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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

land cover mapping the dry season in continental Southeast Asia

Sarah Mubareka

Hans Jurgen Stibig


Objectives data for

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


Methods data for

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)


Methods data for

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)


Inconveniences in the data set data for

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


Masking unusable pixels data for

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)



Usable pixels data for

Cloud

Cloud shadow

Dilation

VZ > 45

No data

SWIR defect

Hot spot

Specular


Methods data for

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)


Sub-image composite data for

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


Pixel composites data for

Single criteria

Double criteria


Pixel composites data for

MaxDVI

MaNMiVZ

MaNMiRED

MaxNDVI

MaxNDWI

MaxNDDI

S10Dry

S10Wet


Pixel composites - visual interpretations data for

MaxDVI

(S1)

[MaxDVI=(NIR-red)]


Pixel composites - visual interpretations data for

MaxNDVI

(S1)

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


Pixel composites - visual interpretations data for

MaxNDVI

MinVZA

(S1)

MaxNDVI

MinRED

(S1)


Pixel composites - visual interpretations data for

MaxNDWI

(S1)

MaxNDDI

(S1)

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

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


Pixel composites - visual interpretations data for

S10Wet

S10Dry

[S10Wet=Minimum SWIR]

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


Methods data for

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.


Sensitivity Analysis: heterogeneous test sites data for

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


Sensitivity Analysis: heterogeneous test sites data for

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


Sensitivity Analysis: heterogeneous test sites data for

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


Sensitivity Analysis: heterogeneous test sites data for

View zenith angle distribution of pixels for S1 composites

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


Methods data for

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)


Study site source data for

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


Deciduous data for

grassland

agriculture

Mosaic

Evergreen

bamboo

Mixed

Selecting training sites


Land cover classes most confused data for

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%



MaxNDVI MinRED (S1) data for

Mosaic (S1)

MaxNDDI (S1)

S10Dry


Deciduous - mosaic data for

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


Homogeneous test sites data for

Viewing angle differences for these classes


rivers & lakes data for

Base classification

MaxNDVI MinRED (S1)

Mosaic (S1)

Possible evergreen

vs mixed forest

dry vegetation

MaxNDDI (S1)

S10Dry

Conclusions


Objectives data for

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


Objectives data for

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


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 since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that


Max NDDI adjustments since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that

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


Max NDDI adjustments since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that

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


Conclusions since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that

  • 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 since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that


Database for each since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that

pixel composite

  • Day

  • Month

  • Solar zenith angle

  • Solar azimuth angle

  • View azimuth angle

  • View zenith angle


MaN since too many parameters separating them exist (2 months of data vs 8; spilling over outside of dry season..), it can be said that

MaNMiRED

MaNMiVZ

EVERGREEN

High density

Med/low density

Mosaic

Wd & shrb

Study site source


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