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Beyond Spectral and Spatial data: Exploring other domains of information: 3

This article discusses the importance of multitemporal information in remote sensing, including spectral, angular, multi-temporal, distance-resolved, and spatial domains. It also addresses the challenges and techniques for analyzing and utilizing multitemporal data.

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Beyond Spectral and Spatial data: Exploring other domains of information: 3

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  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

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

  17. 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

  18. 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.

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

  20. VI Issues

  21. VI Issues

  22. 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)

  23. Examples/Techniques NDVI variation Mozambique

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

  25. Forest cover 1973

  26. Forest cover 1985

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

  28. Examples/Techniques NDVI time series

  29. Examples/Techniques

  30. Examples/Techniques

  31. Examples/Techniques

  32. Time of greenness onset

  33. Duration of growing season

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

  35. 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.

  36. dryness LAI, cover

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

  38. PCA • Rotation and scaling along orthogonal directions of maximum variance

  39. PC2 PC1

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

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

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

  43. Seasonality - ITCZ movement

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