SlideShare uma empresa Scribd logo
1 de 34
Error Propagation in Forest Planning Models Karl R. Walters and Sean J. Canavan
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measurement Error (ME) in Forestry ,[object Object],[object Object],[object Object],[object Object]
Measurement Error (ME) in Forestry ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measurement Error (ME) in Forestry ,[object Object],[object Object],[object Object],[object Object]
Normal (0,1) PDF x 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 -5 -4 -3 -2 -1 0 1 2 3 4 5 f(x)
Normal (O,1) CDF x 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -5 -4 -3 -2 -1 0 1 2 3 4 5 F(x) = P(X < x)
Traditional Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Traditional Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Normal (O,1) CDF x 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -5 -4 -3 -2 -1 0 1 2 3 4 5 F(x) = P(X < x)
Fitted CDF Equation 0.00 0.20 0.40 0.60 0.80 1.00 -1.5 -1 -0.5 0 0.5 1 1.5 Error Size Cumulative Probability  Pr(   = 0)  Pr(   < 0)  Pr(   > 0) P(X =  x ) =  Pr(   < 0)*Negative Error CDF     < 0 Pr(   < 0) + Pr(   = 0)     = 0 Pr(   < 0) + Pr(   = 0) + Pr(   > 0)*Positive Error CDF      > 0
Fits of Error Type Probabilities 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.5 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 Dbh (inches) Probability P(e > 0) P(e = 0) P(e < 0)
Empirical Dbh Error CDF Surface Cumulative Probability Dbh (inches) Error (inches) 0.25 0.50 0.75 1.00 0.0 20.0 30.0 40.0 -0.6 0.0 0.6 1.2 1.8 10.0
Fitted Dbh CDF Surface
Forest Modeling Experiment ,[object Object],[object Object],[object Object],[object Object],[object Object]
Example Forest ,[object Object],[object Object],[object Object],[object Object],[object Object]
Model Parameters ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Unconstrained ,[object Object],[object Object]
Unconstrained ,[object Object],[object Object],[object Object],[object Object],[object Object]
Unconstrained ,[object Object],[object Object],[object Object]
Unconstrained 4,838,760  5,142,948  4,925,347  5,180,696  Total Harvest 33,076  32,206  33,355  28,352  HW saw logs 4,403  4,275  4,428  4,333  RC saw logs 18,073  16,731  47,399  16,736  WW oversize logs 591,469  613,119  615,886  606,089  WW saw logs 224,367  220,707  232,815  221,610  DF oversize logs 3,467,413  3,756,908  3,480,189  3,795,724  DF saw logs 499,959  499,000  511,276  507,851  DF export logs Dbh_Ht Err  Ht_Err  Dbh_Err  Clean  50 Year Summary
Unconstrained ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Unconstrained 36,766  36,144  36,423  35,213  commercial thin 49  19  49  -  precomm thin 537tpa 36  -  19  266  precomm thin 436tpa -  -  -  -  precomm thin 360tpa -  -  -  -  precomm thin 300tpa 18,142  18,142  18,142  17,039  slashing -  -  -  -  planted NF 680tpa 5,656  5,656  5,656  5,108  planted NF 550tpa 19,381  20,504  19,381  30,342  planted DF 550tpa 30,912  29,820  30,850  19,586  planted DF 450tpa 55,948  55,980  55,886  55,036  clearcut Dbh_Ht Err  Ht_Err  Dbh_Err  Clean  50 Year Summary
Nondeclining yield ,[object Object],[object Object]
Nondeclining Yield ,[object Object],[object Object],[object Object]
Nondeclining Yield ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Nondeclining Yield 3,498,616  3,624,749  3,561,097  3,654,132  Total Harvest 35,169  33,695  34,992  33,844  HW saw logs 5,815  5,767  6,181  6,099  RC saw logs 27,815  27,137  57,946  27,880  WW oversize logs 798,669  803,591  819,808  824,113  WW saw logs 351,540  316,824  352,489  322,816  DF oversize logs 1,636,676  1,764,109  1,631,819  1,761,552  DF saw logs 642,932  673,628  657,862  677,828  DF export logs Dbh_Ht Err  Ht_Err  Dbh_Err  Clean  50 Year Summary
Nondeclining Yield ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Nondeclining Yield 22,493  21,884  21,600  21,368  commercial thin 228  19  1941  -  precomm thin 537tpa 537  -  367  266  precomm thin 436tpa 161  256  -  716  precomm thin 360tpa 74  56  105  56  precomm thin 300tpa 18,142  17,094  17,145  17,094  slashing -  -  -  -  planted NF 680tpa 5,019  5,019  5,019  5,019  planted NF 550tpa 18,704  21,878  16,636 20,809  planted DF 550tpa 21,085  17,931 23,117  18,978  planted DF 450tpa 44,808  44,828  44,772  44,806  clearcut Dbh_Ht Err  Ht_Err  Dbh_Err  Clean  50 Year Summary
Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Discussion ,[object Object],[object Object],[object Object]
Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Discussion ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Thank you. Any questions?

