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Data Analysis

Data Analysis. What is unique about phenology?. Data is sparse Definition of many phenological events is fuzzy More dependence on visual interpretation Need long term data for accurate analysis Models may need to more scale sensitive Include more parameters Data driven modeling.

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Data Analysis

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  1. Data Analysis

  2. What is unique about phenology? • Data is sparse • Definition of many phenological events is fuzzy • More dependence on visual interpretation • Need long term data for accurate analysis • Models may need to more scale sensitive • Include more parameters • Data driven modeling

  3. CS Research Issues • Spatio-temporal data mining • Sensor networks • Image analysis • Trigger event (send alerts) • Cheap sensors • Visual, temp, precip, soil moisture (build) • Locate near already existing automated networks • Upto 2 miles line of sight • Cost a few thousand dollars • Visualization

  4. Dataset Requirements • What data do we need? • Can we get European datasets? • Quality of Data, Quality of Information, Quality of Knowledge • Pedigree, provenance • Track the sources • Tracability (where did the data from, e.g. meat labeling, Gerber baby food)

  5. What might be possible with 20 years (or less) of phenological data? • Facilitate understanding of plant phenological cycles and their relationship to climate • Exploratory data analysis • Data Mining Tools • Spatial, temporal, spatio-temporal, integrated (plant, insect) • Extending these to spatio-temporal will be innovative • Event detection in temporal datasets • Case Based Reasoning • Simulation tools • What tools are out there? • Do they have computational bottlenecks • Visualization tools

  6. What might be possible with 20 years (or less) of phenological data? • Comprehensive evaluation of satellite-derived measurements • Detecting hidden signals • May be use data mining techniques • Large Data Volume management and manipulation • High performance storage and computing • Change Detection • Inter-sensor calibration issues

  7. What might be possible with 20 years (or less) of phenological data? • Detection of long-term phenological trends in response to climate variability/global warming • Much of the work uses linear regression models • Assumes stationarity over time • Change point detection (e.g El Nino became more frequent in1980s) • Need to break up the time into smaller slices

  8. What might be possible with 20 years (or less) of phenological data? • Evaluate impacts of longer growing seasons on pollinators, cattle, crop and forest pests, wildfires, carbon storage, and water use • Regression, spatial autocorrelation • Has space and time components • Early spring is arriving earlier faster (second order analysis)

  9. Two sample problems • Alfalfa and lady beetle • When do we harvest alfalfa • Need to model and match phenology of both • Critical climate for crops • Phenology events as critical triggers in crop yield

  10. Notes • Need closer interaction between CS and Phenology • Need to know more about the models • How quickly do we need answers? • Seconds, days, months • How do we leverage the NADSS effort

  11. Signature Project

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