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Evaluating different compositing methods using SPOT-VGT S1 data for land cover mapping the dry season in continental Southeast Asia. Sarah Mubareka. Hans Jurgen Stibig. Objectives.

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Evaluating different compositing methods using spot vgt s1 data for

Evaluating different compositing methods using SPOT-VGT S1 data for

land cover mapping the dry season in continental Southeast Asia

Sarah Mubareka

Hans Jurgen Stibig


Evaluating different compositing methods using spot vgt s1 data for

Objectives

1. To maximise the SPOT-VGT S1 data set potential in mapping land cover in continental Southeast Asia for the dry season (January & February 2000)

2. To compare S1 composites to S10 composites for the dry season vegetation mapping


Evaluating different compositing methods using spot vgt s1 data for

Methods

1. Masking “unusable” pixels

2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based)

3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using

A. General heterogeneous test sites (50x50 pixels)

B. Land-cover-specific test sites (180-600 pixels)


Evaluating different compositing methods using spot vgt s1 data for

Methods

1. Masking “unusable” pixels

2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based)

3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using

A. General heterogeneous test sites (50x50 pixels)

B. Land-cover-specific test sites (180-600 pixels)


Evaluating different compositing methods using spot vgt s1 data for

Inconveniences in the data set

1

2

3

1

4

1: The gap between orbit

0 and 1 of the same day

resulting in one or two

black bands within each

scene

2: Defective SWIR

detectors, resulting in a

streak appearing in

some scenes

3: Pixels buffering the error

in (2) resembling pixels

representing land cover

4: Cloud and cloud shadow


Evaluating different compositing methods using spot vgt s1 data for

Masking unusable pixels

SWIR strip masks

Viewing and solar angles

S1 Jan & Feb

SWIR

band bitmap

Δθ=0º and Δφ=0º (±20º)

yes

yes

yes

Bany=0

Mask=6

Mask=2

Mask=5

no

Dilation

Blue>720

SWIR>320

no

yes

Δθ=0º and Δφ=180º (±20º)

Mask=1

yes

Mask=7

Mask=3

no

Dilation

no

Mask=3

pThrs>45

yes

Mask=4

Cloud shadow angle

no

yes

Usable pixels

Mask=8

(Lissens,

2000)

(Fillol 1999,

Simpson1998)


Evaluating different compositing methods using spot vgt s1 data for

February 24, 2000 image


Evaluating different compositing methods using spot vgt s1 data for

Usable pixels

Cloud

Cloud shadow

Dilation

VZ > 45

No data

SWIR defect

Hot spot

Specular


Evaluating different compositing methods using spot vgt s1 data for

Methods

1. Masking “unusable” pixels

2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based)

3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using

A. General heterogeneous test sites (50x50 pixels)

B. Land-cover-specific test sites (180-600 pixels)


Evaluating different compositing methods using spot vgt s1 data for

Sub-image composite

METHOD (theoretically..):

-Each image in the database is

divided into a 12x12 grid

-The least polluted sub-image

is selected

-Unsupervised classification

per sub-image followed by

fusion of classified sub-

images

GLITCHES

-Visible seam, difficult to

calibrate sub-images to

reduce contrast

-Not a completely cloud-free

image


Evaluating different compositing methods using spot vgt s1 data for

Pixel composites

Single criteria

Double criteria


Evaluating different compositing methods using spot vgt s1 data for

Pixel composites

MaxDVI

MaNMiVZ

MaNMiRED

MaxNDVI

MaxNDWI

MaxNDDI

S10Dry

S10Wet


Evaluating different compositing methods using spot vgt s1 data for

Pixel composites - visual interpretations

MaxDVI

(S1)

[MaxDVI=(NIR-red)]


Evaluating different compositing methods using spot vgt s1 data for

Pixel composites - visual interpretations

MaxNDVI

(S1)

[NDVI=(NIR-red)/(NIR+red)]


Evaluating different compositing methods using spot vgt s1 data for

Pixel composites - visual interpretations

MaxNDVI

MinVZA

(S1)

MaxNDVI

MinRED

(S1)


Evaluating different compositing methods using spot vgt s1 data for

Pixel composites - visual interpretations

MaxNDWI

(S1)

MaxNDDI

(S1)

[NDWI=(NIR-SWIR)/(NIR+SWIR)]

[NDDI=(SWIR-NIR)/(SWIR+NIR)]


Evaluating different compositing methods using spot vgt s1 data for

Pixel composites - visual interpretations

S10Wet

S10Dry

[S10Wet=Minimum SWIR]

[S10Dry=Minimum NIR if pixel is not green for S10Wet]


Evaluating different compositing methods using spot vgt s1 data for

Methods

1. Masking “unusable” pixels

2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based)

3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using

A. General heterogeneous test sites (50x50 pixels)

B. Land-cover-specific test sites (180-600 pixels)

De Wasseige

et al.


Evaluating different compositing methods using spot vgt s1 data for

Sensitivity Analysis: heterogeneous test sites

ex.

Zone 8

ex.

Zone 2

Sensitivity to atmosphere: reflectance in blue channel

  • Mosaic is inconsistently sensitive in the blue channel

  • MaxDVI most affected

  • MaxNDVI composites least affected

  • S10 composites moderately affected


Evaluating different compositing methods using spot vgt s1 data for

Sensitivity Analysis: heterogeneous test sites

ex.

