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Fire Sync Data Analysis

Fire Sync Data Analysis. Christel’s Baby Steps to Temporal and Spatial Analyses. Overview. Conceptual Map Study Design Data Charactistics Data Analysis Roadmap to Success Future Work. Conceptual Map. Forest Type, Landscape position, other. CLIMATE ENSO, PDO, AMO. FIRE EVENTS

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Fire Sync Data Analysis

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  1. Fire Sync Data Analysis Christel’s Baby Steps to Temporal and Spatial Analyses

  2. Overview • Conceptual Map • Study Design • Data Charactistics • Data Analysis • Roadmap to Success • Future Work

  3. Conceptual Map Forest Type, Landscape position, other CLIMATE ENSO, PDO, AMO FIRE EVENTS Spatial/Temporal Dynamics -Year events -X, Y coord Summer DROUGHT PDSI weather Winter ppt Fuel Oxygen, Ignition Summer soil moisture

  4. Study Design • Observational • Post Ex Facto • Non-random • Spatial • Temporal

  5. Fire Site Categorical X,Y information Fire Event Time Series X,Y information Binary Data Clumping by climatic region could be count data Phase Events ENSO Event Time Series Binary Data? Other PDO, AMO Events as well? Phases Categories? PDSI Continuous index Grid Data Time series X,Y information Data Characteristics

  6. Climate - normal Fire data ??Non-linear Correlated observations Inference? Ecological/Climatological Statistical Prediction? (Interpolation) Data Characteristics

  7. Conceptual Map Forest Type, Landscape position, other CLIMATE ENSO, PDO, AMO FIRE EVENTS Spatial/Temporal Dynamics -Year events -X, Y coord Summer DROUGHT PDSI weather Winter ppt Fuel Oxygen, Ignition Summer soil moisture

  8. The Big Science Question • Yr Climate • A, B, C • A-, B, C • A-, B, C- • … Asynchronous spatial fire pattern over time?? El Nino Influence

  9. Conceptual Map Forest Type, Landscape position, other CLIMATE ENSO, PDO, AMO FIRE EVENTS Spatial/Temporal Dynamics -Year events -X, Y coord Summer DROUGHT PDSI weather Winter ppt Fuel Oxygen, Ignition Summer soil moisture

  10. Research Questions • Yr Climate • A, B, C • A-, B, C • A-, B, C- • … Does drought reflect climatic conditions – spatially and temporally? El Nino Influence

  11. Superposed Epoch Analysis Nonparametric methods for correlated time series data Focuses to find signals around extreme events Research Approach

  12. Superposed Epoch Analysis 77 sites related to drought in the year of the fire Temporal results Descriptive Mapping Research Approach

  13. Spatial Relationships? Regionalize Analysis to deflat spatial influence Test for autocorrelation in distance (x,y) Research Approach

  14. Research Approach • Regionalize • Climatic – PDSI data • PCA ordination

  15. Research Approach • Response Groups • Fire Event data • Clustering • dendrograms • Nonmetric multidimensional Scaling • ordination

  16. Sample Unit = Site

  17. Sample Unit = Site

  18. Sample Unit = Year

  19. Sample Unit = Year

  20. Analyzing Spatial & Temporal at the same time?? “Synchrony” Definition: A process of adjustment of rhythms due to an interaction Spatial covariance in population density fluctuations Research Approach

  21. Synchrony Analysis • Spatial covariance – Point Pattern Analysis • Demonstrate scale • Identify mechanisms • Endogenous • Exogenous • Moran’s I Effect: density independent factor (e.g., climate) overrides local population regulators by large environmental shocks that synchonize the population

  22. Synchrony Analysis • Spatial Autocorrelation • Pattern of nearby locations are more likely to have similar magnitude than by chance alone • Signature of past spatial-temporal patterns

  23. Synchrony Analysis • Spatial Autocorrelation Coefficient • Provide an average isotrophic estimation of autocorrelation at each distance class • Formal testing with Confidence Intervals • Bonferroni Adjustment • Distances result in + or – relationships • Displayed with correlograms

  24. Synchrony Analysis • Variogram • Identify and model spatial pattern • Predict (kriging) unmeasured areas value • Require parameter fitting & model selection

  25. Synchrony Analysis • Variogram

  26. Roadmap to success • Hypothesis refinement • Data statements & tests

  27. Roadmap to success • Exploratory data analysis • All datasets • Data format (binary, count, continuous…) • Transformations? • Outliers? • Possible interaction terms (elevation, forest type)?

  28. Roadmap to success • Summary Analyses • Multivariate/NMS (time or space) • Clustering (time or space) • Repeated measures • SEA • Variograms (scale)

  29. Roadmap to success • Statistical Inference & Prediction • Model based methods

  30. Future Work • Spatio-temporal dynamics • Fire, Drought, Climate oscillations • Kurt – drought info • Christel – fire info • Grant - ENSO • Comparison of dynamics • Drought vs fire, etc. • Prediction to unmeasured areas • Hierarchal modelling

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