1. Beyond Spectral and Spatial data: Exploring other domains of information: 3 GEOG3010 Remote Sensing and Image Processing Lewis RSU

2. Domains of Information spectral angular multi-temporal distance-resolved spatial

3. Multitemporal information Background –The reflectance / scattering properties of earth's surface change over time

4. Multitemporal information Background –May be due to factors such as: vegetation growth / senescence cycles de/reforestation / fires variations in soil moisture variation in (size of) water bodies built environment changes coastal erosion

5. Multitemporal information Background –Changes occur at a range of temporal scales over a range of spatial scales

6. Multitemporal information satellite EO appropriate to range of dynamic monitoring tasks –repeated coverage –consistent instrumentation –accurate –non-intrusive –variety of spatial and temporal scales

7. Multitemporal information satellite EO appropriate to range of dynamic monitoring tasks –monitoring vegetation dynamics over course of a year –link to (crop) growth models to provide yield estimates –distinguish cover types (classification)

8. dynamics

9. Anomalies

10. Issues temporal sampling –reconcile requirements of monitoring task with sensor characteristics and external influences repeat cycle of sensor spatial resolution of sensor lifespan of mission / historical data cloud cover effects on optical / thermal data

11. Issues discriminating surface changes from external influences on RS data –Viewing and illumination conditions can change over time Viewing: –wide field of view sensors –pointable sensors Illumination: –variations in Sun position variations in atmospheric conditions

12. Issues cloud cover

13. Issues sensor calibration –degradation over time –variations between instruments Coregistration of data –effects of misregistration (practical)

14. Issues Quantity of data –can be large (TB) –preprocessing requirements can be very large –move towards formation of databases of RS- derived 'products' (EOS, CEO)

15. Dealing with issues Vegetation Indices (VIs) –measured reflectance / radiance sensitive to variations in vegetation amount –BUT also sensitive to external factors –want contiguous data (clouds) –Typically take VI compositing approach

17. Use of VIs direct: –attempt to find (empirical) relationship to biophysical parameter (e.g. LAI) indirect: –look at timing of vegetation events (phenology)

18. VI Issues VI can still be sensitive to external factors (Esp. BRDF effects) no one ideal VI - NDVI used historically empirical relationships will vary spatially and temporally

19. VI Issues IDEAL: –Attempt to make VI sensitive to vegetation amount but not to external factors: atmospheric variations topographic effects BRDF effects (view and illumination) soil background effects –SAVI, ARVI etc.

20. VI Issues PRACTICE: –VIs maintain some sensitivity to external factors –Be wary of variations in satellite calibration etc. for time series

21. VI Issues

23. Examples/Techniques land cover change detection Vegetation Indices eg: –change in VI - infer change in vegetation state –NDVI variation in Mozambique (UN World Food Programme)

24. Examples/Techniques NDVI variation Mozambique

25. Classification Change in area covered by various classes –eg. forest cover to investigate variations in global / regional Carbon budgets

26. Forest cover 1973

27. Forest cover 1985

28. Examples/Techniques land cover change detection –Methods: characterise trajectories to models (phenology) analysis of time trajectories of NDVI / thermal data Principal Components Analysis

29. Examples/Techniques NDVI time series

30. Examples/Techniques

33. Time of greenness onset

34. Duration of growing season

35. Examples/Techniques land cover change detection –Methods: characterise trajectories to models (phenology) analysis of time trajectories of NDVI / thermal data Principal Components Analysis

36. Examples/Techniques Lambin, E. F. and D. Ehrlich (1996), The surface temperature -- vegetation index space for land cover and land-cover change analysis, International Journal of Remote Sensing 17(3):463-487.

37. LAI, cover dryness

38. Examples/Techniques land cover change detection –Methods: characterise trajectories to models (phenology) analysis of time trajectories of NDVI / thermal data Principal Components Analysis

39. PCA Rotation and scaling along orthogonal directions of maximum variance

40. PC1 PC2

41. Consider multitemporal NDVI: Expect high degree of correlation but also deviations from this use PCT...

42. Monthly NDVI - Africa 96.68% of variance in PC1 Loadings very similar for all months …average

44. Monthly NDVI - Africa 2% of variance in PC2 Dec-March minus April-Nov

45. Seasonality - ITCZ movement

52. PCA Information on –state (PC1) –dynamics (seasonality, longer term trends)

53. Summary Basis: dynamics/change Methods: classification … change phenology-based description / classifications NDVI / thermal data - temporal trajectories Principal Components Analysis