Modelling Historic Fire Boundaries Using Inventory Data

1. Modelling Historic Fire Boundaries Using Inventory Data 2004 Western Forest Mensurationists Conference Kah-Nee-Ta Resort, Warm Springs, OR June 20-22, 2004 Rueben Schulz Dr. Peter Marshall

2. 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

3. Project and Study Area We are here

4. 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

5. 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

6. Datasets: TSF

7. 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

8. What was done • Sampling to collect time-since-fire data • Analysis: • Regression modelling • Clustering fire events

9. 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

10. Analysis Overview Single-Aged Regression Models Predicted Time-Since-Fire Multi-Aged Regression Models Forest Inventory Fill in missing values from neighbours Predicted Fire Events Cluster Predicted Time-Since-Fire

11. 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

12. Modelling: Single-Aged Polygons • Significant variables • Inventory stand age • Modification value • Leading and Secondary species • Average stand age within 400m • R2 ~ 0.4

13. 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

14. 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

15. Modelling: Output

16. Modelling: Fit

17. 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

18. Clustering: Output

19. Clustering: Output

20. Clustering: Fit

21. 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

22. 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