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Test of forest classification over Bavaria (Germany) using a SPOT-VGT pixel mosaic
Erwann FILLOL, Pamela KENNEDY, Sten FOLVING
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•To create a cloud-free image of Europe by pixel compositing SPOT-VGT S1data
•To evaluate the efficiency of the Maximum NDVI / Minimum Red (MaNMiR) pixel compositing method in discriminating three types of forest cover: Evergreen, deciduous, and mixed through classic classification methods
Objectives
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• SPOT-VGT S1 : 45 daily acquisitions for the months of July and August, 2000 for all of Europe
• CORINE Land Cover database (CLC) : 44 classes for 3 hierarchical levels (Artificial surfaces, Agricultural areas, Forests and semi-natural areas), obtained in part using Landsat TM imagery (resolution: 100 meters)
Databases
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• Cloud cover (Lissens et al.)
if blue> 0.36 and swir> 0.16 Cloud
• Dilation of cloud cover – 5kmx5km window
• Elimination of cloud shadow
• Scan angle limitation
If v > 45° Ground resolution degradation
• SWIR detector defects
If swir > 0.75 SWIR defect
• Hot-Spot and Specular limitation
Pre-processing: Compositing MASKING
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• Cloud cover (Lissens et al.)
if blue> 0.36 and swir> 0.16 Cloud
• Dilation of cloud cover - 5x5 window
• Elimination of cloud shadow
• Scan angle limitation
If v > 45° Ground resolution degradation
• SWIR detector defects
If swir > 0.75 SWIR defect
• Hot-Spot and Specular limitation
Pre-processing: Compositing MASKING
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• Cloud cover (Lissens et al.)
if blue> 0.36 and swir> 0.16 Cloud
• Dilation of cloud cover - 5x5 window
• Elimination of cloud shadow
• Scan angle limitation
If v > 45° Ground resolution degradation
• SWIR detector defects
If swir > 0.75 SWIR defect
• Hot-Spot and Specular limitation
Pre-processing: Compositing MASKING
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• Cloud cover (Lissens et al.)
if blue> 0.36 and swir> 0.16 Cloud
• Dilation of cloud cover - 5x5 window
• Elimination of cloud shadow
• Scan angle limitation
If v > 45° Ground resolution degradation
• SWIR detector defects
If swir > 0.75 SWIR defect
• Hot-Spot and Specular limitation
Pre-processing: Compositing MASKING
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•Solar zenith and azimuth angles are known•Cloud height minimum and maximum are estimated •The distance [d=h/tan(90- s)] and direction of the cloud shadow can be estimated
Cloud shadow elimination
h s
hmin=2kmhmax=12km
d s
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• Cloud cover (Lissens et al.)
if blue> 0.36 and swir> 0.16 Cloud
• Dilation of cloud cover - 5x5 window
• Elimination of cloud shadow
• Scan angle limitation
If v > 45° Ground resolution degradation
• SWIR detector defects
If swir > 0.75 SWIR defect
• Hot-Spot and Specular limitation
Pre-processing: Compositing MASKING
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• Cloud cover (Lissens et al.)
if blue> 0.36 and swir> 0.16 Cloud
• Dilation of cloud cover - 5x5 window
• Elimination of cloud shadow
• Scan angle limitation
If v > 45° Ground resolution degradation
• SWIR detector defects
If swir > 0.75 SWIR defect
• Hot-Spot and Specular limitation
Pre-processing: Compositing MASKING
![Page 11: Test of forest classification over Bavaria (Germany) using a SPOT-VGT pixel mosaic Erwann FILLOL, Pamela KENNEDY, Sten FOLVING](https://reader036.vdocuments.us/reader036/viewer/2022062410/5697bfa41a28abf838c972f9/html5/thumbnails/11.jpg)
• Cloud cover (Lissens et al.)