Zone 1

ex.

Zone 3

Sensitivity to vegetation: reflectance in NIR channel

  • S10Dry underestimates green vegetation

  • maxNDVI composites tend to overestimate green vegetation cover


Evaluating different compositing methods using spot vgt s1 data for

Sensitivity Analysis: heterogeneous test sites

ex.

Zone 6

ex.

Zone 2

Texture Variance

  • Mosaic can be used as control (least speckle)

  • The composite with the least speckle is MaNMiRED

  • S10 composites are mostly sensitive over dry zones


Evaluating different compositing methods using spot vgt s1 data for

Sensitivity Analysis: heterogeneous test sites

View zenith angle distribution of pixels for S1 composites

No composite consistently selects near-nadir pixels (except MaNMiVZ) - regardless of land cover type


Evaluating different compositing methods using spot vgt s1 data for

Methods

1. Masking “unusable” pixels

2. Generating cloud-free composites using pixel and sub-image compositing based on 1 and 2 criteria methods (vegetation index-based)

3. Evaluating composite sensitivity to atmosphere and vegetation; view angle preference; and texture variance using

A. General heterogeneous test sites (50x50 pixels)

B. Land-cover-specific test sites (180-600 pixels)


Evaluating different compositing methods using spot vgt s1 data for

Study site source

Mekong River Commission 1997 forest cover map (based on TM classification)


Evaluating different compositing methods using spot vgt s1 data for

Deciduous

grassland

agriculture

Mosaic

Evergreen

bamboo

Mixed

Selecting training sites


Evaluating different compositing methods using spot vgt s1 data for

Land cover classes most confused

high density

evergreen

medium/low density

Continuous

forest cover

deciduous

mixed

high density

Forest

regrowth

evergreen

Mosaic of

forest cover

deciduous

mixed

Wood & shrubland

evergreen

Rock

Agriculture

Non-forest

Bamboo

Grass

cropping area >30%

Mosaic of cropping

cropping area <30%


Evaluating different compositing methods using spot vgt s1 data for

Homogeneous test sites


Evaluating different compositing methods using spot vgt s1 data for

MaxNDVI MinRED (S1)

Mosaic (S1)

MaxNDDI (S1)

S10Dry


Evaluating different compositing methods using spot vgt s1 data for

Deciduous - mosaic

Grassland

Deciduous - continuous

Agriculture

Homogeneous test sites

Isolating clear classes in maxNDDI

In order to detect which classes are not clouded over in the maxNDDI composite, we compare reflectance values for the NIR bands.

IF NIRmaxNDDI > NIRMaNMiRED, then class is retained for classification with maxNDDI


Evaluating different compositing methods using spot vgt s1 data for

Homogeneous test sites

Viewing angle differences for these classes


Evaluating different compositing methods using spot vgt s1 data for

rivers & lakes

Base classification

MaxNDVI MinRED (S1)

Mosaic (S1)

Possible evergreen

vs mixed forest

dry vegetation

MaxNDDI (S1)

S10Dry

Conclusions


Evaluating different compositing methods using spot vgt s1 data for

Objectives

1. To maximise the SPOT-VGT S1 data set potential in mapping land cover in continental Southeast Asia for the dry season (January & February 2000)

2. To compare S1 composites to S10 composites for the dry season vegetation mapping


Evaluating different compositing methods using spot vgt s1 data for

Objectives

1. To maximise the SPOT-VGT S1 data set potential in mapping land cover in continental Southeast Asia for the dry season (January & February 2000)

2. To compare S1 composites to S10 composites for the dry season vegetation mapping


Evaluating different compositing methods using spot vgt s1 data for

Though the S1 and S10 composites cannot be compared directly since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that

1. A more cloud-free image is obviously possible with the S10 composites (for filling holes of missing data?)

2. Since MaxNDVI criteria is used for generating the 10-day data set, it is difficult to assess the degree to which green vegetation is exaggerated and therefore may affect the borders between green vegetation and other land cover


Land cover mapping

Land cover mapping


Evaluating different compositing methods using spot vgt s1 data for

Max NDDI adjustments

High within class variance for composite max NDDI (ex zone 8):


Evaluating different compositing methods using spot vgt s1 data for

Max NDDI adjustments

High within class variance for composite max NDDI (ex zone 8):


Evaluating different compositing methods using spot vgt s1 data for

Conclusions

  • Classification approach: By ecosystem

  • Classification method

    • hybrid unsupervised and supervised

    • integration of vegetation index channels

    • fusion of classifications :

  • 1/ Combination of MaNMiRED (used for most

  • classes), mosaic, MaxNDDI (by masking classes)

  • 2/ Classification of sub-images using S10 composites for filling cloud-contaminated zones

  • Areas for improvement

  • Masking parameters: hot spot/specular zones; cloud height estimation; automating SWIR sensor defect masking; cloud/haze thresholding

  • Bi-directional effects: normalisation of pixels to a common geometry


Appendix

Appendix


Evaluating different compositing methods using spot vgt s1 data for

Database for each

pixel composite

  • Day

  • Month

  • Solar zenith angle

  • Solar azimuth angle

  • View azimuth angle

  • View zenith angle


Evaluating different compositing methods using spot vgt s1 data for

MaN

MaNMiRED

MaNMiVZ

EVERGREEN

High density

Med/low density

Mosaic

Wd & shrb

Study site source


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