1. PODS: Interpreting Spatial and Temporal Environmental Information
Edoardo (Edo) Biagioni
University of Hawai’i at Mānoa

2. 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

3. Sample Terrain

4. What’s a POD, anyway?

5. Inside a “Rock” light Internal: voltage wind (bend) temperature
humidity
light
wind (bend)
Computer & Radio
Batteries
Internal: voltage

6. Data Collection 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?

7. 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

8. 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?

9. Endangered: Silene Hawaiiensis

10. Who needs the Information?
Scientists (botanists)
High-School Students
Virtual Tourists

11. 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

12. 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

13. Picture of weather data, from web

14. Simple Map Blue: rain Big Blue: recent rain Cyan: cool, dry
Red: warm, dry

15. 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

16. 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

17. Better Map Blue: rain Red: temperature Yellow: sunlight
Plant population
Not (yet) automated on the web…

18. 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

19. Acknowledgements and Links
Co-Principal Investigators: Kim Bridges, Brian Chee
Students: Shu Chen, Michael Lurvey, Dan Morton, Bryan Norman, and many more
pictures, data
these slides, the paper