abteilung biometrie und informatik smc spring meeting 2007, vancouver, wa. mapping forest...

Abteilung Biometrie und Informatik SMC Spring Meeting 2007, Vancouver, WA.

Mapping Forest Characteristics across the Landscape using Sample Plot and

Airborne Laser Scanning Data

Johannes Breidenbach

Forest Research Institute of Baden-Württemberg(Forstliche Versuchs- und Forschungsanstalt

Baden-Württemberg)


Outline

• Introduction

• Lidar

• Methods

• Plotwise estimation of– Lorey’s height– Volume, Biomass– Diameter distributions


Systematic sample plot inventoryIntroduction


r = 3 m; > 10 cm

r = 6 m; > 15 cm

r =12 m; > 30 cm

Introduction

r = 2 m; > 7 cm


Mapping of forest characteristics with Lidar data

Introduction

1. Calibration of regression models (Lidar+sample plots)

2. Application of regression models (Lidar only)


Mapping of forest characteristics with Lidar data

Introduction

1. Calibration of regression models (Lidar+sample plots)

2. Application of regression models (sample plots)


Outline

• Introduction

• Lidar

• Methods



Light detection and ranging (Lidar)

Source: TopoSys

Lidar


Digital Terrain Model

Lidar


Digital Surface Model

Lidar


Outline

• Introduction

• Lidar

• Methods



Two Approaches to analyse Lidar Data

• High resolution data (> 1 beam/m²)– Single tree approach– By now: mainly scientific studies

• Low resolution data (< 1 beam/m²)– Plotwise approach– Robust method

Methods


Methods

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Methods


Inventurnummer 200

Fre

qu

en

cy

0 10 20 30 40

02

04

06

08

0

Min Max

Median

3. Quartile1. Quartile

Lidar vegetationheight [m]

Methods

Height metrics

d5d4d3d2d1

Density metrics

Mean


Additional covariates

• Coniferous proportion (CP) = (CHMF - CHML)/ CHMF > 0.3

• Crown cover (CC) = CHMF > 1 m

Methods


Outline

• Introduction

• Lidar

• Methods



Incidents effecting height estimates and Lidar metrics

• CP = Crown shape

• Stem density (TPH)

• Slope

Density metrics couldn‘t substitute the influence of slope and TPH

Lorey‘s height


hhh

Lorey‘s height

10 15 20 25 30 35 40 4510

1520

2530

3540

45

3rd quartile Lidar vegetation height [m]

Pre

dict

ed H

L [m

]

slope=5slope=25


Outline

• Introduction

• Lidar

• Methods



E(μ) = Mean.L + Mean.L * CP + Mean.L * CC + d7

E(σ) = Mean.L

R² 0.6; RMSE 120 m³/ha; 60 Mg/ha (~28%)

Estimation of Volume and Biomass

Vol./Biomass

),(~ Normaly


Nature reserve Ridis

Area: 31,5 haEstimated biomass (inventory): 8491 t, 300 t/ha, 8% Error1

Estimated biomass (Lidar):8681 t, 276 t/ha, 2% Error1

Area: 18,5 haEstimated biomass (inventory):

3796 t, 186 t/ha, 17% Error2

Estimated biomass (Lidar):4082 t, 221 t/ha, 3% Error2

Area: 1 haEstimated biomass (Lidar):

329 t, 10% Error

1,2 difference not significant

Vol./Biomass


Estimations using mixed effects models

Vol./Biomass

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ijijiij Xby 0


With random effects

Volume [m³/ha]S

tan

d n

um

be

r

42488199

108114122158162164209223238252263272280284

-200 0 200

With out random effects

Volume [m³/ha]

Sta

nd

nu

mb

er

42488199

108114122158162164209223238252263272280284

-200 0 200

Vol./Biomass

Bias decreases, variance increases, hardly any change in RMSE


Outline

• Introduction

• Lidar

• Methods



Estimation of diameter distributions

Inventurnummer 200

Fre

qu

en

cy

0 10 20 30 40

02

04

06

08

0

Lidar vegetationheight [m]

