Nadia.Rochdi @nrcan.gc.ca

Richard Fernandes

Sylvain Leblanc

Chris Butson

Abdel Abuelguesim

Peter White

Shusen Wang

Mapping LAI over CanadaMapping LAI over CanadaMethods and ResultsMethods and Results

Why and How mapping LAI

Objective

Provide consistent LAI maps over Canada to be used in water, carbon, energy flux models

Main Steps

1. Introduction

2. Field LAI measurements through optical techniques.

3. Basic satellite LAI retrieval algorithm : Which regression method should be used ? Does vegetation indice make difference? Does LAI algorithm show temporal consistency?

4. Inter-comparison of global product : MODIS-POLDER-VEGETATION

Mapping LAI

Objective

Provide consistent LAI maps over Canada to be used in water, carbon, energy flux models

Main Steps

1. Introduction

2. Field LAI measurements through optical techniques.

3. Basic satellite LAI retrieval algorithm : Which regression method should be used ? Does vegetation indice make difference? Does LAI algorithm show temporal consistency?

4. Inter-comparison of global product : MODIS-POLDER-VEGETATION

Definition – just to remind us!

Projected “green” foliage area per unit of ground surface area (m2/m2):

• Important biophysical property of vegetation canopies used in ecological modeling (used in Myneni et al. 2002 ; Running et al., 1989).

• Referred as Green Leaf Area Index (GLAI).

LAI is defined as half the total foliage surface of all sides per unit of surface projected on the local horizontal datum (used in Chen and Black, 1992; Fernandes et al., 2001; Liu et al., 2002; Chen et al., 2003).

Well adapted for flat leaves : grass, crops, deciduous forest

In conifers shoot is considered as the foliage element: Needles organization should be taken into account

1. Introduction

STAR- a possible show stopper

Shoot silhouette to total needle area ratio ‘STAR’ (Oker-Blom & al 1988) STAR variability: [0.09 0.22]

1. Introduction

effect on scattering within canopies:• Modify the shoot scattering albedo

(Smolander et al. 2003)

STAR

• Modify the canopy BRDF

(5SCALE simulation on Old black spruce)

Pinus Bancksiana (Jack Pine) Picea-Mariana (Black Spruce) Pinus-sylvestris (Scot Pine)

177.0STAR 153.0STAR18.0STAR

Mapping LAI

Objective

Provide consistent LAI maps over Canada to be used in water, carbon, energy flux models

Main Steps

1. Introduction

2. Field LAI measurements through optical techniques.

3. Basic satellite LAI retrieval algorithm : Which regression method should be used ? Does vegetation indice make difference? Does LAI algorithm show temporal consistency?

4. Inter-comparison of global product : MODIS-POLDER-VEGETATION

In-situ optical PAI/LAI estimation

)cos(/).().()( PAIGeP Canopy gap fraction

Foliage elements clumping (): Same for foliage and woody materials

Plant area index PAI: w

E

LLAI

PAI

Woody Area Index

Needle-to-shoot area ratio

G() always close to 0.5 for =57.5° for any random foliage azimuth angle distribution (Warren Wilson and Reeve 1959, Weiss et al 2004)

Projection of unit foliage in direction G(): Same for foliage and woody materials

Average Leaf Inclination Factor

Pro

jec

ted

are

a o

f th

e l

eav

es

G(

)

2. Field LAI Measurements using Optical Techniques

Methods

dP

PAI )sin()cos()(

)(ln2

2

0

5.0)sin()(2

0

dG• Method2: Rely on Cauchy’s theorem (Miller 1967):

cos.

)()(

)(

G

LnPPAI

• Method1: Invert gap fraction measurements near 57º (G()=0.5)

Both methods should match if they sample the same stand.

PAI-Miller ( 10-80)

PA

I (

55-6

0)

2. Field LAI Measurements using Optical Techniques

But what about clumping?

Gap size distribution (Chen et al 1995, corrected in Leblanc 2002)

)(1)(

)()(1),(

pp WL

pp e

WLF

),0(1),0(ln

),0(1),0(ln)(

mmr

mrm

FF

FF

Lp(): Projected LAI: Gap sizeWp(): Foliage element mean projection on the groundFm(0,): Measured accumulated gap fraction larger than 0Fmr(0,): Gap fraction for the canopy without large gaps

This method throws away big gaps (discontinuous media). Limited when clumping could impact small gaps (should be test in agriculture)

2. Field LAI Measurements using Optical Techniques

Azimuth Angle

Dig

ital

Nu

mb

er

Gap Size (pixel)

Acc

um

ula

ted

Gap

Fra

ctio

n

=10°

Digital hemispherical Photos (DHP) Software

Provide clumping as function of view angle Deals with sub-pixel gaps

2. Field LAI Measurements using Optical Techniques

Can we replace the TRAC with DHP ?

Gap Fraction TRAC

Gap

Fra

ctio

n D

HP

R2=0.9

Good correlation between both techniques but with a systematic bias.Scattering effect on DHP & TRAC inability to see very small

gaps

Gap Fraction TRAC

Gap

Fra

ctio

n D

HP

R2=0.95

Improvement occurs when removing sub-pixel gap in DHP processing. Possibility to get () angular variation from DHP since TRAC measurements is solar zenith dependent

2. Field LAI Measurements using Optical Techniques

Mapping LAI

Objective

Provide consistent LAI maps over Canada to be used in water, carbon, energy flux models

Main Steps

1. Introduction

2. Field LAI measurements through optical techniques.

3. Basic satellite LAI retrieval algorithm : Which regression method should be used ? Does vegetation indice make difference? Does LAI algorithm show temporal consistency?

