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PODS: Interpreting Spatial and Temporal Environmental Information

PODS: Interpreting Spatial and Temporal Environmental Information. Edoardo (Edo) Biagioni University of Hawai’i at M ā noa. The Challenge. Endangered plants grow in few locations Hawai'i has steep weather gradients: the weather is different in nearby locations

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PODS: Interpreting Spatial and Temporal Environmental Information

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  1. PODS:Interpreting Spatial and TemporalEnvironmental 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. light wind (bend) temperature Computer& Radio humidity Batteries Inside a “Rock” 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 • http://weather.yahoo.com/graphics/satellite/east_usa.html

  14. Simple Map Blue: rain Big Blue: recent rain Cyan: cool, dry Red: warm, dry http://red2.ics.hawaii.edu/cgi-bin/location

  15. 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 Graphs vs. Maps

  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 • http://www.botany.hawaii.edu/pods/pictures, data • http://www.ics.hawaii.edu/~esb/pods/these slides, the paper • esb@hawaii.edu

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