Qu1: (9.56,12.3] Qu3: (14.1,17.7] n Bäume: 481

BHD [cm]

De

nsi

ty

0 20 40 60 80 100

0.0

00

.01

0.0

20

.03

0.0

40

.05

0.0

6

DBH

DBH [cm]


),(~ Weibully

yy

yf exp),|(1

n

iiyfLLH

1

,|log

n

iiiiyfLLH

1

,|log

ii g 111 ii g 2

12

DBH


Qu1: (23.4,26.2] Qu3: (28.5,32.1] Plots: 17 Trees: 205

Den

sity

0 20 40 60 80

0.00

0.04

0.08

Qu1: (20.6,23.4] Qu3: (24.9,28.5] Plots: 42 Trees: 551

Den

sity

0 20 40 60 80

0.00

0.04

0.08

Qu1: (17.9,20.6] Qu3: (24.9,28.5] Plots: 32 Trees: 347

DBH [cm]

Den

sity

0 20 40 60 80

0.00

0.04

0.08

Qu1: (17.9,20.6] Qu3: (21.3,24.9] Plots: 38 Trees: 531

0 20 40 60 80

0.00

0.04

0.08

Qu1: (15.1,17.9] Qu3: (21.3,24.9] Plots: 60 Trees: 823

0 20 40 60 80

0.00

0.04

0.08

Qu1: (15.1,17.9] Qu3: (17.7,21.3] Plots: 27 Trees: 392

DBH [cm]0 20 40 60 80

0.00

0.04

0.08

Qu1: (12.3,15.1] Qu3: (17.7,21.3] Plots: 66 Trees: 850

0 20 40 60 80

0.00

0.04

0.08

Qu1: (9.56,12.3] Qu3: (14.1,17.7] Plots: 40 Trees: 481

0 20 40 60 80

0.00

0.04

0.08

Qu1: (6.78,9.56] Qu3: (10.5,14.1] Plots: 20 Trees: 214

DBH [cm]0 20 40 60 80

0.00

0.04

0.08

DBH


Future work

• Influence of registration errors

• Integration into existing systems and procedures (IRIS)

)|(),|()|,( lidarDBHpdflidarDBHHpdflidarDBHHpdf


Thank you for your attention!



• High resolution data (> 1 beam/m²)

• Low resolution data (< 1 beam/m²)

Methods



• High resolution data ( 1 beam/m²)– Suited for single tree approach = delineation of

single crowns– Statistical relations between

• Crown properties (e.g. crown diameter), tree height (laser derived) and

• Volume, diameter (terrestrial, single tree related ground truthing)

Conifer dominated, open forests, high costs

for georeferencing of single treesBy now primarily for scientific purposes

Methods


High resolution data

Methods



• Low resolution data ( 1 beam/m²)– Plot level approach – Satistical relations between

• Vegetation height distributions (laser derived) and

• Sample plot volume, plot mean tree height, plot biomass, plot diameter structure (terrestrial, e.g. FFI)

Robust methodUse of available standard inventory data for

calibration

Methods


Material

• ~2000 inventory sample plots– Volume 420..1200 m³/ha– Biomass 220..550 Mg/ha– Lorey‘s height 30..42 m– Spruce, Beech, Maple and others

• ~150 km² Lidar data– Optech ALTM 1225– 1.5 m point spacing (low density)– Leaf-off condition

Material


Results

log(μ) = βμ0 + βμ1 · 3rd Qu + βμ2 · 1st Qu · 3rd Qu

log(σ) = βσ0 + βσ1 · 1st Qu + βσ1 · 1st Qu · 3rd Qu

DBH

abteilung biometrie und informatik smc spring meeting 2007, vancouver, wa. mapping forest...

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