Impact Evaluation for Wildlife and Wildlife Habitat of the ...
Imputating snag data to forest inventory for wildlife habitat modeling
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Transcript of Imputating snag data to forest inventory for wildlife habitat modeling
Imputating snag data to forest inventory for wildlife habitat
modelingKevin Ceder
College of Forest ResourcesUniversity of Washington
GMUG – 11 February 2008
Why impute snag data?
• Snags are an important habitat element and needed for habitat assessments.
• These data are often not collected in forest inventory
• The Large-Landscape Wildlife Assessment models will need these data
Why use Nearest-Neighbor?
• Non-parametric requiring no assumptions of underlying functional form
• Retains the variance/covariance structure of the input data in the output data
The Questions
1) Can snag data be imputed using kNN techniques with stand and site data?
2) How well do the results fit observed data?
3) Which distance measure performs best? 4) What is the effect of increasing
neighborhood size?5) How do the results compare with random
sampling?
The Process
• The database– FIA integrated database version 2.1– Data for private forests in western Washington
(1510 plots)– Both tree and snag data collected between
1989 - 1991– Representative of the forest targeted for the
LLWA project
The Process
• The tool - – The yaImpute package for kNN imputation
• Raw, Euclidean, Mahalanobis, MSN, MSN2, ICA, and randomForest distance measures
• k = 1, 2, 3, 4, 5, 10• For k>1 imputed data are distance weighted means of
neighbors
– 9999 permutations of the data for comparisons with random sampling
• k = 1, 2, 3, 4, 5, 10• For k>1 imputed data are distance weighted means of
neighbors using Euclidean distance
The Statistics
• Goodness of fit • Comparison with random
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The Input Data – Tree and site data (xData)
N = 1510 Min Max Mean
Trees per Acre (TOT_TPA) 6.7 2920.5 475.8
Basal Area per Acre (TOT_BA, sqft/ac) 0.0 397.8 119.8
Quadratic Mean Diameter (QMD, in) 0.1 28.5 7.6
Mean Height (MEAN_HT, ft) 1.0 147.9 43.7
Stand Age (AGE, yr) 5 215 37
Site Index (SITE_INDEX_FIA, feet @ 50 yr) 44 180 112
Slope (SLOPE, %) 0 99 24
Aspect (ASPECT_DEG, deg) 0 130 155
Elevation (ELEV_FT, ft) 3 4724 869
The Input Data – Snag data (yData)
N = 1510 Min Max Mean
Snags per Acre (SNAG_TPA_TOTAL) 0.0 96.8 4.8
Basal Area (SNAG_BA, sqft/ac) 0.0 9.7 0.5
Quadratic Mean Diameter (SNAG_QMD, in) 0.0 10.5 2.7
Mean Height (SNAG_ MEAN_HT, ft) 0.0 161.0 14.9
• 695 of 1510 plots did not have snags present
Results
1) Can snag data be imputed using kNN techniques with stand and site data?
Yes!
Results
1) How well do the results fit observed data?
RMSD SPA BA QMD Mean Ht
Min 7.0 0.6 2.5 18.9
Max 11.2 1.0 3.7 28.4
Mean 9.0 0.8 3.0 23.4
Results
1) How well do the results fit observed data?
BIAS SPA BA QMD Mean Ht
Min -1.7 -0.2 -0.6 -4.0
Max -0.1 0.0 0.1 0.3
Mean -0.5 0.0 -0.1 -0.8
Results
1) How well do the results fit observed data?
MAD SPA BA QMD Mean Ht
Min 3.5 .3 1.8 11.3
Max 6.2 0.6 2.7 18.1
Mean 5.1 0.5 2.3 15.1
Results
1) How well do the results fit observed data?
Marginally…
• High RMSD and MAD relative to mean snag measures in the data
• Observed vs imputed plots show poor patterning
Results
2) Which distance measure performs best?
3) What is the effect of increasing neighborhood size?
Results
2) Which distance measure performs best?• All are generally similar• randomForest imputations provide lower
RMSD and MAD but under-predict more than others
3) What is the effect of increasing neighborhood size?
• Increasing k reduces RMSD and MAD• Little effect on bias• Slightly decreased range in imputed values
with k = 10
Results4) How do the results compare with random
sampling?
RMSD k 1 2 3 4 5 10
SNAG_TPA_TOTALNN 11.24 9.96 9.15 8.70 8.62 8.35
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
SNAG_BA_TOTALNN 0.95 0.84 0.77 0.73 0.73 0.71
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
SNAG_QMDNN 3.55 3.10 2.93 2.85 2.79 2.69
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
SNAG_MEAN_HTNN 27.89 24.38 22.78 22.02 21.69 20.87
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
Results4) How do the results compare with random
sampling?
MAD k 1 2 3 4 5 10
SNAG_TPA_TOTALNN 6.03 5.59 5.27 5.16 5.13 4.97
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
SNAG_BA_TOTALNN 0.53 0.49 0.46 0.45 0.44 0.43
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
SNAG_QMDNN 2.51 2.38 2.30 2.26 2.23 2.23
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
SNAG_MEAN_HTNN 17.46 15.86 14.96 14.67 14.51 14.13
p 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
Results4) How do the results compare with random
sampling?
• p-values of 0.001 suggest that there is some underlying very weak relationship between snags and overstory
• Imputation is better than just randomly assigning snags to stands
Why didn’t it work better?
• Very weak correlations between overstory and snags– Snags are from prior stand
• Many of the snags in the FIA database have advanced decay classes
• Often snags are larger than QMD
– Management history• Snags were removed at harvest• Thinning captures mortality
Future Direction
• Assessing the effects of imputed data on habitat model outputs– If there are big differences then what?