PODS: Interpreting Spatial and Temporal Environmental Information

PODS:Interpreting Spatial and Temporal

Environmental Information

Edoardo (Edo) BiagioniUniversity 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”

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

http://red2.ics.hawaii.edu/cgi-bin/location

Blue: rain

Big Blue: recent rain

Cyan: cool, dry

Red: warm, dry

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

Not (yet) automated on the web…

Blue: rain

Red: temperature

Yellow: sunlight

Plant population

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]