Transcript PODS Interpreting Spatial and Temporal Environmental Information

PODS:
Interpreting Spatial and Temporal
Environmental Information
Edoardo (Edo) Biagioni
University of Hawai’i at Mānoa
The Challenge
• Endangered plants grow in few locations
• Hawai'i has steep weather gradients: the
weather is different in nearby locations
• A single weather station doesn’t help, so
• Have many sensors (PODS)
• Make them unobtrusive: rock or log
• Resulting in lots of data
Sample Terrain
What’s a POD, anyway?
Inside a “Rock”
light
Internal: voltage
wind (bend)
temperature
humidity
Computer
& Radio
Batteries
Data Collection
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Wind, Rain, Temperature, Light, Moisture
At each pod
Every 5 minutes to 1 hour, for years
Images at some of the pods
Networking challenge: getting the data
back without discharging the batteries
• How to make sense of all this data?
Spatial Patterns
• Wet and dry areas have different plants
• Cold and warm areas have different plants
• Where is the boundary? The boundary
will be different for different plant species
• Does cloud cover matter?
• Does wind matter? Pollinators, herbivores
Temporal Patterns
• Is this a warm summer? Winter?
• Is it a warm summer everywhere, or just in
some places?
• Does it rain more when it is warmer?
• What events cause flowering?
• How long does it take the plant to recover
after an herbivore passes?
Endangered: Silene Hawaiiensis
Who needs the Information?
• Scientists (botanists)
• High-School Students
• Virtual Tourists
What use is the Information?
• Study the plants, prevent decline
• Determine what is essential for the plant’s
survival: e.g., how will global warming
affect it?
• Locate alternative areas
• Watch what happens, instead of trying to
reconstruct what happened
• Capture rare phenomena
How is the data communicated?
• Graphs, maps, tables
• Tables unwieldy for large numbers of
PODS
• Graphs need many different scales
• Maps can help intuitive understanding
• Ultimately, need to find useful patterns
Picture of weather data, from web
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http://weather.yahoo.com/graphics/satellite/east_usa.html
Simple Map
Blue: rain
Big Blue: recent rain
Cyan: cool, dry
Red: warm, dry
http://red2.ics.hawaii.edu/cgi-bin/location
Graphs vs. Maps
• Graphs
• Good for recognition
of temporal patterns
• Can summarize a lot
of data very concisely
• Mostly for
homogeneous data
• Maps
• Good for recognition
of spatial patterns
• Can summarize a lot
of data very concisely
• Good for
heterogeneous data
Strategies
• Data Mining: search data for patterns, try
to match to plant distribution
• Machine Learning: try to predict new data.
If prediction is wrong, something
unpredicted (unpredictable!) is happening
• Better maps, incorporating lots of data
including images, but in a way that
supports intuitive analysis
Better Map
Blue: rain
Red: temperature
Yellow: sunlight
Plant population
Not (yet) automated on the web…
Where to go from here
• Plant “surveillance”: being there, remotely
• Data Collection is only the essential first
step
• Data Analysis must be supported by
appropriate tools
• Find out what really matters in the life of
an endangered plant
Acknowledgements and Links
• Co-Principal Investigators: Kim Bridges,
Brian Chee
• Students: Shu Chen, Michael Lurvey, Dan
Morton, Bryan Norman, and many more
• http://www.botany.hawaii.edu/pods/
pictures, data
• http://www.ics.hawaii.edu/~esb/pods/
these slides, the paper
• [email protected]