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Making sense of methane fluxes with MATLAB Gavin McNicol EPS 209 Data source – Jaclyn Hatala & Dennis Baldocchi.

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Presentation on theme: "Making sense of methane fluxes with MATLAB Gavin McNicol EPS 209 Data source – Jaclyn Hatala & Dennis Baldocchi."— Presentation transcript:

1 Making sense of methane fluxes with MATLAB Gavin McNicol EPS 209 Data source – Jaclyn Hatala & Dennis Baldocchi

2 Field Site & Method Sherman Island, Sacramento- SJ Bay Delta Collaboration with Baldocchi lab Eddy covariance flux measurements CH 4 CO 2 H 2 O

3 Diurnal methane fluxes Source: Jaclyn Hatala & Dennis Baldocchi

4 Cow Cam! Source: Detto et al. (2010)

5 MATLAB Image Processing Basic Goals: Find cows in image (color, texture, intensity) Produce vector of ‘cow’ and ‘no cow’ time points Can we do more than just presence/absence?

6 Results so far Individual images: can we see the cows? Range Filter

7 Results Individual images: can we see the cows? Mahalanobis distance

8 Results Range filter method on test images: 59% accuracy (type I error) Threshold

9 Results Unexpected result 18 th – 25 th January 2010

10 Next steps 1 – Combine mahalanobis distance information with edge detector 2 – Better to over-predict. 3 – Group photos by day as cows are never there all day and look at deviation from mean intensity. Questions?


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