2. Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University

3. Vegetation dynamics of Kalahari ecosystems savannas, shrublands & grasslands occupy 53 million km2... including 60% of Africa state and transition mosaics support sustainable agriculture

4. Reducing / removing uncertainties Vegetation community distribution Timing and nature of change Vegetation dynamics at regional scales Aims: to develop remote sensing method(s) to characterise vegetation community dynamics to understand the limits on those methods at local to regional scales

5. Method 1 Analyse reflectance properties of individual landscape components –H0: there is no difference in the reflectances 2 Simulate the composite reflectance of vegetation communities –Develop a landscape reflectance model –H0: vegetation community structure is not related to reflectance 3 Apply model to analyse regional vegetation dynamics

6. Study areas Southern Kalahari Tshane Kalaghadi Transfrontier Park Tsabong Severn Eastern Kalahari Makoba <40% vegetation cover

7. Field & lab measurements Field & Lab spectroscopy Landscape components

8. Experiment 1A: differences in the reflectances of landscape components Note: data normalised for % cover grass > bush visible ~ 3% - 5% near-infrared 0% soil > vegetation

9. Landsat TM4

10. Experiment 1b: variation in soil reflectance 680 nm 850nm principal plane orthogonal plane Differences of ~2% Soil crust ~6% increase

11. Experiment 2: simulating reflectance using a canopy reflectance model Simulated reflectance =  landscape component x reflectance Grass cover Bush cover

12. Results: part 1 Significant monospectral differences between soil and vegetation Vegetation community structure not related to reflectance Dimensionality of single-date imagery limits local scale applications

13. Moving forward... 3 Analyse time series of Earth observation data –H0: there is no difference in the temporal profiles of vegetation communities 4 Apply model to analyse regional vegetation dynamics

14. NOAA- AVHRR G1K - dekad composite

16. Results: part 2 Key periods in the time series are prone to cloud cover –Few systems provide data at required temporal frequency Vegetation response to rainfall and temp is complex –statistical relationships not reliable –process-response models immature Spatial precision, registration and spectral calibration –only output regional scale assessments

17. Conclusions: problems or prospects? Local scale mapping of key ecosystem variables is challenging – limited by dimensionality of spectral data and variations in soil properties Regional scale mapping is feasible but monitoring is more challenging New systems (always) promise more but success will depend on integrating data –multi-sensor –constraining the landscape reflectance model (with GI)