if blue> 0.36 and swir> 0.16 Cloud
• Dilation of cloud cover - 5x5 window
• Elimination of cloud shadow
• Scan angle limitation
If v > 45° Ground resolution degradation
• SWIR detector defects
If swir > 0.75 SWIR defect
• Hot-Spot and Specular limitation
Pre-processing: Compositing MASKING
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90
180
hotspot speculardhs dspec
dcruxhs
dcruxspec
22hsd
22spec 180d
To minimise directional effects, the acquisitions situated near the hot spot and specular zones (± 20°) are eliminated
Hot-Spot and Specular limitation
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Image: 26th of August 2000
Resulting mask
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Image: 26th of August 2000
Resulting mask
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Image: 26th of August 2000
Resulting mask
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Image: 26th of August 2000
Resulting mask
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Pre-processing: Compositing DOUBLE CRITERIA COMPOSITING
Double criteria compositing :
• Maximum NDVI (MaN), to eliminate haze and unscreened pixels top 15% retained
• Minimum reflectance in the red channel (MiR), to limit atmospheric effects and enhance green vegetation
D’Iorio and al., 1991
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Composite result
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Composite result
Raw image (August 26th 2000)
MaNMiR MaN Classic
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Test area : Bavaria (Germany)
Corine classification Resolution : 100 m
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Corine classification Resolution : 100 m
Test area : Bavaria (Germany)
Test site selection based on:
• little topographic effect
• 3 forest types present: coniferous, deciduous, mixed
• site is representative of temperate forests
200 km
300 km
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Test area : Bavaria (Germany)
Arable land43%
Urban6%
Grass land1%
Transitional wood-shrub
1%
Mixed forest5%
Coniferous forest24%
Broad-leaved forest
4%
Agriculture10% Pasture
6%
Corine classification Resolution : 100 m
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• Maximum Like-lihood algorithm
• Training site selected over a homogeneous area (according to Corine classification)
• Using channels SWIR, NIR & Red
• 3 classes : Coniferous, deciduous, mixed forests
Classification
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Red reflectance
NIR
reflectance
SW
IR
reflectance
NIR reflectance Red reflectance
SW
IR reflectance
Broad leaved forest
Coniferous forest
Mixed forest
Spectral separability
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Corine classification SPOT-VGT composite
Classification Results
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Classification Results
Broad leaved forest
Coniferous forest
Mixed forest
Corine SPOT-VGT
Coniferous 24 % 20.2 %
Broad-Leaved
4 % 1.4 %
Mixed 5 % 3.0 %
Non-Forest 67 % 75.4 %
Corine classification SPOT-VGT composite
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Dense forest zones most accurately classified
Over estimation in sparse forest due to surrounding (pasture)
Classification Results
Broad leaved forest
Coniferous forest
Mixed forest
Corine classification SPOT-VGT composite
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Classification Results
Broad leaved forest
Coniferous forest
Mixed forest
Corine classification SPOT-VGT composite
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Classification Results
Broad leaved forest
Coniferous forest
Mixed forest
Under estimation in sparse and fragmented forest.
Surrounding : Non-irrigated arable land
Corine classification SPOT-VGT composite
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Coniferous from SPOT-VGT
Mixed forest7%
Broad-leaved forest1%
Agriculture7%
Pasture3%
Transitional wood-shrub
2%
Arable land8%
Coniferous forest71%
Urban1%
Composition of actual land cover (based on CLC) classified as Coniferous according to SPOT-VGT
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Composition of actual land cover (based on CLC) classified as Broad-Leaved according to SPOT-VGT
Grass land2%
Broad-leaved forest48%
Coniferous forest8%
Mixed forest17%
Arable land7%
Pasture7%
Agriculture9%
Transitional wood-shrub
2%
Broad-Leaved from SPOT-VGT
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Urban1%Grass land
1%
Mixed forest21%
Coniferous forest29%
Broad-leaved forest10%
Agriculture16%
Pasture9%
Arable land11%
Transitional wood-shrub
2%
Mixed from SPOT-VGT
Composition of actual land cover (based on CLC) classified as Mixed Forest according to SPOT-VGT
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Grass land1%
Transitional wood-shrub
1%Mixed forest4%
Coniferous forest12%
Broad-leaved forest3%
Agriculture12%
Pasture6%
Arable land53%
Urban8%
Non-Forest from SPOT-VGT
Composition of actual land cover (based on CLC) classified as Non-Forest according to SPOT-VGT
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Classification sensitivity to sub-pixel forest density
LowMedium
High
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Conclusions and discussion
• High quality composites are possible with Spot-VGT
• High potential in discriminating dense coniferous wood-land
• Must be careful with area estimation of forest cover in Europe, especially in fragmented and mixed forest
• Potential of combining medium resolution radiometer like IRS-WiFS (200m resolution) and low resolution SPOT-VGT