fire sync data analysis
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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
CLIMATEENSO, PDO,
AMO
SummerDROUGHT
PDSIWinter ppt
Summer soil moisture
FIRE EVENTSSpatial/Temporal
Dynamics-Year events-X, Y coord
FuelOxygen, Ignition
weather
Forest Type, Landscape
position, other
Study Design Observational Post Ex Facto Non-random
Spatial Temporal
Data Characteristics 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 Climate - normal Fire data ??Non-linear Correlated observations
Inference? Ecological/Climatological Statistical
Prediction? (Interpolation)
Conceptual Map
CLIMATEENSO, PDO,
AMO
SummerDROUGHT
PDSIWinter ppt
Summer soil moisture
FIRE EVENTSSpatial/Temporal
Dynamics-Year events-X, Y coord
FuelOxygen, Ignition
weather
Forest Type, Landscape
position, other
The Big Science Question
El Nino Influence
Yr Climate
1 A, B, C
2 A-, B, C
3 A-, B, C-
4 …
Asynchronous spatial fire pattern over time??
Conceptual Map
CLIMATEENSO, PDO,
AMO
SummerDROUGHT
PDSIWinter ppt
Summer soil moisture
FIRE EVENTSSpatial/Temporal
Dynamics-Year events-X, Y coord
FuelOxygen, Ignition
weather
Forest Type, Landscape
position, other
Research Questions
El Nino Influence
Yr Climate
1 A, B, C
2 A-, B, C
3 A-, B, C-
4 …
Does drought reflect climatic conditions – spatially and temporally?
Research Approach Superposed Epoch Analysis
Nonparametric methods for correlated time series data
Focuses to find signals around extreme events
Research Approach Superposed Epoch
Analysis 77 sites related to
drought in the year of the fire
Temporal results Descriptive
Mapping
Research Approach Spatial Relationships?
Regionalize Analysis to deflat spatial influence
Test for autocorrelation in distance (x,y)
Research Approach Regionalize
Climatic – PDSI data PCA ordination
Research Approach Response Groups
Fire Event data Clustering
dendrograms Nonmetric
multidimensional Scaling ordination
DCA, species in 4 or less exp un deletedRed Pine Herbs 2002
X Data
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
Y D
ata
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
CC1
CR1
DC1
DR1 LC1LR1
SC1
SR1
CC2
CR2DC2
DR2
LC2
LR2
SC2
SR2
CC3
CR3
DC3
DR3
LC3
LR3
SC3
SR3
CC5
CR5
DC5DR5
LC5
LR5SC5
SR5
Sample Unit = Site
Sample Unit = Site
Sample Unit = Year
Sample Unit = Year
sync
-tri
al
Dis
tanc
e (
Obj
ect
ive
Fun
ctio
n)
Info
rma
tion
Re
ma
inin
g (%
)
0 100
4.1E
-02
75
8.2E
-02
50
1.2E
-01
25
1.6E
-01
0
_160
0_1
608
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2_1
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0_1
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4_1
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3_1
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5_1
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8_1
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_199
1_1
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4_1
997
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8_1
999
_200
0_1
605
_164
9_1
930
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3_1
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9_1
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2_1
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_163
4_1
688
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0_1
669
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7_1
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0_1
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1_1
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4_1
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1_1
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3_1
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9_1
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3_1
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7_1
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5_1
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3_1
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2_1
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3_1
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7_1
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1_1
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3_1
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0_1
601
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0_1
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0_1
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6_1
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7_1
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_190
8_1
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5_1
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9_1
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6_1
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1
Research Approach 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
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
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
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
Synchrony Analysis
Variogram Identify and model spatial pattern Predict (kriging) unmeasured areas value
Require parameter fitting & model selection
Synchrony Analysis
Variogram
Roadmap to success
Hypothesis refinement Data statements & tests
Roadmap to success
Exploratory data analysis All datasets
Data format (binary, count, continuous…)
Transformations? Outliers? Possible interaction terms (elevation,
forest type)?
Roadmap to success
Summary Analyses Multivariate/NMS (time or space) Clustering (time or space) Repeated measures SEA Variograms (scale)
Roadmap to success
Statistical Inference & Prediction Model based methods
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