Making sense of methane fluxes with MATLAB

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

Field Site & Method Sherman Island, Sacramento- SJ Bay Delta Collaboration with Baldocchi lab Eddy covariance flux measurements CH4 CO2 H2O

Diurnal methane fluxes Source: Jaclyn Hatala & Dennis Baldocchi

Cow Cam! Source: Dettoet al. (2010)

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?

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

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

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

Results Unexpected result 18th – 25th January 2010

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?

Making sense of methane fluxes with MATLAB

Making sense of methane fluxes with MATLAB

Presentation Transcript

Making Sense of Middleware

Making Sense of Technology

SENSE-MAKING .

of making sense...

Making Sense of Sustainability

Making Sense of Writing

Making Sense of Smells

Making Sense of Nonfiction

Making Sense of Mathematics

Making Sense of moocs

Making Sense of Things

Making Sense of SLOs

Making Sense of Graduation

Making Sense of Culture

Modeling of microscale variations in methane fluxes

Making Sense of Writing

Making sense of voices

Making Sense of Writing

Making Sense of History

Making sense of SaaS

Making Sense of Maps

Making Sense of Models