Mais conteúdo relacionado

Destaque

1 measurement and error
1 measurement and error 1 measurement and error
1 measurement and error LOHYINNEE
 
Measurement And Error
Measurement And ErrorMeasurement And Error
Measurement And Errorwilsone
 
Linear measurements
Linear measurementsLinear measurements
Linear measurementsNaman Dave
 
Back propagation
Back propagationBack propagation
Back propagationNagarajan
 
Types of errors
Types of errorsTypes of errors
Types of errorsRima fathi
 
Errors and uncertainties
Errors and uncertaintiesErrors and uncertainties
Errors and uncertaintiesdrmukherjee
 
The propagation of action potentials along the axon.
The propagation of action potentials along the axon.The propagation of action potentials along the axon.
The propagation of action potentials along the axon.Christiane Riedinger
 

Destaque (10)

1 measurement and error
1 measurement and error 1 measurement and error
1 measurement and error
 
Unit 3
Unit 3Unit 3
Unit 3
 
Measurement And Error
Measurement And ErrorMeasurement And Error
Measurement And Error
 
Errors in measurement
Errors in measurementErrors in measurement
Errors in measurement
 
Linear measurements
Linear measurementsLinear measurements
Linear measurements
 
Measurement & Error
Measurement & ErrorMeasurement & Error
Measurement & Error
 
Back propagation
Back propagationBack propagation
Back propagation
 
Types of errors
Types of errorsTypes of errors
Types of errors
 
Errors and uncertainties
Errors and uncertaintiesErrors and uncertainties
Errors and uncertainties
 
The propagation of action potentials along the axon.
The propagation of action potentials along the axon.The propagation of action potentials along the axon.
The propagation of action potentials along the axon.
 

Semelhante a Error Propagation in Forest Planning Models

Imaging for Radiotherapy delivery and verification
Imaging for Radiotherapy delivery and verificationImaging for Radiotherapy delivery and verification
Imaging for Radiotherapy delivery and verificationMiami Cancer Institute
 
2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...
2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...
2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...Hector Archila
 
Industrial plant optimization in reduced dimensional spaces
Industrial plant optimization in reduced dimensional spacesIndustrial plant optimization in reduced dimensional spaces
Industrial plant optimization in reduced dimensional spacesCapstone
 
Design of experiment methodology
Design of experiment methodologyDesign of experiment methodology
Design of experiment methodologyCHUN-HAO KUNG
 
Step by step Resource Estimation using Numerical Model_M. Alibasya .pdf
Step by step Resource Estimation using Numerical Model_M. Alibasya .pdfStep by step Resource Estimation using Numerical Model_M. Alibasya .pdf
Step by step Resource Estimation using Numerical Model_M. Alibasya .pdfalibasya97
 
SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...
SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...
SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...Toru Tamaki
 
Math 533 ( applied managerial statistics ) final exam answers
Math 533 ( applied managerial statistics ) final exam answersMath 533 ( applied managerial statistics ) final exam answers
Math 533 ( applied managerial statistics ) final exam answersPatrickrasacs
 
Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...
Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...
Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...CHENNAKESAVA KADAPA
 
Robust 03015 process RGray apex15
Robust 03015 process RGray apex15Robust 03015 process RGray apex15
Robust 03015 process RGray apex15Robert Gray
 
Zero rotation aproach for droop improvement in
Zero rotation aproach for droop improvement inZero rotation aproach for droop improvement in
Zero rotation aproach for droop improvement ineSAT Publishing House
 
Bump hunting とその応用
Bump hunting とその応用Bump hunting とその応用
Bump hunting とその応用Hideo Hirose
 
Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...
Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...
Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...Waqas Tariq
 
Flexscore: Ensemble-based evaluation for protein Structure models
Flexscore: Ensemble-based evaluation for protein Structure modelsFlexscore: Ensemble-based evaluation for protein Structure models
Flexscore: Ensemble-based evaluation for protein Structure modelsPurdue University
 
Sustainable Manufacturing: Optimization of single pass Turning machining oper...
Sustainable Manufacturing: Optimization of single pass Turning machining oper...Sustainable Manufacturing: Optimization of single pass Turning machining oper...
Sustainable Manufacturing: Optimization of single pass Turning machining oper...sajal dixit
 
Real Time Human Posture Detection with Multiple Depth Sensors
Real Time Human Posture Detection with Multiple Depth SensorsReal Time Human Posture Detection with Multiple Depth Sensors
Real Time Human Posture Detection with Multiple Depth SensorsWassim Filali
 
Comparing Machine Learning Algorithms in Text Mining
Comparing Machine Learning Algorithms in Text MiningComparing Machine Learning Algorithms in Text Mining
Comparing Machine Learning Algorithms in Text MiningAndrea Gigli
 

Semelhante a Error Propagation in Forest Planning Models (20)

3D final
3D final3D final
3D final
 
Imaging for Radiotherapy delivery and verification
Imaging for Radiotherapy delivery and verificationImaging for Radiotherapy delivery and verification
Imaging for Radiotherapy delivery and verification
 
2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...
2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...
2015, wbc, archila, h., measurement of the in plane shear moduli of bamboo-gu...
 
