Test of forest classification over Bavaria (Germany) using a SPOT-VGT pixel mosaic

Erwann FILLOL, Pamela KENNEDY, Sten FOLVING

•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

• 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

• 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

• 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

• 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

• 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

•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

• 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

• 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

• 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

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

Image: 26th of August 2000

Resulting mask

Image: 26th of August 2000

Resulting mask

Image: 26th of August 2000

Resulting mask

Image: 26th of August 2000

Resulting mask

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

Composite result

Composite result

Raw image (August 26th 2000)

MaNMiR MaN Classic

Test area : Bavaria (Germany)

Corine classification Resolution : 100 m

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

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

• 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

Red reflectance

NIR

reflectance

SW

IR

reflectance

NIR reflectance Red reflectance

SW

IR reflectance

Broad leaved forest

Coniferous forest

Mixed forest

Spectral separability

Corine classification SPOT-VGT composite

Classification Results

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

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

Classification Results

Broad leaved forest

Coniferous forest

Mixed forest

Corine classification SPOT-VGT composite

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

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

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

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

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

Classification sensitivity to sub-pixel forest density

LowMedium

High

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