GEOG 566

         Advanced spatial statistics and GIScience

April 8, 2018

Spatial and temporal variation in the historical fire regime of the Oregon pumice ecoregion

A description of the research question that you are exploring.

I’m researching historical fire regimes in ponderosa pine, lodgepole pine, and mixed-conifer forests in south central Oregon. Previous fire history reconstructions demonstrate fires were historically frequent occurring every 10-20 years, and were predominantly low severity.  However, the size and sampling design of earlier reconstructions does not describe historical fire sizes and their spatial patterns across landscapes. We mapped historical fire perimeters to answer the following research questions;

1) How did topography, vegetation (fuels), climate, and previous fires constrain fire spread and fire perimeters?

2) Do these constraints create landscape regions (firesheds) with a distinct fire regime?

3) What defines a fireshed?  Do landforms or distinct vegetation types envelop them? In other words, are there significant spatial relationships between firesheds and a combination of topography and vegetation?

3) Are constraints on fire spread and perimeters stable over time or do they vary temporally with climate or land use?


A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent

I systematically reconstructed fire history at 52 plots by removing partial cross sections from 3-10 fire scarred trees within 250 m (20 ha) of plot center. Fire scars were dated to their exact year of formation to build a record of fire events during ~1670-1919. The fire record for unsampled areas is represented by the nearest known fire record from a sampling point using Thiessen polygons (Figure 1 ). This method of interpolation of fire history to unsampled areas assumes that the best predictor of fire history in an unsampled area is the nearest sampled area (Farris 2010). The fire reconstruction area is 85,000 ha and includes a mosaic of landforms, soil types, and forest types.

Fire_maps -Fig. 1


Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.


Fire regimes vary with and are constrained by broad scale top-down drivers primarily climate and local bottom-up drivers including topography, (slope, aspect, landforms, soil) vegetation, and ignitions.

Top Down controls on fire

In central Oregon, climate and ignitions are generally not limiting to fire. Summers are hot and dry and lightning ignitions are abundant (Morris 1934). Previous dendrochronological reconstructions of historical fires demonstrate most fires burned in years with below average spring precipitation (Johnston 2016). However, fire size has not yet been related to climate and we do not know if extensive fires (10,000-80,000 ha) have a different relationship with annual and previous year climate than small fires.

Hypothesis1 – Large fire events depend on continuous fuels in a hot dry year. A series of wetter than average years followed by a drought year provides abundant fuel with low moisture allowing the spread of extensive fires.

If this hypothesis is true, I expect extensive fires to be negatively related to drought in previous years and positively related to drought in the year of the fire event. Small fires should have poorer relationships with climate or may be more common during cool wet years.

Hypothesis 2 – Large and small fire events are limited by fuel not climate.

If this hypothesis is true, I expect no there is no relationship between previous year climate and fire size, and that both extensive and small fires are positively correlated with drought in the year of the fire event. Fire maps would also show that fires did not burn areas that had recently burned.


Bottom-up controls on fire

I expect that bottom-up drivers of topography and fuels are stronger constraints on fire spread than climate, and will explain more of the spatial variation in fire history. Merschel et al. (2018) demonstrated that pumice basins characterized by coarse soils, low productivity, and low fuel abundance constrained spread of some extensive fires and drove spatial variation in fire history on the eastern slope of the central Oregon Cascades. Similar, but more extensive pumice basins occur in this study area. In addition, there are large topographic features including long steep ridges, large volcanic buttes, and gentle rolling topography intermixed across the study region.

Hypothesis 3 – Lodgepole pumice basins limited fire spread because of slow fuel recovery and formed firesheds.

If this hypothesis is true, I expect fire frequency to decrease with increasing abundance of lodgepole pumice basin within an analysis polygon. I also expect variability in fire frequency and the amount of small fires to increase with the increasing abundance of lodgepole pumice basin.

Hypothesis 4 – Fuel recovery controls fire spread throughout the area.

If this hypothesis is true, I expect fire frequency to vary little across the reconstruction area, and that it would not vary significantly among vegetation (forest) types. Time since fire would be the best predictor of whether an area burned in a fire event, and firesheds would not be apparent across the study area.


Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

My first priority is to develop maps of fire events and the metrics that describe fire history.  For example, I would want to map how frequency, variation in frequency, minimum and maximum fire interval, and fire size vary spatially across the study area. It would be very helpful to develop fire maps that also show how time since last fire spatially varied across the study area.  This would help us understand whether fuel recovery limits or constrains fire in the region. I’ve made an animation of fire maps through time before, but it was extremely glitchy and limited in ArcMap. I would like to learn a better animation process because videos of fire maps are effective in presentations. Ideally unburned reconstruction polygons would be shaded by time since fire, current year fires would be depicted in red, and the maps would include a sidebar that summarized drought conditions in the current year. The viewer would simultaneously learn how fires were related to topography, vegetation type, fuel recovery, and climate.

Potential analyses (I’ve got a lot more to learn about)

Cluster analysis to identify fire regime types based on fire regime metrics

Hot Spot analysis to identify how fire history or fire regime metrics vary spatially

Superposed ephoch analysis to identify relationships between climate and fire occurrence and climate and fire size.

Generalized linear mixed modeling to check for spatial autocorrelation in fire regime metrics and to understand relationships between fire regime metrics and topography, vegetation, and landscape structure


Expected outcome: what do you want to produce — maps? statistical relationships? other?

I want to produce maps of fire events and fire regime metrics.

I would like to know how fire size and occurrence was statistically related to climate

I want to produce statistical models that describe how fire regimes vary spatially with topography, vegetation type, and fuel recovery. I suspect that relationships may vary with climate and that interactions between variables may be important (e.g. vegetation type may only be important on steep volcanic buttes)


Significance. How is your spatial problem important to science? to resource managers?

Currently there is much interest in restoring fire in forested landscapes with a history of fire exclusion and vigorous debate on the historical role of fire in different forest types and environmental settings. The science of fire ecology provides us with a good theoretical understanding of what drives variation in fire regimes. However, few datasets available allow us to quantify what drivers of variation have the most influence, and how they interact. By identifying what drives variation in fire regimes we can better plan for, manage, and reintroduce fire into forests with a history of fire suppression.


  1. I’m comfortable working with Arc-Info to make maps, but would like more experience with spatial statistics.
  2. I’m a Python beginner
  3. I’m comfortable working through problems in R, but I often have sloppy code. I could take a big step forward by learning how to produce maps in R and perform multivariate stats in R.




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  1.   merschea — April 9, 2018 @ 8:43 am    

    Hi Julia,

    Thank you for the feedback. I edited the main post above to include a draft map of some fire events because I’m unable to make an attachment to a comment (Fire_maps_test.PDF).

    There are over a 100 fires to map so I produced 9 maps of larger fires since 1770. In some cases I included two fire events on one map if they occurred within 5 years of each other. I can produce maps of each fire using Thiessen polygons, but it sure is slow the way I’m currently making them.

  2.   jonesju — April 8, 2018 @ 10:38 pm    

    hi Andrew,
    Thanks for the good summary and helpful map of your spatial sampling design. It seems like first thing you should create a GIS layer for these sampling points and attribute each point with the date(s) of recorded fires. Then you could create a dot map showing which sites recorded a fire on each date.

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