(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)50 23 10 30 Fax.: ++32 (0)50 23 10 40 Email: lieselot.vandenhoute@katho.be Lieselot Vandenhoute, Lector Aardrijkskunde, Katho departement RENO Sint-Jozefstraat 1, B-8820 Torhout, Belgium Tel.: ++32 (0)50 23 10 30 Fax.: ++32 (0)50 23 10 40 Email: lieselot.vandenhoute@katho.be Lieselot Vandenhoute, Lector Aardrijkskunde, Katho departement RENO Sint-Jozefstraat 1, B-8820 Torhout, Belgium Tel.: ++32 (0)50 23 10 30 Fax.: ++32 (0)50 23 10 40 Email: lieselot.vandenhoute@katho.be Lieselot Vandenhoute, Lector Aardrijkskunde, Katho departement RENO Sint-Jozefstraat 1, B-8820 Torhout, Belgium Tel.: ++32 (0)50 23 10 30 Fax.: ++32 (0)50 23 10 40 Email: lieselot.vandenhoute@katho.be

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

The increasing population growth shown in this graph is thought to have an enormous impact on the natural vegetation. Source: to Lahmeyer, J., 2002. Congo (Kinshasa). Historical demographical data of the whole country. Population Statistics, http://www.library.uu.nl/wesp/populstat/Africa/congokic.htm. 06/09/2002.

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. 1988.

Step 2: Collecting Satellite Imagery 12500 meter 1965

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

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

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

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.

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

Multitemporal Colour Composite N 12500 meter

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