Date post: | 22-Dec-2015 |
Category: |
Documents |
View: | 219 times |
Download: | 5 times |
Ph. D. Dissertation defense
Evaluation of the Performance of the MODIS LAI and FPAR Algorithm with Multiresolution
Satellite Data
Yuhong Tian Department of Geography, Boston University
Dissertation Committee
Ranga B. MyneniYuri KnyazikhinMark A. Friedl
Curtis E. WoodcockAlexander L. Marshak
1 of 39
Contents
1. Introduction
2. Objectives
3. Research Topics Prototyping of the MODIS LAI/FPAR algorithm with LASUR
and Landsat data Radiative transfer based scaling of LAI/FPAR retrievals from
reflectance data of different resolutions Multiscale analysis and validation of the MODIS LAI over
Maun, Botswana
4. Concluding Remarks
5. Future Work2 of 39
1. Introduction
LAI and FPAR:
• Definition
LAI: green leaf area index, one-sided green leaf area per unit ground area.
FPAR: fraction of photosynthetically active radiation (0.4- 0.7 m) absorbed by the vegetation.
• Importance
They are key variables in land surface models for calculation of surface photosynthesis, evapotranspiration, and net primary production.
3 of 39
2. Objectives
Prototyping:1. To test the physical functionality and performance of the algorithm
with MODIS like data.
Effects of spatial resolution:1. To inquire about the cause of the discrepancy between coarse and
moderate resolution output from the same algorithm.
2. To investigate the adjustment of retrieval techniques for data
resolution.
Validation:1. To derive uncertainty information on LAI/FPAR product by
comparing with field data.
2. To define a sampling strategy. 4 of 39
3. Research Topics
Part One
Prototyping of the MODIS LAI/FPAR Algorithm with LASUR and Landsat data
Tian et al., “Prototyping of MODIS LAI and FPAR algorithm with LASUR and Landsat data”. IEEE Trans. Geosci. Remote Sens. 38(5):2387-2401, 2000.
5 of 39
Research Topics: Part one
Introduction
Prototyping: use data from other instruments to test the functionality of the MODIS algorithm before MODIS data are available.
6 of 39
Research Topics: Part one
Data
• Land surface reflectances (LASUR). - spatial resolution: 1/7th of a degree - RED (572-698 nm) and NIR (716-985 nm) from July 1989
• A TM image of Northwest U.S. (Washington and Oregon). - 30 m resolution - RED (630-690 nm) and NIR (760-900 nm) from June 26, 1987.
• A biome classification map (BCM). - Grasses and cereal crops - Shrubs - Broadleaf crops - Savannas - Broadleaf forests - Needle forests
7 of 39
Research Topics: Part one
Impact of Biome Misclassification
Misclassified Biome Type
Grasses and Cereal Crops
Shrubs
Broadleaf Crops
Savannas
Broadleaf Forest
Needle Forests
BCM
Biome Type
Grasses and Cereal Crops
Shrubs
Broadleaf Crops
Savannas
Small effect Large effect
Broadleaf Forest
Needle ForestsLarge effect Small effect
9 of 39
Research Topics: Part one
Scale Dependence of the Algorithm
LANDSATFine
resolution
LASUR Coarse
resolution
10 of 39
Research Topics: Part one
Conclusions
• Prototyping results demonstrate the ability of the algorithm to produce global LAI and FPAR fields.
• The LAI and FPAR fields follow regularities expected from physics.
• The algorithm is dependent on the spatial resolution of the data.
11 of 39
Research Topics
Part Two
Radiative transfer based scaling of LAI/FPAR retrievals from reflectance data of different
resolutions
Tian, et al., “Radiative transfer based scaling of LAI/FPAR retrievals from reflectance data of different resolutions”. Remote Sens. Environ., 2001 (in
review). 12 of 39
Research Topics: Part two
Introduction
The goal of scaling: values of LAI derived from coarse resolution sensor data should equal the arithmetic average of values derived independently from fine resolution sensor data.
Scaling issues arise when
• one attempts to assemble a consistent time series of LAI/FPAR products with data from different spatial resolutions.
• one attempts to validate moderate resolution (~ 1 km) sensor products with field measurements at much finer resolutions.
13 of 39
Research Topics: Part two
Objectives
• To investigate the effect of pixel heterogeneity on LAI/FPAR retrievals.
