Beyond Spectral and Spatial data: Exploring other domains of

information

GEOG3010 Remote Sensing

and Image Processing

Lewis

RSU

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

VI Issues

VI Issues

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

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

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

Forest cover change

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

• phenology

NDVI image sequence over Colorado 1990-1996

Examples/Techniques

NDVI time series

Examples/Techniques

Examples/Techniques

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):463-487.

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