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Beyond Spectral and Spatial data: Exploring other domains of information GEOG3010 Remote Sensing and Image Processing Lewis RSU
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Multitemporal information Background –The reflectance / scattering properties of earth's surface change over time
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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
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Multitemporal information Background –Changes occur at a range of temporal scales over a range of spatial scales
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Multitemporal information satellite EO appropriate to range of dynamic monitoring tasks –repeated coverage –consistent instrumentation –accurate –non-intrusive –variety of spatial and temporal scales
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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)
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dynamics
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Anomalies
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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
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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
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Issues cloud cover
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Issues sensor calibration –degradation over time –variations between instruments Coregistration of data –effects of misregistration (practical)
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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)
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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
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Use of VIs direct: –attempt to find (empirical) relationship to biophysical parameter (e.g. LAI) indirect: –look at timing of vegetation events (phenology)
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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
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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.
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VI Issues PRACTICE: –VIs maintain some sensitivity to external factors –Be wary of variations in satellite calibration etc. for time series
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VI Issues
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Examples/Techniques multitemporal SAR data for crop classification –varying growth / senescence between crops used to distinguish crop type –can attempt to use standard classification algorithms
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Examples/Techniques multitemporal SAR data for crop classification –noise issues with SAR (practical) –image segmentation (detect fields) and classify on field-by-field basis –smooth ('despeckle') data prior to use of pixel-by- pixel classification
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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)
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Examples/Techniques NDVI variation Mozambique
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Classification Change in area covered by various classes –eg. forest cover to investigate variations in global / regional Carbon budgets
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Forest cover 1973
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Forest cover 1985
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Forest cover change
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Examples/Techniques land cover change detection –Methods: characterise trajectories to models (phenology) analysis of time trajectories of NDVI / thermal data Principal Components Analysis
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Examples/Techniques phenology NDVI image sequence over Colorado 1990-1996
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Examples/Techniques NDVI time series
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Examples/Techniques
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Time of greenness onset
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Duration of growing season
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Examples/Techniques land cover change detection –Methods: characterise trajectories to models (phenology) analysis of time trajectories of NDVI / thermal data Principal Components Analysis
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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.
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LAI, cover dryness
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Examples/Techniques land cover change detection –Methods: characterise trajectories to models (phenology) analysis of time trajectories of NDVI / thermal data Principal Components Analysis
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PCA Rotation and scaling along orthogonal directions of maximum variance
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PC1 PC2
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Consider multitemporal NDVI: Expect high degree of correlation but also deviations from this use PCT...
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Monthly NDVI - Africa 96.68% of variance in PC1 Loadings very similar for all months …average
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Monthly NDVI - Africa 2% of variance in PC2 Dec-March minus April-Nov
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Seasonality - ITCZ movement
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PCA Information on –state (PC1) –dynamics (seasonality, longer term trends)
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Summary Basis: dynamics/change Methods: classification … change phenology-based description / classifications NDVI / thermal data - temporal trajectories Principal Components Analysis
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