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Modelling Historic Fire Boundaries Using Inventory Data
2004 Western Forest Mensurationists Conference
Kah-Nee-Ta Resort, Warm Springs, ORJune 20-22, 2004
Rueben SchulzDr. Peter Marshall
Project and Study Area
• “Comparing Stand Origin Ages with Forest Inventory Ages on a Boreal Mixedwood Landscape”– Historic fire mapping in Saskatchewan– 90 000 ha (220 000 acre) study area– Area only recently logged
Project and Study Area
We are here
Project Context
• Emulate natural disturbance regimes for management – Ecosystems will continue to
function if we keep disturbances like they were historically
– Wildfire is the primary disturbance on this landscape
• Time-Since-Fire dataset • Use existing inventory data
– derive fire information – guide sampling
Image: NASA and Canadian Forest Service
Datasets: Time-Since-Fire
• Time-since-fire (TSF) data – Records the year of the last wildfire disturbance – Expensive to collect
• TSF does not record site and species differences
• Does not include human-made features• Ages recorded to the nearest year• Location of ground plots different from inventory
– Near fire edges
• ~ 900 fire polygons
Datasets: TSF
Datasets: Forest Inventory
• New inventory follows the Saskatchewan Forest Vegetation Inventory (SVI) standard
• Inventory records current condition
• Includes human-made features
• Up to 3 tree layers
• Ages in 10 year classes
• ~10 000 inventory polygons
What was done
1. Sampling to collect time-since-fire data
2. Analysis:1. Regression modelling
2. Clustering fire events
Sampling
• Data over the full study area was collected in 2002– Aerial photos – fire
boundaries and location of ground plots
– Ground plots – tree ages, fire scars and release information
• Combined to produce a time-since-fire dataset
Single-Aged Regression Models
Forest Inventory
Multi-Aged Regression Models
Fill in missing values from neighbours
Predicted Time-Since-
Fire
Cluster Predicted Time-Since-Fire
Predicted Fire Events
Analysis Overview
Regression Modelling
• Predict time-since-fire from forest inventory inputs– Inventory stand used as unit of observation– Time-since-fire is the dependent variable – Continuous predicted variable
• not continuous in space
• Linear model forms
• Used GLM with categorical inputs as dummy variables
Modelling: Single-Aged Polygons
• Significant variables– Inventory stand age– Modification value– Leading and Secondary species– Average stand age within 400m
• R2 ~ 0.4
Modelling: Multi-Aged Polygons
• More age values to deal with
• Significant variables– Inventory stand age (average)– Modification value– Average stand age (max) within 400m
• R2 ~ 0.25
Modelling: No-Aged Polygons
• Stands with no inventory information – Roads, gas lines, clearcuts
• Large, road and gas polygons split up
• Assigned neighbour values to fill holes
Modelling: Output
Modelling: Fit
Clustering
• Models only predict time-since-fire for an individual forest inventory stand
• Want location and extent of fire events
• Grouped stands with similar predicted time-since-fire using hierarchical clustering
• Was spatially constrained– Penalty distance matrix added to clustering
Clustering: Output
Clustering: Output
Clustering: Fit
Challenges and Future
• Forest inventory ages are not a direct substitution for time-since-fire
• More work to do– R2 for models is poor
• Future work– Examine autocorrelation in regression models– Improve fire boundary detection– Inclusion of additional data: DEM, Landsat
imagery
Acknowledgements
• NSERC• Forest Development Fund of the Saskatchewan
Forest Centre (SFC)• Sustainable Forest Management (SFM) Network• Forest Ecosystems Branch of the Saskatchewan
Environment and Resource Management (SERM)
• Mistik Management Ltd. • Phil Loseth –draft inventory manual, last years
conference