Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd...
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![Page 1: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/1.jpg)
Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective
Nigel TroddCoventry University
![Page 2: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/2.jpg)
Vegetation dynamics of Kalahari ecosystems
savannas, shrublands & grasslands occupy 53 million km2 ... including 60% of Africa
state and transition mosaics support sustainable agriculture
![Page 3: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/3.jpg)
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
![Page 4: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/4.jpg)
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
![Page 5: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/5.jpg)
Study areasSouthern Kalahari
• Tshane
• Kalaghadi Transfrontier Park
• Tsabong
• Severn
Eastern Kalahari
• Makoba
<40% vegetation cover
![Page 6: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/6.jpg)
Field & lab measurements
Field & Lab spectroscopy
Landscape components
![Page 7: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/7.jpg)
Experiment 1A: differences in the reflectances of landscape components
Note: data normalised for % cover
• grass > bush
visible ~ 3% - 5%
near-infrared 0%
• soil > vegetation
![Page 8: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/8.jpg)
Landsat TM4
![Page 9: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/9.jpg)
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
![Page 10: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/10.jpg)
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
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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
![Page 12: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/12.jpg)
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
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NOAA-AVHRR
G1K - dekad composite
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![Page 15: Observing Kalahari ecosystems at local to regional scales: a remote sensing perspective Nigel Trodd Coventry University.](https://reader035.fdocuments.in/reader035/viewer/2022062802/56649e955503460f94b99d08/html5/thumbnails/15.jpg)
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
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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)