Post on 17-Dec-2015
transcript
PhD remote sensing course, 2013 Lund University Understanding Vegetation indices PART 1
Understanding Vegetation indicesPART 1 : General Introduction
Talk byHongxiao Jin
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
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
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