boston university graduate school of art and sciences lai and fpar estimation and land cover...

BOSTON UNIVERSITY

GRADUATE SCHOOL OF ART AND SCIENCES

LAI AND FPAR ESTIMATION AND

LAND COVER IDENTIFICATION WITH

MULTIANGLE MULTISPECTRAL SATELLITE DATA

by

YU ZHANG

 Submitted in partial fulfillment of the

Requirements for the degree of Doctor of Philosophy

(Total of 31 visuals)

DISSERTATION

Multiangle Remote Sensing

Multiangle remote sensing is simultaneous measurement along different look angles of reflected radiation from a target.

Examples:

• ATSR-2 (2 observation angles, 1km resolution)

• POLDER (up to 14 observation angles, 6km resolution)

• MISR (9 observation angles, 1.1km resolution)

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Land Cover (1)

What is land cover?• Land cover is simply a description of the kind of

vegetation at a location at a given time.

Shrubs Grasses

Broad Leaf Crops

Forests

Land Cover (2)

Why is land cover important?

• Land cover and land use changes inferred from vegetation maps are a direct evidence of the human and climate impact on the land.

• Most climate and biogeochemical models, as well as algorithms that estimate surface biophysical variables from remote sensing data, utilize vegetation maps to assign certain key parameters to reduce the number of unknowns.

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LAI and FPAR (1)

What?

• LAI – Green Leaf Area Index = one-sided green leaf area per unit ground surface area

• FPAR – Fraction of incident Photosynthetically Active Radiation Absorbed by the vegetation canopy

= APAR / IPAR

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LAI and FPAR (2)

Why?

• LAI is a key state variable in all land parameterization of climate, ecology, and hydrology models.

• FPAR is a key variable in terrestrial carbon models.

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Objectives

The objective of my research is to demonstrate the utility of multiangle multispectral remote sensing for estimation of LAI, FPAR and land cover.

Specifically,

• Prototype the MISR LAI/FPAR algorithm (Part I)

• Empirical and theoretical analysis f multiangle, multispectral data (Part II)

• Land cover classification with multiangle multispectral data (Part III)

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PART I: Prototyping MSIR LAI/FPAR Algorithm

• POLDER Data:

– ~6km resolution– Africa– Nov. 1996– Up to 14 multiangle data per pixel

• Biome Classification Map

BCM-Africa

Biome Classification Map derived from AVHRR data (8km)

The Algorithm

Metrics of multiangle observations and uncertainties

Algorithm

LAI & FPAR Solution Distribution Functions:

• mean• variance

LUT based inverse solution of the 3D transport equation

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Saturation Frequency

Saturation Frequency decreases using multiangle data

0

5

10

15

20

25

30

35

Single Angle MultiAngle

Brdlf Crops

Savannas

Brdlf Forests

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Dispersion of LAI

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4 5LAI

DL

AI

Multiangle Single-angle

Dispersion of LAI for single-angle retrievals and multiangle retrievals for broadleaf crops.

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Part I: Conclusions

• The MISR LAI/FPAR algorithm performs satisfactorily

• Retrieval accuracy increases in the case of multiangle inputs

Note: This work is published in Zhang et al, Prototyping of MISR LAI and FPAR algorithm with POLDER data over Africa. IEEE Trans. Geosci. Remote Sens. 38:2402-2418, 2000.

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Part II: Investigations of Multiangle Data

• Empirical Analysis

• BRDF?

• Angular signatures in spectral space?

• Theoretical Analysis

(will not be presented here)

BRDF

backscattering forward scattering

B.S. B.S.F.S.

B.S.

F.S.

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Angular Signature in Spectral Space

Multiangle Single-angle

Location Location

Length No

Orientation No

Intercept No

0

0.1

0.2

0.3

0.4

0.5

0 0.05 0.1 0.15 0.2 0.25 0.3

BRF at Red

BR

F a

t N

IR

Length of the signature

Location (DHR)

Orientation

Intercept

0

0.02

0.04

0.06

-80 -40 0 40 80

View Angle

Red

Ref

lect

ance

0

0.1

0.2

0.3

0.4

0.5

-80 -40 0 40 80

View Angle

NIR

Ref

lect

ance

Interpretation of the Angular Signature Indices

1) Location — Biome type

2) Intercept Indices — Vegetation ground cover

3) Length Indices — Canopy structure

4) Slope Indices — LAI

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IGBP-AS

Angular signatures in the red-NIR (near-infrared) spectral space of the ten land covers from Hansen et al. (2000) 1 km land cover map of North America.

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Part II: Conclusions

• We developed metrics that characterize the BRDF for use in land cover classification

• These metrics have a basis in transport theory

• Note: These works is described in a two-part series: Zhang et al., Required consistency between definitions and signatures with the physics of remote sensing I: empirical arguments. And II: theoretical arguments. Remote Sens. Environ. (Submitted in January 2001).

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Part III: Land Cover Classification with Angular Signature Indices

• Data• North America land cover training sites• POLDER Data (June 1997, North America)

• MethodsMANOVA, PCA, Correlation Matrix

• Classification Techniques• Decision tree classification• Maximum likelihood classification

Classification Variables

• Spectral• Location (2)

• Red, NIR

• Angular • Length, Slope, Intercept (3)• 3 measurement patterns (33=9)

• Total 9+2=11 variables

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Statistic1

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Variance of PCA

0

0.1

0.2

0.3

1 2 3 4 5 6 7 8 9 10 11

Principal Component

Prop

ortio

n of

Var

ianc

e

Data information content is larger than spectral variables only

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2-Classify

The maximum classification accuracies as functions of the number of variables used in the decision tree and maximum likelihood classification methods.

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11

Number of Variables

Ove

rall

Acc

urac

y (%

)

Decision Tree Classification

Maximum Likelihood Classification

Part III: Conclusions

• The statistical analyses confirm the idea that incorporating angular signature variables will improve biome classification.

• The maximum likelihood classification result indicates a improvement of classification accuracy using directional variables.

• Note: These works is prepared for publication: Zhang and Woodcock, Improve the land cover classification accuracy with multiangle remote sensing data. (In preparation, 2001).

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CONCLUDING REMARKS

My research demonstrates:

• Satisfactory performance of the MISR LAI/FPAR algorithm

• Multiangle data improve accuracy of LAI/FPAR retrievals

• It is possible to define simple metrics that characterize the BRDF – a complicated 4D function

• Multiangle data contain information useful for land cover classification

FUTURE DIRECTIONS

• Comprehensive analysis of MISR data to further develop these ideas( It is not my job! :)

• Introducing temporal domain in land cover classification activity.

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ACKNOWLEDGEMENTS

• Committee

• Fellow Graduate Student

• Data provider:• Leroy, Diner, McIver

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Thank you all!

• Questions Please…

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