Remote Sensing of Woody Vegetation in the West African Sahel
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Transcript of Remote Sensing of Woody Vegetation in the West African Sahel
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Remote Sensing of Woody Vegetation in the West African
Sahel Presented By: Andrew Wickhorst
Space Grant Mentor: Dr. Stefanie HerrmannSpace Grant Symposium
University of ArizonaApril 21, 2012
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Why is it Important to Map Woody Cover?
• Important for understanding the carbon balance
• Trees are of great importance to the Sahelian agricultural parklands
• Monitoring changes in vegetation composition over time.
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What is Spatial Resolution?
Low High
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Study Area: Senegal
http://cache.virtualtourist.com/4/3299027-_Senegal.jpg
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Objectives
1.Create an accurate map of tree densities within the study area using very high spatial resolution (2m) imagery.
2.Determine the optimal model for estimating tree cover densities in a Sahelian landscape using coarser spatial resolution (30m) imagery.
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Very High Spatial Resolution (OrbView, 2m)
0 4 8 12 162Kilometers
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Very High Spatial Resolution (OrbView, 2m)
0 0.1 0.2 0.3 0.40.05Kilometers
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Coarser Resolution (Landsat, 30m)
0 0.1 0.2 0.3 0.40.05Kilometers
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Spatial Resolution & Tree Cover Mappingo High spatial resolution imagery
• Individual trees can be detected • Not available for all locations• Often prohibitively expensive• Limited historical data
o Coarser spatial resolution imagery • Individual trees cannot be detected• Global coverage• Free• Many years of historical data
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Methods1.Obtain and pre-process imagery
2.Create reference map of tree densities from high spatial resolution imagery.
3.Produce and test predictive models of tree densities based on coarse spatial resolution imagery
4.Select “best” model and assess performance
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Tree vs. No Tree
No Tree
Tree
95 % Accurate
0 0.1 0.2 0.3 0.40.05Kilometers
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Reference Map
100 %
0 %50 %
Tree Cover Density0 4 8 12 162
Kilometers
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Predictive Model of Tree Cover
100 %
0 %50 %
Tree Cover Density0 4 8 12 162
Kilometers
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Model-Reference Comparison
Over Predicted
Correctly Predicted
Under Predicted
0 4 8 12 162Kilometers
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Correlation of Actual & Predicted Tree Cover
R² = 0.79SE = 3.6 %
0 10 20 30 40 50 60 70 80 90 1000
102030405060708090
100
Actual % Treecover
Pred
icte
d %
Tre
ecov
er
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Conclusion
• Coarser resolution imagery is a viable alternative to high resolution imagery for the estimation of tree cover
• The resulting model replicates the spatial pattern of tree cover well
• A standard error of 3.6% tree cover indicates a relatively accurate estimation
• However, the interpretation of the standard error also depends on the overall percent tree cover
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Acknowledgements • University of Arizona Space Grant Consortium • Arizona Remote Sensing Center • Office of Arid Land Studies • United States Geological Survey (USGS)• Stefanie Herrmann, PhD• Stuart Marsh, PhD• Willem van Leeuwen, PhD • Barron Orr, PhD• Kyle Hartfield, MS• Abd salam El Vilaly, MS• Katy Landau• Denise Garcia
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Thank You!