The Effects of Spatial Patterns on Canopy Cover Estimated by FVS (Forest Vegetation Simulator) A Thesis Defense by Treg Christopher Committee Members: Jim Chew John Goodburn Brian Steele Hans Zuuring

Outline of Presentation §Overview of what canopy cover is and why it is important §Techniques for estimating canopy cover §Results of overall accuracy of FVS estimates of cover §The effects of spatial patterns on FVS estimates of cover

Effects of Canopy Cover on Ecosystem Fluxes

Fungal and Microbial Activity Fungal hyphae on a decaying log

Thermal Cover for Deer

Effects on understory growth

Shade For Increasing Regeneration Success after Timber Harvest

Tree Crowns

Different methods for measuring tree crowns and their associated cover

Canopy Cover = Sum of Individual Crown Areas / Area of the Plot

Canopy Cover with Overlapping Crowns

Field Methods for Measuring Canopy Cover

Methods for Measuring Canopy Cover using Aerial Photography

Vegetation Models’ Estimates of Canopy Cover

FVS Components §spatially-independent §predicts crown widths from empirically derived equations based on DBH, Species, Height and Crown Ratio §predicts an initial canopy cover from the sum of the area of these crowns §adjusts for overlap using an equation based on the assumption of a random distribution of trees described by the equation: l Cover = 100*[1-exp(-0.01*C’)] Where C’ is canopy cover uncorrected for overlap

Under Estimation of Canopy Cover by FVS From Applegate, 2000

Objectives §Evaluate the performance of two different methods of estimating cover developed in a GIS §Determine the overall accuracy of FVS estimates of canopy cover across a large geographic area §Determine the effects of spatial patterns on the FVS estimates of canopy cover

Objective 1: Evaluate the Performance of Two Methods of Estimating Cover Developed in a GIS

Geographic Information Systems (GIS) §Designed for storing, processing and analyzing spatially- referenced data §Incorporates FVS crown widths into the analysis of cover (this isolates differences in estimates due to spatial patterns from differences in estimates due to potentially inaccurately modeled crown widths) §Allows for generation and an examination of simulated patterns

Crown Area that FVS Includes in Estimates of Cover

§Step 1: Create a Stem Map

§Step 2: Combine FVS Generated Crown Widths with the Stem Map to Create Individual Tree Crowns

§Step 3: Dissolve the Borders Between Crowns

Step 4: GIS “Bole” Method

Step 5: GIS “Crown” Method

The difference between methods for estimating cover and the effects of plot size (ft 2 )

Findings for Evaluating the Performance of Two Methods of Estimating Cover Developed in a GIS §GIS “Crown” method had only a slight bias when compared against FVS estimates of cover for 1900 simulations of random distribution §GIS “Bole” method had a strong overestimating bias compared to FVS and this bias was a function of plot size §The GIS “Crown” method was used to test FVS estimates of cover for non-random patterns

Objective 2: Determine the Overall Accuracy of FVS Estimates

FVS versus GIS “Crown” estimates of canopy cover

Findings: Overall Accuracy of FVS Estimates §The mean difference between GIS (“Crown” method) and FVS estimates of canopy cover for 395 plots across northern Idaho was less than 1% §The standard deviation of this difference was approximately 8% §Any potentially remaining inaccuracies of FVS estimates may still exist as a result of inaccurately modeled individual crown widths

Objective 3: Determine the effects of spatial patterns on the FVS estimates of canopy cover

Regular/Even Patterns

Clumped spatial patterns

Regular Spatial Pattern at Small Scale

Ripley’s K(d) statistic

Example of an Old-Growth Western Hemlock Plot (Large circles:DBH > 5”, small triangles: DBH <5”)

Pattern for Trees Less Than 5” DBH

Pattern for Trees Greater Than or Equal to 5” DBH

Scatter plot of FVS versus GIS cover for the “DBH greater than or equal to 5 inches” spatial attribute

Simulations of Regular Patterns

Findings: Effects of Spatial Patterns on Estimates of Cover by FVS §Regular patterns resulted in average underprediction by FVS of 7% §Regular patterns from simulated plots resulted in average underprediction by FVS of 11% §Clustered patterns resulted in average overprediction by FVS of 3%

Conclusion §FVS accurately predicts canopy cover (disregarding potential errors due to inaccurately modeled individual crown widths) across a large number of plots in northern Idaho. §Spatial patterns effect the precision of FVS estimates by causing underestimation of cover for regular tree patterns and overestimation of cover for clustered tree patterns.

Recommendations §While information on spatial patterns may be useful for improving estimates of cover from FVS, in practice it is difficult to obtain this information §However, if certain patterns consistently appear in specific stand conditions then it would be possible to use some other measure of stand information as surrogates for measurements of spatial patterns §A modifier can then be added to the FVS cover equation to automatically adjust estimates given specific conditions:

Possible Surrogates for Spatial Patterns §Structural classes §Habitat type §Age

And with that, the trees rode off into the sunset...

FVS versus GIS “Crown” estimates of canopy cover

Scatter plot of FVS versus GIS cover for the “bivariate” spatial attribute

Scatter plot of FVS versus GIS cover for the “DBH less than 5 inch” spatial attribute

Scatter plot of FVS versus GIS cover by FVS stand structural characteristics