LiDAR Remote Sensing of Forest Vegetation

Ryan Anderson, Bruce Cook, and Paul Bolstad

University of Minnesota

Light Detection and Ranging (LiDAR)Light Detection and Ranging (LiDAR)

1 ns = 0.15 m

Airborne LiDARAirborne LiDAR

Source: TopScan, Germany

• Ground Surface elevations (30 cm vertical, 1 m Ground Surface elevations (30 cm vertical, 1 m horizontal accuracy)horizontal accuracy)

– Wetland delineation.Wetland delineation.– Interpolation of water table heights.Interpolation of water table heights.

• Vegetation height and density (i.e., structure)Vegetation height and density (i.e., structure)– Improved landcover classification (fusion with Improved landcover classification (fusion with

imagery).imagery).– Spatial estimates of biomass, canopy height, basal Spatial estimates of biomass, canopy height, basal

area, LAI ,etc (does not saturate!)area, LAI ,etc (does not saturate!)– Input variable for other modelsInput variable for other models

Elevation and Vegetation HeightElevation and Vegetation Height

Leaf-On data2.3 million pulses (15% ground hits)Median height = 5.2 m

Bare Earth Elevation (m) Vegetation Height (m)

Landscape ProfilesLandscape Profiles

DeciduousUpland

East-West cross-section

North-South cross-section

Hw

y 182

Upland-WetlandCatena

Clea

rcu

t

Mixed Forest

Grass

Coniferous Wetland

ShrubWetland

Stand StructureStand Structure

Coniferous Wetland

Alder-Cedar Wetland

Mixed Upland

Frequency

Hei

gh

t

First returns for 30 x 30m plots

Bare Earth Elevation“Leaf off” collection

• Spring 2006

• 1st and last returns

“Leaf on” collection

• Summer 2005

• 1st return only

Ground control points (n=34):100% of QA/QC points ± 15 cm

Image difference (n=46 million):90% of 2005/06 pixels ± 60 cm

Approx. 1.5 pulse m-2

1 m nearest neighbor interpolation

1 km

LiDAR Methods…Flying is the easy part!

• Collect vertical ground control points

• Collect field observations for variables of interest (FIA–style plots)

• Acquire LiDAR and fine resolution multi-spectral imagery (Quickbird)

• Triangulate ‘ground hits’ and compute base height of ‘feature hits’

• Use digital terrain model to extract features

• Compute feature heights

• Combine feature heights with return intensity, multi-temporal/spectral imagery, and DEM to classify landcover

• Extract pulses associated with field plots and compute LiDAR variables (density and height for biomass, GPP/NPP; gap fraction for LAI/fPAR)

• Develop relationships between LiDAR and plot variables

• Apply relationships to entire scene

• Use spatial variable to drive growth models (e.g., MODIS GPP/NPP algorithm)

Training Plots

• FIA-style plot design• 76 Upland plots (~30 located precisely enough

to be useful for LiDAR analysis)• Wetland plots to be taken this field season• Height and growth in central subplot• Biomass calculated by species specific

allometric equations [Biomass] = a [DBH]b

• Productivity calculated by inferring past diameters from cored trees

Training Plot Locations

H10 10th percentile of feature heights within the subplot

H50 50th percentile of feature heights within the subplot

H90 90th percentile of feature heights within the subplot

Hmean Average feature height within the subplot

Hmax Maximum feature height within the subplot

Hcv Coefficient of variation of all feature heights within the subplot

D1 The proportion of LiDAR canopy returns that were above the lowest of 10 equal width intervals.

D5 The proportion of LiDAR canopy returns that were above the 5 th of 10 equal width intervals.

D9 The proportion of LiDAR canopy returns that were above the 9 th of 10 equal width intervals.

Ng Number of LiDAR pulses that penetrated to the ground within the subplot

N The total number of LiDAR returns detected within the subplot

LiDAR Variables Extracted for Each Plot

Stepwise Multiple Regression Analysis

)/(951

905010

87554

3210

NNgDDDHCv

HHHY

)/log(9log5log1loglog

90log50log10loglog

87554

3210

NNgDDDHCv

HHHY

Full Model 1:

Full Model 2:

Where Y is the plot-measured variable of interest (biomass, height, productivity, etc)

Canopy Height

10 15 20 25

12

14

16

18

20

22

Lorey's Mean Height

Lid

ar

Pre

dic

ted

He

igh

t

AlderAspen / FirNorthern HardwoodsOther WetlandUpland coniferWetland Conifer

1:1 line

R2= 0.771564028836599

Predictor Coefficient Standard Error

p value

Intercept 12.86944 1.87939 <.001

H50 1.10396 0.17653 <.001

Hcv -0.08655 0.04708 0.0802

D5 0.09749 0.03548 0.0121

r2 = .7716

crossvalidation RMS: 2.32784 m

0 5000 10000 20000 30000

05

00

01

00

00

20

00

0

Biomass (kg/ha)

Lid

ar

Pre

dic

ted

Bio

ma

ss (

kg/h

a)

AlderAspen / FirNorthern HardwoodsOther WetlandUpland coniferWetland Conifer

1:1 line

R2= 0.767876173391003

Biomass

Response variable: Biomass

Predictor Coefficient Standard Error

p value

Intercept -5648.36 2934.37 .068

H10 3056.92 839.04 .00152

H90 1303.72 268.38 <.001

D5 -286.97 87.13 <.001

r2 = .7679

crossvalidation RMS: 6511.06 Kg/ha

50 100 150 200

05

01

00

15

0

5 year average net productivity (kg/ha)

Lid

ar

Pre

dic

ted

5 y

ea

r a

vera

ge

ne

t pro

du

ctiv

ity (

kg/h

a)

AlderAspen / FirNorthern HardwoodsOther WetlandUpland coniferWetland Conifer

1:1 line

R2

= 0.493289250597683

Productivity

Response variable: ANPP5

Predictor Coefficient Standard Error

p value

Intercept 213.011 36.866 <.001

H10 -26.471 6.313 <.001

H90 8.704 2.959 .007

Hcv -3.963 1.167 <.003

r2 = .5933

crossvalidation RMS: 44.628 Kg/Ha * Year

This Field Season

This Field Season