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 1.Sampling to collect time-since-fire data 2.Analysis: 1.Regression modelling 2.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. 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

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 R 2 ~ 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 R 2 ~ 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

20. Clustering: Fit

21. Challenges and Future Forest inventory ages are not a direct substitution for time-since-fire More work to do –R 2 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