Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective

Nigel TroddCoventry University

Vegetation dynamics of Kalahari ecosystems

savannas, shrublands & grasslands occupy 53 million km2 ... including 60% of Africa

state and transition mosaics support sustainable agriculture

Reducing / removing uncertainties

• Vegetation community distribution• Timing and nature of change • Vegetation dynamics at regional scales

Aims: • to develop remote sensing method(s) to characterise vegetation community dynamics• to understand the limits on those methods at local to regional scales

Method1 Analyse reflectance properties of individual

landscape components – H0: there is no difference in the reflectances

2 Simulate the composite reflectance of vegetation communities– Develop a landscape reflectance model– H0: vegetation community structure is not related to

reflectance

3 Apply model to analyse regional vegetation dynamics

Study areasSouthern Kalahari

• Tshane

• Kalaghadi Transfrontier Park

• Tsabong

• Severn

Eastern Kalahari

• Makoba

<40% vegetation cover

Field & lab measurements

Field & Lab spectroscopy

Landscape components

Experiment 1A: differences in the reflectances of landscape components

Note: data normalised for % cover

• grass > bush

visible ~ 3% - 5%

near-infrared 0%

• soil > vegetation

Landsat TM4

Experiment 1b: variation in soil reflectance

30

35

40

45

50

55

- 70 - 60 - 50 - 40 - 30 - 20 - 10 0 10 20 30 40 50 60 70

View Zenith Angle (degrees)

Reflecta

nce (

%)

t1

m2

tg1

tg3

35

40

45

50

- 70 - 60 - 50 - 40 - 30 - 20 - 10 0 10 20 30 40 50 60 70

View Zenith Angle (degrees)

Reflecta

nce (

%)

t1

m2

tg1

tg3

38

44

50

56

62

- 70 - 60 - 50 - 40 - 30 - 20 - 10 0 10 20 30 40 50 60 70

View Zenith Angle (degrees)

Reflecta

nce (

%)

t1

m2

tg1

tg3

42

46

50

54

58

- 70 - 60 - 50 - 40 - 30 - 20 - 10 0 10 20 30 40 50 60 70

View Zenith Angle (degrees)Reflecta

nce (

%)

t1

m2

tg1

tg3

680 nm 850nm

principal plane

orthogonal plane

• Differences of ~2%

• Soil crust ~6% increase

Experiment 2: simulating reflectance using a canopy reflectance model

Simulated reflectance = landscape component x reflectance

0 10% 20% 30% 40%

0 27.3 23.2 19.6 16.2 13.2

30% 22.7 19.0 15.7 12.7 10.0

60% 18.1 14.8 11.8 9.2 *

Grass cover

Bush cover

Results: part 1

• Significant monospectral differences between soil and vegetation

• Vegetation community structure not related to reflectance

• Dimensionality of single-date imagery limits local scale applications

Moving forward...

3 Analyse time series of Earth observation data– H0: there is no difference in the temporal profiles of

vegetation communities

4 Apply model to analyse regional vegetation dynamics

NOAA-AVHRR

G1K - dekad composite

Results: part 2• Key periods in the time series are

prone to cloud cover – Few systems provide data at required temporal

frequency

• Vegetation response to rainfall and temp is complex– statistical relationships not reliable– process-response models immature

• Spatial precision, registration and spectral calibration– only output regional scale assessments

Conclusions: problems or prospects?• Local scale mapping of key ecosystem

variables is challenging– limited by dimensionality of spectral data and

variations in soil properties

• Regional scale mapping is feasible but monitoring is more challenging

• New systems (always) promise more• but success will depend on integrating data

– multi-sensor– constraining the landscape reflectance

model (with GI)