PhD remote sensing course, 2013 Lund University Understanding Vegetation indices PART 1

Understanding Vegetation indicesPART 1 : General Introduction

Talk byHongxiao Jin

Light interaction with canopy

Transmittance and reflectance

PART 1

Ground area A

P

a

Opaque leaf area a

()2

A

A

Ground area A, N opaque leaves

P

LAIkae black leaf, a=1

For transparent any LAD leavesabsorptivity a

𝜏=(1− 𝑎𝐴

)𝑁

≈𝑒−𝐿𝐴𝐼

Canopy transmittancefor horizontal opaque leaf

Transmittance model from simple exponential equation. a=0.8 for PAR (or red), a=0.2 for NIR.In comparison with SAIL model.

Figure: canopy transmittance from in situ 4-sensor PAR component measurements (Eklundh et al., 2011). PAR (or red) transmittance is much larger than modelled. In Abisko the caopy LAI is ca. 0.8-1.9

dLIkdI

00| II L

Simplest RT equation

Boundary conditions

kLeII 0Solution

I

I

0

d Ld I }

I

I

0

LAI

𝐿𝐴𝐼=𝑇𝑜𝑡𝑎𝑙1𝑠𝑖𝑑𝑒𝑙𝑒𝑎𝑓 𝑎𝑟𝑒𝑎𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑𝑎𝑟𝑒𝑎

LAIkeII 0

Horizontal leaves

Thin flat leaf

I

I

0i

Horizontal leavesCan be understood from optical path and cross-section area.

L A I c o s( )i

i

LAIkeII 0

Leaf angle distributionprobability density function: G(l)Random leaves

L A I ·

c o s ( )i

)cos(0

i

GLAIk

eII

I

I

0i

is the angle between sun beam and leaf normal

2/

0cos)(

ll dG

LAI

Cross-section area

Average ratio of shadow cast area onto horizontal surface to each single leaf area

Canopy reflectance

Boundary conditions

Hapke diffusive reflectance theory

Estimate FPAR from measured canopy reflectance

Suppose

)11

1(

12

kLeR

R

R

RFPAR

Vegetation indices

PART 2

The history of VIs

NDVI

Dr. John Rouse, the Director of the Remote Sensing Center of Texas A&M University where the Great Plains study was conducted with Landsat-1

PhD student Donald Deering and his advisor Dr. Robert Haas

With the assistance of a resident mathematician (Dr. John Schell)

To “normalize” the effects of the solar zenith angle

NDVI is simple, easy to use, by correlating with ground observation.and therefore, NDVI is over-used (abused)

Use NDVI forLAIfAPARfraction of vegetation coverwater contentpreciptationLeaf nitrogen contentchlorophyll concentration in leafBiomassPlant productivity (GPP/NPP)Vegetation stress monitoringVegetation disturbanceFlowering phenologyRats activityGrazing monitoring…

https://ecocast.adobeconnect.com/_a954016155/p3dz6o2fuv6/?launcher=false&fcsContent=true&pbMode=normal00:25:39 -00:27:10

NDVI~fAPAR

• Observed direct proportional relationship

• Attempt to prove it by prof. Knyazikhin• The well-known fAPAR product only use

this direct proportional relationship as backup algorithm to infer fAPAR from NDVI

y = 0.0045x + 1.0988R² = 0.94

1.0

2.0

3.0

4.0

5.0

6.0

0 260 520 780 1040 1300

RVI

Total wet biomass (g/m2)

y=5.5-exp(1.6641-0.0022·x)R2=0.86

Figure: RVI has a good linear relationship with total wet biomass (Data point are digitized from Tucker (1979)’s NDVI paper)

Vegetation isolines from Huete’s cotton field experiment

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2 0.3 0.4

NIR

refle

ctan

ce

Red reflectance

100%

97%

90%75%

60% 40% 25%20%

0%

Veg. cover %

vegetation is

oline

ND

V I iso

line

1

21

RVI

RedNIR

RedNIRNDVI

(- ,- )l l1 2

SAV

I iso

line

LRedNIR

RedNIR

lRedlNIR

lRedlNIRSAVI

12

12 )()(

For l1=l2=L/2

0.050.1

0.150.2

0.250.3

0.350.4

0.45

0

0.1

0.2

0.3

0.4

0.5

0 0.1 0.2 0.3 0.4 0.5

NIR

Red

LAI=0

LAI=0

.25

LAI=0

.5

LAI=

1

LAI=

2LAI =

3

soil brig

htness in

crease

Vegetation isoline modelled from Hapke diffusive reflectance(same as from SAIL model)

NDVI EVI EVI2 DVI RVI VPI LVI AVICV(±60) 0.05 0.13 0.13 0.18 0.16 0.24 0.20 0.23

NDVI EVI EVI2 DVI RVI VPI LVI AVICV(±15) 0.01 0.03 0.03 0.04 0.02 0.05 0.05 0.05

Principal plane

Perpendicular toprincipal plane

NDVI EVI EVI2 DVI RVI VPI LVI AVICV(±60) 0.04 0.08 0.07 0.08 0.12 0.10 0.09 0.10

NDVI EVI EVI2 DVI RVI VPI LVI AVICV(±15) 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01