Barbier Nicolas

IRD - UMR AMAP

ULB - FNRS

Programme Pilote de recherches: Biodiversité, Changements globaux et Santé dans les

forêts tropicales humides

• Part of a broader effort from IRD in central Africa:� Tropical forest as a focal object for interdisciplinary research ;

� Consequences of global change on biodiversity, resources and health;

� Possibly set up permanent research platforms.

• Yaoundé workshop 10/2009

�Call for partnerships…

Need to quantify canopy structure

•Valuable for :

� Carbon dynamics and forest degradation

assessment;

� Forest ecology (allometry rules, gap dynamics, etc.);

Forest-climate interaction (gaz and vapor exchange);� Forest-climate interaction (gaz and vapor exchange);

� Forest types, etc.

•Difficult to measure in the field,

� Approached via indirect estimates (DBH);

� Limited representativity.

Remote sensing methods

•Limitation of pixelwise optical and radar :

� Saturation for high biomass levels (>250 t/ha);

� Unable to detect degradation.

•High cost of airborne sensors (e.g. LiDAR).

•Potential of VHR imagery (Quickbird, Ikonos, etc.) but operational methodologies to be developed.

RCA: GeoEye pansharpened G-NIR-B

Peaks at

dominant frequencies

Fourier periodograms: quantification of image texture

10

0 m

10

0 m

Proisy et al. RSE (2007)French Guiana

2D Fourier transform textural 2D Fourier transform textural ordination (FOTO)

• 2D Fourier transform

• Radial power spectrum (r-spectrum)

= Proportions of the image variance accounted for by successive frequencybins, across azimuthal orientations.

Imagery

(metric optical)

Forest cover

Unit-windows

100 - 150 m sides

Frequencies

Fourier analysis

r-spectracomputation

Separation into

Workflow:

• Principal components of image r-spectra dataset

= Identification of main axes of textural variation in the image dataset.

Table of

r-spectra

PCA ordination

Separation into

homogeneous acquisition

groups

Global

standardisation

Partitioned

standardisation

Texture indices (PCA 1, PCA 2)

Invertedforest

parameter

(e.g. meancrown size)

Relation of Texture indices with

forest parameters for a set of

(DART) stands

10

0 m

Textural ordination: principal axes of variation

10

0 m

Couteron et al. J Appl Ecol (2005)

FOTO analysis on aerial photographs in French Guiana

Relationships with forest parameters

Couteron et al. J Appl Ecol (2005)Terra Firme forest; French Guiana

Relationships with forest parameters

No saturation!

Proisy et al. RSE (2007)

Biomass prediction using Ikonos imagery in mangrove stands; French Guiana

D1 D2 D3 D4

Q4 Q5 Q6 Q7

Q8 Q9 Q10 Q11

a

b

a b

a. Ikonos VHR.b. DART simulations

Radiative transfer modelsRadiative transfer models

• Testing the effect of acquisition conditions

• Mitigation : partitioning method

D5 D6 D7 D8

a. Ikonos VHR.b. LIDARc-d. Hillshade models

a b

c d

Extreme configurations: typical examples

a b

c d

Фs-v

= 0°; θV = 9°; θS = 59° Фs-v

= 180°; θV = 9°; θS = 59°

Hillshade effects on

LiDAR surface models

e f

Фs-v = 80°; θV = 3°; θS = 59° Фs-v

= 80°; θV = 15°; θS = 59°

Фs-v = 80°; θV = 9°; θS = 47° Фs-v = 80°; θV = 9°; θS = 71°Barbier et al. RSE (2010)

Bi-directional

texture distribution

� ‘Hotspot’ effect on texture,

� Alleviated by partitioningapproach.

-2-2-2-2

-2-2-2-2

-1-1-1-1

-1-1-1-1

-1-1-1-1 -1-1-1-1

0000

0000

00000000

11111111

1111

-1-1-1-1Sun Height = 47°

0

3

6

9

12

15

0

30

60

90

120

150

180

Sun Height = 47°

0000 00000000

0000

1111

1111 11111111

2222

2222 22222222

33333333

3333

Sun Height = 71°

0

3

6

9

12

15

0

30

60

90

120

150

180

-1-1-1-1

-1-1-1-1-1-1-1-1

0000

00000000

0000

1111

11111111

1111

22222222 2222 2222

Sun Height = 59°

0

3

6

9

12

15

0

30

60

90

120

150

180

Sun Height = 59° Sun Height = 71°180

a b c

Фs-v

θV

No

n-p

art

itio

ne

d

θS = 47° θS = 59° θS = 71°

θ = 47° θ = 59° θ = 71°

0000

0000

0000

00000000

0000

0000

0000

0000

0000

0000

0000

00000000

0000

1111

1111

Sun Height = 47°

0

3

6

9

12

15

0

30

60

90

120

150

180

0000

0000

0000

0000

0000

0000

0000

0000

0000

0000

00000000

0000

1111

1111

0000

0000

Sun Height = 59°

0

3

6

9

12

15

0

30

60

90

120

150

180

0000

00000000

0000

0000

0000

0000

Sun Height = 71°

0

3

6

9

12

15

0

30

60

90

120

150

180

d e f

Фs-v

Pa

rtitio

ne

d

θS = 47° θS = 59° θS = 71°

Barbier et al. RSE (2010)

Large scale application

maps of apparent crown size and canopy heterogeneity

Barbier et al. GEB (2010)

Amazonia

Validation: the Canopy project

• Texture analysis: The FOTO method (IRD).

• Field validation: Large forest inventory database across

Central Africa (FRM).

• Software development: Convivial interface in ArcGis

(Nev@ntropic).

Laurent Dufy (FRM)

Nicolas Bayol (FRM)

Antoine Mugnier (FRM)

Couteron Pierre (IRD-AMAP)

Proisy Christophe (IRD-AMAP)

Barbier Nicolas (IRD-AMAP, ULB-FNRS)François Marques (Nev@ntropic)

Large scale validation: test sites

FRM test sites

Possible PPR sites

Imagery

• Planet Action: Spot 5, Formosat, Kompsat

• Others: Quickbird, Ikonos, GeoEye, Orbview

FOTO

analysis

Field Data

• Geolocalized plots of 0.5 ha

• Forest concessions above 50,000 ha

• Inventoried surface areas around 1%

• All trees above 10-20 cm diameter

• Destructive biomass sampling (upcoming)

Perspectives

• Quantification of forest canopy structure.

• Valuable insights into :

�Degradation level ;

�Biomass and carbon, with no saturation ;

�Other forest structural attributes.�Other forest structural attributes.

• Large scale application and repeatable.

• Effect of acquisition conditions mitigated.

• Relative low cost.