛(2 ∙ 𝑎𝑟𝑐𝑡𝑎𝑛(ሻሻ2∙𝑓E
𝑯𝟏

1. Take images at 2
different elevations (E, E’)
2. Pair two spherical imagesby 3 control points
3. Mark the base and top ofthe same tree in bothimages (α1, α2, β1, β2)∆𝒉R𝑡𝑎𝑛 𝛽1 ∙ 𝐸 − 𝑡𝑎𝑛 𝛼1 ∙ 𝐸′𝛥ℎ =𝑡𝑎𝑛 𝛼1 − 𝑡𝑎𝑛(𝛽1 ሻ𝐸 + ∆ℎ
𝑅=𝑡𝑎𝑛(𝛼1ሻ𝑅 ∙ tan 𝛽2 + 𝑅 ∙ tan(𝛽1ሻ𝐻 = 𝑚𝑒𝑎𝑛 ቊ𝑅 ∙ tan 𝛼2 + 𝑅 ∙ tan(𝛼1ሻDiameter StepsD/2𝑃(𝑥0 , 𝑦0 ሻ
3. HSV Classification
H=hue; S=saturation; V=brightness
range: [0,1]=[min, max]Though it is called sphericalcamera, the spherical images
are flattened into a common
2D images stored on the disk.

h sv
Lower limit: (0.5, 0.3, 0.75)
Upper limit: (0.68, 1, 1)

D/2

1. Calculate distance(R)from Height part
2. Mark the left and rightedge of a tree at breast
height, to calculate
Projected Angle ωh sv
Lower limit: (0, 0, 0.88)
Upper limit: (1, 0.3, 1)

R𝐸 + ∆ℎ
𝑅=𝑡𝑎𝑛(𝛼1ሻ𝜔𝑠𝑖𝑛 2 ∙ 𝑅
D/2 =𝜔
1 − sin( 2 ሻωRod Lean Effects
Slight lean(-5° to 5°)

5% deviation

5% deviation

5% deviation

Algorithm Implementation
θIdeallyActual✓ Measuring Trees 5m away, 5° lean doesn’t havesubstantial effects on Height and Distance estimates
Future Work Develop software to implement this algorithm
 Compare image measured with field measured.
✓ HSV classification threshold works in different sky conditions

 Sunspots are a problem resulting in misclassification

4.6m
3.6m

Tripod𝜷𝟏Y31.Spherical to fisheye𝑦0
𝜃=× 360°
𝑤𝑖𝑑𝑡ℎ𝑑 = 𝑟 2 − 𝑟 − 𝑥0 2𝑥 ′ = 𝑟 + 𝑠𝑖𝑛𝜃 ∙ 𝑑ቊ ′𝑦 = 𝑟 + 𝑐𝑜𝑠𝜃 ∙ 𝑑Study Area

Noonan(NRF, 83 plots)

XX

Integrate attributes measured with traditional tools into one
consumer-grade spherical camera, validate the feasibility andrepeatability compared with traditional field inventory tools

1 Center Digital
sample location

Y

2

The key point to apply this on a spherical image is determining
which trees in an image should be counted or not

Traditional Tools Drawbacks

Labor intensive

YAngle count sampling is a sampling method that uses ahorizontally projected angle (basal area expansion factor, BAF) toselect trees for inclusion in the sample. Basal area per ha is
obtained by counting selected trees and multiplying by the BAF.
Software Implementation

Or “Plant Fraction” (PF)Time consuming

𝜷𝟐𝜶𝟏Basal Area

Stand-level
Attributes

𝑯𝟑2.6m1.6m
Take images at 4 heights

Future Work
Full paper
Effects of digital
locations, understory
density, hidden tree The effects of zenith angle selection;
 Compare with other fisheye analyzing software; Compare with the fisheye camera images;
 Deal with sunspot problems

Conclusion❖ Using spherical camera to integrate stand basal area,plant fraction, trunk diameter, and tree height is feasible.
❖ These methods may have obstacle effects limited by 2d
image and cause slight under-estimation.
❖ The image method proved repeatable and reproduceable
among different users.