1 SWALIM Workshop June, Nairobi Monitoring Land Cover Dynamics in sub-Saharan Africa H.D. Eva, A. Brink and D. Simonetti

2 SWALIM Workshop June, Nairobi Outline Introduction Method Results Discussion The future

3 SWALIM Workshop June, Nairobi Monitoring Natural Ressources for Development Cooperation (MONDE) Land –Land cover change, land availability, pressure, degradation, phenology, fires Forest –Deforestation, logging, sustainable management Biodiversity –Protected areas, specific ecosystems Fresh water –Inland water quality, surface water availability in dry areas Coastal areas and marine resources –Fish resources, sensitive ecosystems, coastal pressure Urban areas –Environmental degradation around cities, urban growth Introduction

4 SWALIM Workshop June, Nairobi Key Questions - Thematic What have been the main land cover changes in sub-Saharan Africa over the last 25 years? What are the main drivers and pressures that have affected sub-Saharan Africa? What impacts do such land cover changes have? Introduction

5 SWALIM Workshop June, Nairobi Key Questions - Method Test the suitability of Remote Sensing data from different sources (i.e. MSS and TM) for detecting both spatial and thematic changes Test the sampling scheme Introduction

6 SWALIM Workshop June, Nairobi Methodology Sample of high (30-80m) resolution satellite data from the 1970’s (MSS), the 1980’s(TM) and 2000 (TM) Sample stratification by using the White eco-regions 57 samples subdivided into 511 sub-samples, resulting in 1% coverage of the area Land cover change assessment of 4 key classes: –Agriculture –Forest –Natural non forest vegetation –Barren –(water) Extrapolation of the results by direct expansion to eco-region and full sub- Saharan level Method

7 SWALIM Workshop June, Nairobi Stratification and Sampling Method

8 SWALIM Workshop June, Nairobi Sampling and Classification From each sample site 9 (20 by 20 km) boxes are extracted and independently classified using unsupervised clustering Classes regrouped into –Forest –Non Forest Natural Vegetation –Agriculture –Barren –(Water) Class identification carried out by expert knowledge with aid from existing maps, Africover (FAO), other ancillary data and Google Earth Method

9 SWALIM Workshop June, Nairobi Forest Cover Change in Madagascar Method

10 SWALIM Workshop June, Nairobi Agriculture expansion on Somalia Method

11 SWALIM Workshop June, Nairobi Irrigated Agriculture in Sudan Method

12 SWALIM Workshop June, Nairobi Land Abandonment in Angola Method

13 SWALIM Workshop June, Nairobi Results Sub-Saharan Africa’s land cover in the year 2000 Land cover changes for Sub-Saharan Africa – 1975 to 2000 Potential impacts Results

14 SWALIM Workshop June, Nairobi Sub-Saharan Africa’s land cover in the year 2000 Results

15 SWALIM Workshop June, Nairobi Distribution of land cover (%) by ecoregion for the year 2000 Results

16 SWALIM Workshop June, Nairobi Proportion of land cover (%) within each ecoregion for the year 2000 Results

17 SWALIM Workshop June, Nairobi Land cover changes for Sub-Saharan Africa – 1975 to 2000 T(%)C: +57 AAC: 4,937 AA(%)C: 2.3 T(%)C: -16 AAC: -2,853 AA(%)C: -0.7 T(%)C: -5 AAC: -2,356 AA(%)C: -0.2 T(%)C: +15 AAC: 263 AA(%)C: 0.6 Results

18 SWALIM Workshop June, Nairobi Loss of Natural Vegetation Results

19 SWALIM Workshop June, Nairobi Available Land after Agro-Ecological Zoning Results

20 SWALIM Workshop June, Nairobi Methodological Problems The images –MSS – TM use different path/row system(WRS1/WRS2). This often requires mosaicing of MSS images –Geo-referencing problems between MSS and TM –The capacity to extract changes between MSS and TM is severely compromised by noise, spatial resolution, spectral resolution and seasonal differences Discussion

21 SWALIM Workshop June, Nairobi Methodological Problems MSS – TM - Difference in Spatial resolution Discussion

22 SWALIM Workshop June, Nairobi Methodological Problems The Sampling –Stratification by eco-regions not ideal –1% sample is probably too low Discussion

23 SWALIM Workshop June, Nairobi Methodological Problems Classification and Visual Interpretation –Unsupervised classification and manual class identification is time consuming and needs expert knowledge Discussion

24 SWALIM Workshop June, Nairobi Discussion

25 SWALIM Workshop June, Nairobi 2007 and Future The future

26 SWALIM Workshop June, Nairobi 2007 and Future Senegal – Sampling 68 Samples The future

27 SWALIM Workshop June, Nairobi 2007 and Future Tanzania – Sampling 310 Samples The future