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 light wind (bend) Computer & Radio Batteries Internal: voltage

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?

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 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 esb@hawaii.edu