• To develop a physically based theory for scaling with scale dependent radiative transfer formulation.
14 of 39
Research Topics: Part two
Data
• AVHRR land surface reflectances at 1 km resolution over North America for July 1995
- RED (580-680 nm) and NIR (725-1100 nm). - 1 km AVHRR reflectance data were aggregated to 4, 8, 16, 32 and 64 km resolutions.
• A six biome map of North America
- developed from 1 km AVHRR NDVI data of 1995 and 1996 by Lotsch et al. (2000).
15 of 39
Research Topics: Part two
Characterizing Land Cover Heterogeneity
• Percentage function (pf). B1 B1 B4 B4
B5 B5 B5 B4
B4 B5 B5 B5
B5 B5 B1 B5
pf1=3/16; pf4=4/16; pf5=9/16
Purity of this pixel=pf5=9/16
A 4 km x 4 km resolution pixel
16 of 39
The percentage occupation of subpixel biome type in a given coarse resolution pixel.
• “purity” of a pixel.
Percentage function of dominant biome type in a given coarse resolution pixel.
Research Topics: Part two
Purity Decreases as Spatial Resolution Decreases
Resolution (km) Resolution (km)
Biome purity > 90%
Per
cent
age
of p
ixel
s
Per
cent
age
of p
ixel
s
Biome purity < 50%
17 of 39
Research Topics: Part two
Purity Has a Strong Effect on LAI Retrievals
Error = |LAItrue-LAIestimated|/LAItrue
18 of 39
B1 B1 B4 B4
B5 B5 B5 B4
B4 B5 B5 B5
B5 B5 B1 B5
B5 B5 B5 B5
B5 B5 B5 B5
B5 B5 B5 B5
B5 B5 B5 B5
Dom
inan
t L
and
Cov
er P
uri
ty
a) Reflectance at 4 km resolution
b) Biome type
Input:
Research Topics: Part two
Energy conservation law as a tool to scale models
19 of 39
Leaf absorption: (1-)Noi
Leaf scattering: Noi
Leaf interception: pNoi
Noi
Not
(1-p)N0i
Ni= Noi + pNoi+ (p)2Noi+…
= Noi/(1-p)
Nt= Not/(1-pt)
= 1/(1-pfsoil)jpfj
N
N = NR + Nt + (1-)Noi
: leaf albedo, the portion of radiation flux density incident on the leaf surface that the leaf transmits and reflects.p: the fraction of photons that are scattered by leaves and will interact with leaves again.
Research Topics: Part two
20 of 39
Dom
inan
t L
and
Cov
er P
uri
ty
Improved Retrieval Accuracy
Before After
Dom
inan
t L
and
Cov
er P
uri
ty
Research Topics: Part two
Conclusions
• LAI retrieval errors are inversely related to the proportion of the dominant land cover in a coarse resolution pixel.
• Pixel heterogeneity must be accounted to improve accuracy in retrievals.
21 of 39
Research Topics
Part Three
Multiscale analysis and validation of the MODIS LAI over Maun, Botswana
Tian, et al., “Multiscale analysis and validation of the MODIS LAI over Maun,
Botswana”. Remote Sens. Environ., 2001 (submitted in October, 2001). 22 of 39
Research Topics: Part three
Introduction
As MODIS LAI and FPAR data start to become publicly available, product quality must be ensured by validation.
Validation: the process of assessing the uncertainty of data products by comparison to reference data (e.g., in situ, aircraft, and high-resolution satellite sensor data).
23 of 39
Research Topics: Part three
Objectives
• To develop an appropriate ground-based validation technique for assessing the uncertainties in MODIS LAI product.
• To develop sampling strategies to collect data needed for validation of the MODIS LAI product.
24 of 39
Research Topics: Part three
Data
• LAI measured by LAI-2000 Plant Canopy Analyzer.
• Landsat ETM+ (30 m) data.
• MODIS reflectance data (1 km) simulated from ETM+.
