1. Mapping land-cover in southern Africa Session 5 Stuart Martin & Mark Thompson Director AfriGEOSS Symposium 28 April 2016, Victoria Falls

2. Precursors to the mapping project Access to open, freely available and reliable imagery Landsat 8 Sentinel 2 This opens up new possibilities Multi-temporal datasets assist with a semi- automated mapping approach Multiple growing seasons Phenological characteristics

3. Land-cover mapping approach 24 Months 1990 2013 / 2014 2015

4. Land-cover for 10 southern African countries (2015) 10 Countries Victoria Falls

5. Semi-automated land-cover mapping

7. Land-cover versus land-use “Puristic” base land-cover classes Trees / Bush / Grass / Bare / Water (P/T) Semi-automated mapping approach 51 x spectrally dependent land-cover classes 7 x high level land-cover classes (product)

8. Portability of the mapping approach Khartoum, Southern Sudan Nigeria Lagos, Nigeria Paraguay

9. Land-cover versus land-use Creating a useable land-cover / land-use datasets Requires further processing (manual interpretation) or access to external dataset (masks) 72 x Land-cover classes (LCSS / LCML) Further enhancements Settlements Agriculture Non-pivot irrigation Water splits Mines

10. South African National Land-cover (1990–2013/14) 1990 2013/14

11. Quantifying land-cover change

12. Accuracy assessment Desktop approach Using high resolution imagery for sample sites 6,415 preselected sample sites

13. Summary Land-cover is consistent and repeatable Time and associated costs to map large areas is significantly reduced Data democracy has downstream benefits Not linked to a specific country or project Cross-border Pro-actively developing relevant datasets

14. 14 Thank you!