1. (Kikwit region, Democratic Republic of the Congo)
Remote Sensing in Geography Education, illustrated by a vegetation dynamics study
(Kikwit region, Democratic Republic of the Congo)
Lieselot Vandenhoute, Lector Aardrijkskunde, Katho departement RENO
Sint-Jozefstraat 1, B-8820 Torhout, Belgium
Tel.: ++32 (0) Fax.: ++32 (0)
Lieselot Vandenhoute, Lector Aardrijkskunde, Katho departement RENO
Sint-Jozefstraat 1, B-8820 Torhout, Belgium
Tel.: ++32 (0) Fax.: ++32 (0)
Lieselot Vandenhoute, Lector Aardrijkskunde, Katho departement RENO
Sint-Jozefstraat 1, B-8820 Torhout, Belgium
Tel.: ++32 (0) Fax.: ++32 (0)
Lieselot Vandenhoute, Lector Aardrijkskunde, Katho departement RENO
Sint-Jozefstraat 1, B-8820 Torhout, Belgium
Tel.: ++32 (0) Fax.: ++32 (0)

2. Step 1: Situation of the study area
Geographical situation of the study area in the Democratic Republic of the Congo
Kolwezi .
. Lubmbashi
ANGOLA
ZAMBIA
Atlantic Ocean
. KINSHASA
. Kisangani
. Ilebo
. Mbandaka
Kindu .
. Kananga
. Kikwit
. Matadi
Bukavu .
Goma .
Kalemie .
REP. OF THE CONGO
GABON
CAMEROON
CENTRAL AFRICAN REPUBLIC
SUDAN
UGANDA
TANZANIA
RWA
BUR
Congo
. Bumba
. Gbadolite
Lualaba
Lake Tanganyika
200
400 km N

3. The increasing population growth shown in this graph is thought to have an enormous impact on the natural vegetation.
Source: to Lahmeyer, J., Congo (Kinshasa). Historical demographical data of the whole country. Population Statistics, 06/09/2002.

4. Dense forest in river valley
This area was chosen because of its dense population in comparison with other parts of the country. The increase of a dense rural population should have a clear impact on the natural vegetation.
Savannah plateau
Dense forest in river valley
Foto from Prof. Dr. Rudi Goossens. Universiteit Gent, Faculteit Wetenschappen, Opleiding Geografie

5. Step 2: Collecting Satellite Imagery
12500 meter
1965

6. 12500 meter N
False Colour Composite of a SPOT scene taken on the 2nd of July Spatial resolution: ± 20x20 m. Geometric accuracy: RMSE 59,500 m.
1987

7. False Colour Composite of an ASTER scene of the 21nd of July 2001 Western part of the study area. Resampled spatial resolution: ± 20x20 m. Geometric accuracy: RMSE 88,530 m.
12500 meter N
2001

8. Step 3: Image Classification
Palmerais
Forêt claire
Forêt galerie
Digitised vegetation categories on the Corona mosaic.
12500 meter N

9. N NDVI-classification of the SPOT scene. 12500 meter
12500 meter N
NDVI from –1 up to –0,60
NDVI from –0,59 up to –0,20
NDVI from –0,19 up to 0,00
NDVI from 0,01 up to 0,20
NDVI from 0,21 up to 0,40
NDVI from 0,41 up to 0,60
NDVI from 0,61 up to 0,80
NDVI from 0,81 up to 1,00
NDVI-classification of the SPOT scene.

10. N NDVI-classification of the ASTER scene. 12500 meter
12500 meter
NDVI-classification of the ASTER scene.
NDVI from –1 up to –0,60
NDVI from –0,59 up to –0,20
NDVI from –0,19 up to 0,00
NDVI from 0,01 up to 0,20
NDVI from 0,21 up to 0,40
NDVI from 0,41 up to 0,60
NDVI from 0,61 up to 0,80
NDVI from 0,81 up to 1,00

11. Multitemporal Colour CompositeN
12500 meter

12. Exercise 1:
Create a Satellite Images mosaic.
Exercise 2:
Digitize all tree vegetation on the Corona image.
Exercise 3:
Create a (false) colour composite selecting the correct the spectral bands.
Students learn how to:
Work with digital images
Interpret digital images
Work with photo editing software
Reduce the inaccuracies
EXTRA
- Georeference
Interpret a Corona image
Work with Remote Sensing software or GIS such as ILWIS or ArcView, …
Label the digitised objects and work with attributes
Create a (false) colour composite
Select the correct spectral bands
Stretch the spectral bands to become a clear and readable
image
Interpret a (false) colour composite
EXTRA:
Remove noise
Exercise 4:
Create a NDVI image.
Exercise 5:
Create a multi temporal colour composite.
Create an NDVI image
Combine different spectral bands
Visualise an NDVI image
Interpret an NDVI image
Work with the colour cube
Interpret satellite images
Interpret combined satellite images
Create binary images