25 of 39
Research Topics: Part three
Pandamatenga
S 18 39.5
E 25 29.8
Maun
S 19 55.8
E 23 30.7
Okwa
S 22 24.6
E 21 42.8
Tshane
S 24 10.1
E 21 53.3
26 of 39
Sampling Scheme 1000 m
1000 m
N375W 0 N375E
A375W A375E
B375W B375E
750 m
250 m250 m
300 m
250m
A
C
D
E
F
B
1 2 75 643 START POINT
25m
N
Research Topics: Part three
27 of 39
Research Topics: Part three
Validation of the MODIS LAI Product At Maun
Field data ETM+ MODIS product
Problems with validation
• Only four pairs of pixels between field measurements and MODIS data.
• Spatial registration is not accurate.
28 of 39
Research Topics: Part three
ETM+ Image Segmentation Map
12
3
4
5
6 7
89
10
1112
1314 15
Patch by Patch Comparison
Shortcomings of pixel by pixel comparison
• GPS readings are not accurate.• Measured LAI values have high variation over short
distances. 29 of 39
Research Topics: Part three
Consistency between LAI Retrievals and Field Measurements
30 of 39
sLAI-Field Measurements
LA
I-A
lgor
ithm
Ret
riev
als
Research Topics: Part three
D1
D2
D3
Hierarchical Analysis of Multiscale Variation in LAI Data
Four scale levels: whole image > class > region > pixel
D1=D11+ D12+D13
D2=D21+ D22
D3=D31+ D32+D33
D11
D12
D13
D31D32
D33
D22
D21
32 of 39
Four images: image effect, class effect, region effect, pixel effect
Research Topics: Part three
Distance (h)
Sem
ivar
ianc
e (
) sill
range
Semivariogram Analysis for 4 Scale Levels
)(
2)]()([)(2
1)(
hN
xZhxZhN
h
33 of 39
Three Sites
Maun (Botswana)
Research Topics: Part three
Harvard Forest(USA)
Ruokolahti Forest (Finland)
34 of 39
LAI Semivariograms
Maun
Research Topics: Part three
Harvard Forest Ruokolahti Forest
35 of 39
Pixel EffectRegion EffectClass EffectOriginal Image
Sem
ivar
ianc
e
Sem
ivar
ianc
e
Sem
ivar
ianc
e
Research Topics: Part three
Conclusions
• Consistency between LAI retrievals from 30 m ETM+ data and field measurements indicates satisfactory performance of the algorithm.
• Hierarchical variance analysis shows that the LAI retrievals from ETM+ data demonstrate multiple characteristic scales of spatial variation.
1. Within the three sites, patterns of variance in the class, region, and pixel scale are different with respect to the importance of the three levels of landscape organization.
2. The spatial structure is small across the three sites. Validation needs to be performed over small areas.
3. For validation activities, patches are better than individual pixels unless sample and registration accuracy are outstanding.
36 of 39
4. Concluding Remarks
• Prototyping results demonstrate the ability of the algorithm to produce global LAI and FPAR fields. The LAI and FPAR fields follow regularities expected from physics.
• LAI retrieval errors are inversely related to the proportion of the dominant land cover in a coarse resolution pixel.
• A physically based theory for scaling with a scale dependent radiative transfer formulation was developed.
37 of 39
• Consistency between LAI retrievals from 30 m Landsat ETM+ data and field measurements from Maun (Botswana) indicates satisfactory performance of the algorithm.
• LAI fields demonstrate multiple characteristic scales of spatial variation. Isolating the effects associated with different scales through variograms aids the development of a new sampling strategy for validation of MODIS products.
38 of 39
5. Future Work
• Use MODIS products to improve representation of the land surface in global climate models using the scaling ideas developed here.
39 of 39
The MODIS LAI/FPAR algorithm
1),(),,(1
2
1
00
N
k k
vkvk dpr
N
rk: modeled BRDF
dk: satellite measured BRDF
k: uncertainties in measurements and simulations
p=[canopy, soil]
1 2 3 4 5LAI
Freq
uenc
y
Solution distribution function
Research Topics: Part one
Retrieval Index Depends on Quality of Surface Reflectance
Uncertainty
Retrieval Index: ratio of the number of retrieved pixels to total number of pixels.
Xijk = I + Ci + Rij + Pijk
I: the image effect; Ci: the effect associated with class i;
Rij: the effect associated with region j of class i;
Pijk: the pixel effect associated with pixel k of region j of class i.
Research Topics: Part three
I = (D)
Ci = (Di) - (D)
Rij = (Dij) - (Di)
Pijk = Xijk - (Dij)
Image decomposition