Spatial Analysis of Large Tree Distribution of FIA Plots on the

Lassen National Forest

Tom Gaman, East-West Forestry Associates, Inc

Kevin Casey, USDA-FS R5 Remote Sensing Lab

Our FIA Investigation

1. Examine the Statistical Value of Collecting Hectare Tree Data.

2. Test spatial relationships among trees using the distance and bearing data to index plot structure.

3. Create plot “cartoons” and relate to high-resolution imagery, hence to landscape

1. Examine the Statistical Value of Collecting Hectare Tree Data.

2002 Lassen National Forest Densification & FIA Hex Plots• 132 plots – 83

have HA trees

• 66 had multiple HA trees >= 32.0” dbh

1 ac. Vs. 1 ha.! What do we gain from the extra work?

Methods

A. Select large trees on annular plot using the distance and azimuth data.

B. Create 2 data sets for each plot1. All Hectare Trees2. Hectare Trees on ¼ acre annular plots only

C. Statistical Analysis # Large Trees. Acre data (expanded to ha value) vs. hectare data by mapped forest “Type”

The Raw Data

Data Comparison by Forest Type

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An equalized

Statistical Analysis of Results

Statistical Analysis of Results Sample size: Hectare Sample size: AcreNo. of Large Trees No. of Large Trees

All plots All PlotsMeasured on full Hectare expanded to per ha. Basis)Mean 3.857143 Mean 4.143105Standard Error 0.455235 Standard Error 0.589473Median 1 Median 2.471Mode 0 Mode 0Standard Deviation 5.250026 Standard Deviation 6.798135Sample Variance 27.56277 Sample Variance 46.21464Range 29 Range 37.065Minimum 0 Minimum 0Maximum 29 Maximum 37.065Sum 513 Sum 551.033Count 133 Count 133

Conclusions. 1. Though SE consistently somewhat less for HA sampling by type, difference would reduce with larger # plots.2. Smaller (4 of the ¼ acre) samples may overestimate # large trees/ha 3. Ha Plots offer an excellent tool for more accurately quantifying large trees on individual plots but of limited value in reducing the error for large samples.

11.8% 14.0%

Statistical Analysis of Results Mean # HA trees/Hectare by type

Conclusions. 1. Though SE consistently somewhat less for HA sampling by type, difference would reduce with larger # plots.2. Smaller (4 of the ¼ acre) samples may overestimate # large trees/ha 3. Ha Plots offer an excellent tool for more accurately quantifying large trees on individual plots but of limited value in reducing the error for large samples.

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Mean Mean Mean Mean Mean Mean

Measured on full Hectare

expanded to per ha. Basis)

2. Test spatial relationships among trees using the distance and bearing data to index plot structure.

Creating the “Clumping Index”

Methods

• Selected 11 plots…1 randomly for each forest “Type” per regional type map.

• Calculate coordinate locations of each tree and create a shapefile for each plot.

0614151

Tested a variety

of methods which

did not work

0606172

Calculate Proximity

• Use Proximity Analysis tool to create “PROXIMITY POLYGONS”

• Calculate area of each polygon and total plot

0614151

Calculate “Clumping Index”• Divide total area by number of trees to obtain AVERAGE polygon

which would represent equal spacing (as in a plantation)• Divide actual area of each PROXIMITY POLYGON by AVERAGE

thus obtaining an expression of POLYGON CLUMPING.• Average the two lowest POLYGON CLUMPING values to obtain the

CLUMPING INDEX.

Clumping Index Values

Clumping Index Values for Selected FIA P lots on the Lassen National Forest

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H14335 H15306 H14715 H14151 H15367 H15615 H06258 H15039 H15554 H15428 H15983

Plot

Clumping Index

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ping Index Values

Low Value—Most Clumping High Value—even distribution

0614335 0615554

Clumping Value = 1 no clumping!

Conclusions from Clump Index:

• “Indexing” may be a valuable use of FIA for ecological modeling of variability within the landscape

• Would be interesting to examine relationships among snags and smaller trees

• The index could be derived directly from tree data without all the interim steps.

3. Create plot “cartoons” and relate to high-resolution imagery, hence to landscape.

High Resolution Digital Imagery

We were curious if tree cartoons generated by SVS would approximate digital imagery captured by a low flying aircraft.

The following slides show these two data sets side-by side.

Hectare trees are usually apparent on the imagery, but only in relatively open-grown stands.

Mapped the Inventory Plots using Distance and Azimuth Data from Tree (*.TRE) files

All Trees

Large Trees on the Annular Plot only

Trees on the entire Hectare

Conclusions from SVS process:

• Trees can be realistically drawn in their true locations using SVS.

• GPS coordinates can be used to locate plot centers on geo-referenced imagery.

• Annular and hectare plots can be accurately drawn over imagery.

• In dense stands individual trees are very difficult to identify.

0615367 clump index 0.19 med high

0615039 clump index 0.166

0614715 (clump index = 0.134 medium high)

0606258 (clump index 0.435 medium)

0615554 (clump index 0.95 low)

Conclusions

• Spatial Analysis allows us to extract more information on individual plots

• FIA data may be valuable in modeling relationships

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