TEOS Group Presentation The Interns: Kathlyn Bland Devin Sevilla
Martin Gawecki The Mentor: Eric Graham Slide 2 A Closer Look at
Bracken Ferns: Stomatal Response Presented by Kathlyn Bland Slide 3
Interesting to study on an ecological and economic basis
Differences in the physiological aspects of fronds will be due to
acclimation to differences in above-ground conditions The plant as
a whole has competing goals of maximizing its carbon gain and
minimizing its water loss. Exposed fronds (sun-fronds) may be
acclimated to conserve water as a priority over carbon gain and so
will have rapid stomatal responses. Fronds occurring more in the
understory (shade-fronds) may be acclimated for carbon gain at the
expense of more water lost and so may have slower stomatal
responses as they wait for sunflecks (that will contribute
substantially to their carbon budget). Slide 4 Time series for
stomatal conductance low light high light low light high light
Slide 5 Time series for stomatal conductance low light high light
low light high light Slide 6 Time series for stomatal conductance
Induction Stomatal reopening under high light Stomatal closure with
low light Slide 7 Time series for stomatal conductance Things to
compare between fronds in different light conditions: Rates at
which stomata open and close: 1.Time for full induction after high
light starts. 2.Time to reach minimum conductance after beginning
of shade period. 3.Time to return to maximum conductance after high
light is resumed. Absolute values of photosynthesis and
conductance: 1.Maximum photosynthetic rate in high light. 2.Maximum
stomatal conductance in high light. 3.Total drop in conductance due
to shade period. Slide 8 Automated Infrared Gas Sampling for
Terrestrial Carbon Dioxide and Water Vapor Concentration Gradients
(that thing we talked about last time) Presented By: Devin Sevilla
and Martin Gawecki Slide 9 Results of Previous Experiments Two
experiments run in the last 2 weeks White Mountains Deployment
James Reserve Data Issues with IRGA and Solenoids Searching for New
Materials Efficient solenoid valves operating at 120V AC Efficient
pump Slide 10 James Reserve CO 2 Data Slide 11 James Reserve H 2 O
Data Slide 12 The Other TEOS Project Spectral Analysis of Charge
Coupled Devices in Context of Digital Cameras as Biological Sensors
(that other TEOS project) Slide 13 Image Processing Procedure
Spectral Power Distribution Analysis Color Space Transformations
MATLAB Image Toolbox Biological Data Analysis Slide 14 Spectral
Power Analysis: Goals Using illuminants and a spectroradiometer to
characterize response of cameras CCD Wavelength selector
Integrating Sphere Spectroradiometer Comparison of CCD responses
under various lighting conditions with the Color Checker Chart
Slide 15 Spectral Power Analysis: Target Data Data for the Sony
camera is available and we would like to replicate this for the
Cannon camera This will allow us to translate the perceived color
and the real color from the cameras CCD Slide 16 Spectral Power
Analysis: Procedure 1.Measure spectral power distribution of
standard illuminants 2.Measure CCD response to selected standard
illuminants 3.Map relation of spectral power to selected color
space values of camera 4.Get spectral spectral power distribution
(SPD) of flowers 5.Map the SPD of the flower to expected color
space values of the camera Slide 17 Spectral Power Analysis: Setup
Slide 18 Spectral Power Analysis: Preliminary Results Slide 19
Color Space Transformations Different color representations (RGB,
CMYK, CIEXYZ, HSV, CIEL*a*b*, RGB) Human Color Gamut and the
Standard Observer What is an optimal color representation? Slide 20
MATLAB Image Toolbox Supports a wide range of functions: Spatial
image transformations, Morphological operations, Neighborhood and
block operations, Linear filtering and filter design, Transforms,
Image analysis and enhancement, Image registration, Deblurring,
Region of interest operations Porting to other platforms is fairly
easy 110 grains of rice!!! Slide 21 Biological Data Analysis:
Applications Be able to analyze vast sets of data with minimal
human interaction Characterization of plant wellness through image
analysis Smart Agriculture Environmental monitoring Extended
analysis of what humans cannot see Slide 22 Future Work
Characterize camera CCD Test spectral power distributions with
different illuminants and illumination conditions Design algorithm
to analyze particular data set and determine flower counts Field
Test at the James Reserve Slide 23 Questions?
