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Imputing plot-level tree attributes to pixels and aggregating to stands in forested landscapes. Andrew T. Hudak, Nicholas L. Crookston, Jeffrey S. Evans USDA Forest Service Rocky Mountain Research Station Moscow, Idaho Michael J. Falkowski University of Idaho

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imputing plot level tree attributes to pixels and aggregating to stands in forested landscapes

Imputing plot-level tree attributes to pixels and aggregating to stands in forested landscapes

Andrew T. Hudak, Nicholas L. Crookston, Jeffrey S. Evans

USDA Forest Service

Rocky Mountain Research Station

Moscow, Idaho

Michael J. Falkowski

University of Idaho

Department of Forest Resources

Moscow, Idaho

Brant Steigers, Rob Taylor

Potlatch Forest Holdings, Inc.

Lewiston, Idaho

Halli Hemingway

Bennett Lumber Products, Inc.

Princeton, Idaho

outline
Outline
  • LiDAR for Precision Forest Management
  • Regression-based basal area prediction
  • LiDAR-derived predictor variables
  • randomForest-based basal area prediction
  • Aggregating to the stand level
  • Imputation-based basal area prediction
lidar project areas
LiDAR Project Areas

Hudak et al. (2006), Regression modeling and mapping of coniferous forest basal area and tree density from

discrete-return lidar and multispectral satellite data. Canadian Journal of Remote Sensing 32: 126-138.

field sampling
Field Sampling
  • Plots randomly placed within strata defined by:
    • Elevation
    • Solar insolation
    • NDVIc (satellite image-derived indicator of Leaf Area Index)
    • 3 (elevation) x 3 (insolation) x 9 (NDVIc) = 81 strata / study area
  • 1/10 acre plots in Moscow Mountain study area
  • 1/5 acre plots in St. Joe Woodlands study area
  • Fixed radius plot for all trees >5” dbh, circumscribed by variable radius plot for large trees
slide6

Airborne LiDAR and Satellite Image Data Acquisitions

LiDAR surveys collected summer 2003

(ALI = Advanced Land Imager)

predicted basal area ln transformed regression model
Predicted Basal Area (ln-transformed) regression model

Hudak et al. (2006), Regression modeling and mapping of coniferous forest basal area and tree density from

discrete-return lidar and multispectral satellite data. Canadian Journal of Remote Sensing 32: 126-138.

slide8

Hudak et al. (2006), Regression modeling and mapping of coniferous forest basal area and tree density from

discrete-return lidar and multispectral satellite data. Canadian Journal of Remote Sensing 32: 126-138.

multiple linear regression basal area
Multiple Linear Regression – Basal Area

N=13678 pixels

Hudak et al. In press

Canadian J. Remote Sensing

Adjusted R2=0.91

predictor variables
Predictor Variables

Heights

Minimum

Maximum

Range

Mean

Standard Deviation

Coefficient of Variation

Skewness

Kurtosis

Average Absolute Deviation

Median Absolute Deviation

5th, 25th, 50th, 75th, 95th Percentiles

Interquartile Range

Canopy Relief Ratio

  • (mean – min) / (max – min)

Intensity

predictor variables cont d
Predictor Variables, cont’d.

Canopy Density

DENSITY - Percent vegetation returns

  • measure of total canopy density

STRATUM0 - Percent ground returns

STRATUM1 - Percent veg returns >0 and <=1m

TXT – Standard deviation of returns >0 and <=1m

  • texture measure of ground clutter

STRATUM2 - Percent veg returns >1 and <=2.5m

STRATUM3 - Percent veg returns >2.5 and <=10m

STRATUM4 - Percent veg returns >10 and <=20m

STRATUM5 - Percent veg returns >20 and <=30m

STRATUM6 - Percent veg returns >30m

PCT1 - Percent 1st returns

PCT2 - Percent 2nd returns

PCT3 - Percent 3rd returns

predictor variables cont d1
Predictor Variables, cont’d.

SLP – Slope (degrees)

SLPCOSASP – Slope * cos(Aspect)

SLPSINASP – Slope * sin(Aspect)

INSOL – Solar Insolation

TSRAI – Topographic Solar Radiation Aspect Index

  • (1 - cos((pi / 180)(Aspect - 30))) / 2

Topography

randomforest model breiman 2001 liaw and wiener 2005
randomForest Model (Breiman 2001; Liaw and Wiener 2005)
  • Generates a “Forest” of multipleclassification trees
  • Nonparametric bootstrap
  • 30% out of bag (OOB) random sample
  • Provides robust model fitting
  • Freely available R package
importance plot basal area
Importance Plot – Basal Area

26 Variables in final model

30% Out of bag sample

10,000 Bootstrap iterations

100 Node permutations

Random variable subsets

89.97% variation explained

equivalency plot1
Equivalency Plot

Region of Similarity, Intercept

equivalency plot2
Equivalency Plot

Region of Similarity, Intercept

No bias

equivalency plot3
Equivalency Plot

Region of Similarity, Slope

Region of Similarity, Intercept

No bias

equivalency plot4
Equivalency Plot

Region of Similarity, Slope

No disproportionality

Region of Similarity, Intercept

No bias

slide26

Alternative virtual inventory approaches.

  • A systematic sample over a set of polygons
  • A separate systematic sample in each polygon.
stand subsamples
Stand Subsamples

Triangular sample design

Captures spatial variation

150m spacing

Systematic - offset rows

equivalency plot5
Equivalency Plot

Slight disproportionality,

not significant

Slight overprediction

bias, not significant

(N = 50 stands)

yaimpute package crookston and finley in prep
yaImpute package (Crookston and Finley, in prep)
  • Eight options for k-NN imputation
    • including MSN, GNN, randomForest
  • Comparative plotting functions
  • Mapping capability
  • Freely available R package
slide34

Twelve lidar-derived

predictor variables

(X’s) used to impute

and map Basal Area

of 11 conifer species

(Y’s) with the

yaImpute package

Hudak et al. (In Review), Nearest neighbor imputation modeling of species-level, plot-scale structural attributes from LiDAR data. Remote Sensing of Environment.

equivalency plot6
Equivalency Plot

Slight disproportionality,

significant

Slight bias towards

overprediction,

insignificant

slide37

Equivalency Plot

Strong disproportionality,

significant

Strong bias towards

overprediction,

significant

conclusions
Conclusions:
  • LiDAR metrics provide detailed structure information
  • Our sampling design based on a spectral data-derived LAI index may have inadequately stratified our landscapes based on basal area variation
  • Stand exams may not represent an unbiased sample of the full range of conditions in these landscapes, which is problematic for landscape-level inferences
  • The R packages randomForest and yaImpute hold much promise for modeling and mapping, as regression and imputation tools
  • Necessary next step is to impute tree lists from the LiDAR predictor variables for input into FVS
slide42

Questions?

Acknowledgments

  • Funding:
    • Agenda 2020 Program
  • Industry Partners:
    • Potlatch Land Holdings, Inc.
    • Bennett Lumber Products, Inc.
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