Image Processing to Create Environmental and Ecological Data!
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1 Image Processing to Create Environmental and Ecological Data! Embedded Networked Sensing Eric Graham, Staff Ecologist cens.ucla.edu 1/34!
2 Image Processing to Create Ecological Data! Image Capture Ecological Data Targeted Cover/Photosynth! Multipurpose Phenology! User-Submitted Verification! 2/34!
3 Image Processing to Create Ecological Data! Image Capture Image Processing Ecological Data Targeted Simple approaches:! Color! Brightness! HDR! Cover/Photosynth! Multipurpose Phenology! User-Submitted Verification! 3/34!
4 Targeted Image Capture! James Reserve, San Bernardino Mountains, CA NWTower, Acoustic, AMARSS, Agave Hill Tower cameras NIMS Mosscam 4/34!
5 Targeted Image Capture! La Selva Biological Station, Costa Rica 5/34!
6 Targeted Image Capture! La Serena, Chile, watering experiments 6/34!
7 Cross-country Plant Phenology Multipurpose Image Capture for Plant Phenology! Thousands of geo-located, internetconnected cameras allow us to compare ground-based imaging with satellite data. 7/34!
8 User-Submitted Images for Citizen Science The What s Invasive! citizen science campaign for collecting weed locations has been a tremendous success, with 956 registered users who have contributed 3,014 observations of invasive weeds in 19 active parks! 8/34!
9 Image Processing with Python and R! Python, a free, open-source programming language Add-on tools to Python 2.6 include SciPy, a powerful set of array-based methods. R, a free, open-source environment for statistics If you are smarter than me, then RPy is Python interface that allows R commands to be embedded into Python code. (not included in programs here) 9/34!
10 Image Processing using Color Spaces! Lots of color models used for segmenting images: Normalized Red-Green-Blue (R / (R + G + B), etc) Hue-Saturation-Luminance Y-x-y (from CIE X Y Z) CIE L*a*b Red minus Green (R G) Red divided by Green (R / G) Excess Green (2 * Green Red Blue) Normalized Difference Index ((G R) / (G + R)) i1-i2-i3 (agricultural linear transform) ATD, based on human vision models And algebraic combinations of just the above results in about > 50 color space components to test. 10/34!
11 Example: Targeted Images using Color for Plant Cover! The color variation within an image taken under natural, uncontrolled lighting conditions is principally dominated by variations in luminance (lightness and darkness). Unfortunately, the RGB color space, although native to many image capture systems, does not separate color, from the luminance. 11/34!
12 Example: Targeted Images using Color for Plant Cover! RGB! ExG! Lab! HSL! ATD! Yxy! 12/34!
13 Example: Targeted Images using Color for Phenology! James Reserve Northwest Tower! From January 1, 2009! To January 1, 2010! When does Spring/Fall start?! 13/34!
14 Example: Targeted Images using Color for Phenology! Spring/leaf! Detection! Fall! Detection! 14/34!
15 Cross-country Plant Phenology Example: Targeted Images using Color for Phenology! Different color spaces detect different plant phases such that, for instance, Excess Green is good for spring detection and the Lab color space is better for detecting fall leaf colors.! 15/34!
16 Cross-country Plant Phenology Multipurpose Image Capture for Plant Phenology! Thousands of internetconnected cameras allow us to compare ground-based imaging with satellite data. cam135 We can manually separate deciduous, evergreen, understory areas of interest. 16/34!
17 Cross-country Plant Phenology Example: Multipurpose Images for Plant Phenology! Deciduous Evergreen Understory 17/34!
18 Example: User-Submitted Geo-Tagged Images! 18/34!
19 Example: User-Submitted Geo-Tagged Images! As part of a UCLA course in California Ecosystems, Professor Philip Rundel wanted to have his students map invasive plants along Topanga Canyon in the Santa Monica Mountains.! He decided to try out the system himself first, and in a couple hours walking down the canyon and concentrating on one invasive species (Euphorbia terracina), he mapped 373 locations.! 19/34!
20 How do we flag nonplant observations?! 20/34!
21 Continued Work and the CENS People Involved High Dynamic Range Imaging for quantification of reflected light from bracken fern: Joey Degges, John Hicks, Eric Yuen Plant Phenology Imaging for comparing sites with remote sensing: Erin Riordan, Eric Yuen Detect flower images from What s Invasive and from Flickr and deliver only flower images to mobile phones: Jameel Al-Aziz, Kyung Han, Jinha Kang, Charles Stigler 21/34!
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