4. Inter-comparison of global product : MODIS-POLDER-VEGETATION

Can we make a map using in-situ data and co-incident imagery

Large errors in both LAI and VI are common. Classical linear regression will be biased. Thiel-Sen Method is unbiased, requires no error information and is robust to 29% outliers (Kendall and Stewart Advanced Theory Statistics)

3. Satellite LAI Retrieval Algorithm

SR

LA

I

LAI VALERI data on conifers (Larose forest) and SR landsat ETM+

1.5

1.7

1.9

2.1

2.3

2.5

2.7

2.9

3.1

0 1 2 3 4 5 6

SR

LA

I

TS

LAI data

LAI on SR

Can we make consistent maps of the same area from “Landsat ETM+”?

Errors on Lai retrieval were assessed in regards to four atmospheric correction approaches:

1. Aerosol optical depth constant for all scenes (AOD550=0.06)2. no-atmospheric correction (AOD550=0)3. scene Dense Dark Vegetation (DDV) (scene-dependent AOD550)4. MODIS AOD550 measurements.

Overlap reflectance was to some extent consistent but depends on spectral band : visible band show bigdifferences (32-73%).

LAI consistency errors do not depend on atmospheric correction method: 0.6 unit LAI (25%) Color composite from SPOT4-VEGETATION

20 Landsat ETM+ images from 7-25 days apart

3. Satellite LAI Retrieval Algorithm

SR vs RSR vs ISR

Vegetation indices (VI):

Simple ratio

Reduced Simple Ratio

Infrared Simple Ratio

LAI = -0.0205RSR 2 + 0.8903RSR + 0.0497R2 = 0.8559

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 5 10 15 20

RSR

LAI

LAI = -0.021SR 2 + 0.9857SR - 1.6788

R2 = 0.834

0.0

2.0

4.0

6.0

8.0

10.0

12.0

0 5 10 15 20 25

SR

LAI

REDNIRSR

min,max,

max,

SWIRSWIR

SWIRSWIRSRRSR

SWIRNIRISR

3. Satellite LAI Retrieval Algorithm

Regressions between ISR and LAI are as satisfactory or better than RSR & SR

ISR

LA

I

R2=0.86

Coniferforests

Atmospheric Noise should be considered when using VI’s

-25

-20

-15

-10

-5

0

5

10

15

20

25

0 1 2 3 4

RSR

Rel

ativ

e E

rro

r in

RS

R (%

)

0.026 0.041 0.100 0.190Red

-5

-4

-3

-2

-1

0

1

2

3

4

5

1 2 3 4

ISRR

ela

tiv

e E

rro

r in

ISR

(%

)

0.2150.1500.1290.0850.0630.0420.2150.1500.1290.0850.0630.042

SWIR

RSR relative errors around 20% for typical uncertainties in aerosol optical depth ISR seems to be more robust estimator than RSR showing less sensitivity to

atmospheric conditions

3. Satellite LAI Retrieval Algorithm

Errors in aerosol optical depth (around a baseline value of 0.10) are respectively equal

to +0.10 (solid lines) and –0.10 (broken lines)

Impact on Time Series of LAI

ISR shows consistent temporal behavior in relation to the SR one. High LAI estimations form ISR in the end of growing season.

3. Satellite LAI Retrieval Algorithm

10 days maximum SPOT-VEGETATION

“Sparse shrub and grass”

Mapping LAI

Objective

Provide consistent LAI maps over Canada to be used in water, carbon, energy flux models

Main Steps

1. Introduction

2. Field LAI measurements through optical techniques.

3. Basic satellite LAI retrieval algorithm : Which regression method should be used ? Does vegetation indice make difference? Does LAI algorithm show temporal consistency?

4. Inter-comparison of global product : MODIS-POLDER-VEGETATION

Large Area Intercomparison

Needleaf Forest

Broadleaf Forest

Crops

Pastures/Grasses

Tundra/Barren

-4 -2 0 2 4

LAI Difference

LAND USE OF COMPARED REGIONS MODIS LAI – VGT LAI (JUNE 2000)

Needleaf Forest

Broadleaf Forest

Crops

Pastures/Grasses

Tundra/Barren

-4 -2 0 2 4

LAI Difference

LAND USE OF COMPARED REGIONS POLDER LAI June1997 – VGT LAI June 1998VGT LAI * 20 VGT LAI * 20

MO

DIS

LA

Ix20

PO

LD

ER

LA

Ix20

Needleaf Forest

VGT LAI * 20 VGT LAI * 20

MO

DIS

LA

Ix20

PO

LD

ER

LA

Ix20

Crops

Considering 1 year difference in the data and 9km scale POLDER and VGT are relatively consistent

MODIS LAI estimations are higher than VGT and POLDER over Forests and tundra

4. Inter-Comparison of Global Products

Over crops and pasture MODIS seems to show unreasonable LAI values range

Main conclusions

• Stick to one LAI definition even if it is arbitrary (invariance).

• Needle to shoot area ratio is problematic.

• Digital hemispherical photography can provide consistent in-situ measurements of “PAI”.

• Atmospheric contamination (especially sub-pixel clouds) can cause big problems in “products”.

What we need

• Temporal validation using simple consistent methods (e.g. plantwatch method of quarters, using simple cheap cameras placed in-situ with “cell phone” links, etc.)

• We need more work to determine if we can actually see within shoots using passive optical imagery.

• More attention is needed with respect to the relationship between clumping and factors like angle, scale, species, crown shape...

• We should consider either a global LAI effort (a la global land cover) or at least a global LAI validation effort under the auspices of GEO (global earth observations).