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

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

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

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

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

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

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)

dynamics

Anomalies

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

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

Issues cloud cover

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

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)

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

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

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

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.

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

VI Issues

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)

Examples/Techniques NDVI variation Mozambique

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

Forest cover 1973

Forest cover 1985

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

Examples/Techniques NDVI time series

Examples/Techniques

Time of greenness onset

Duration of growing season

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

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):

LAI, cover dryness

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

PCA Rotation and scaling along orthogonal directions of maximum variance

PC1 PC2

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

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

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

Seasonality - ITCZ movement

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

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