Industrial plant optimization in reduced dimensional spaces
Industrial plant optimization in reduced dimensional spacesIndustrial plant optimization in reduced dimensional spaces
Industrial plant optimization in reduced dimensional spaces
 
Design of experiment methodology
Design of experiment methodologyDesign of experiment methodology
Design of experiment methodology
 
Step by step Resource Estimation using Numerical Model_M. Alibasya .pdf
Step by step Resource Estimation using Numerical Model_M. Alibasya .pdfStep by step Resource Estimation using Numerical Model_M. Alibasya .pdf
Step by step Resource Estimation using Numerical Model_M. Alibasya .pdf
 
SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...
SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...
SMOOTHING POSTERIOR PROBABILITIES WITH A PARTICLE FILTER OF DIRICHLET DISTRIB...
 
Math 533 ( applied managerial statistics ) final exam answers
Math 533 ( applied managerial statistics ) final exam answersMath 533 ( applied managerial statistics ) final exam answers
Math 533 ( applied managerial statistics ) final exam answers
 
Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...
Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...
Inf-Sup Stable Displacement-Pressure Combinations for Isogeometric Analysis o...
 
Robust 03015 process RGray apex15
Robust 03015 process RGray apex15Robust 03015 process RGray apex15
Robust 03015 process RGray apex15
 
LargeRDFBench
LargeRDFBenchLargeRDFBench
LargeRDFBench
 
Zero rotation aproach for droop improvement in
Zero rotation aproach for droop improvement inZero rotation aproach for droop improvement in
Zero rotation aproach for droop improvement in
 
Saga.lng
Saga.lngSaga.lng
Saga.lng
 
Bump hunting とその応用
Bump hunting とその応用Bump hunting とその応用
Bump hunting とその応用
 
LexanHF1110R
LexanHF1110RLexanHF1110R
LexanHF1110R
 
Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...
Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...
Optimum Algorithm for Computing the Standardized Moments Using MATLAB 7.10(R2...
 
Flexscore: Ensemble-based evaluation for protein Structure models
Flexscore: Ensemble-based evaluation for protein Structure modelsFlexscore: Ensemble-based evaluation for protein Structure models
Flexscore: Ensemble-based evaluation for protein Structure models
 
Sustainable Manufacturing: Optimization of single pass Turning machining oper...
Sustainable Manufacturing: Optimization of single pass Turning machining oper...Sustainable Manufacturing: Optimization of single pass Turning machining oper...
Sustainable Manufacturing: Optimization of single pass Turning machining oper...
 
Real Time Human Posture Detection with Multiple Depth Sensors
Real Time Human Posture Detection with Multiple Depth SensorsReal Time Human Posture Detection with Multiple Depth Sensors
Real Time Human Posture Detection with Multiple Depth Sensors
 
Comparing Machine Learning Algorithms in Text Mining
Comparing Machine Learning Algorithms in Text MiningComparing Machine Learning Algorithms in Text Mining
Comparing Machine Learning Algorithms in Text Mining
 

Mais de KR Walters Consulting Services

Barber Revisited: Aggregate Analysis in Harvest Scheduling Models
Barber Revisited: Aggregate Analysis in Harvest Scheduling ModelsBarber Revisited: Aggregate Analysis in Harvest Scheduling Models
Barber Revisited: Aggregate Analysis in Harvest Scheduling ModelsKR Walters Consulting Services
 
Here’s how to decide between stumpage- or delivered price harvest scheduling ...
Here’s how to decide between stumpage- or delivered price harvest scheduling ...Here’s how to decide between stumpage- or delivered price harvest scheduling ...
Here’s how to decide between stumpage- or delivered price harvest scheduling ...KR Walters Consulting Services
 
Subdivision of large uniform stands lacking natural bounding features
Subdivision of large uniform stands lacking natural bounding featuresSubdivision of large uniform stands lacking natural bounding features
Subdivision of large uniform stands lacking natural bounding featuresKR Walters Consulting Services
 
Design and development of a generalized forest management modeling system: Wo...
Design and development of a generalized forest management modeling system: Wo...Design and development of a generalized forest management modeling system: Wo...
Design and development of a generalized forest management modeling system: Wo...KR Walters Consulting Services
 
Spatial forest planning on industrial land: A problem in combinatorial optimi...
Spatial forest planning on industrial land: A problem in combinatorial optimi...Spatial forest planning on industrial land: A problem in combinatorial optimi...
Spatial forest planning on industrial land: A problem in combinatorial optimi...KR Walters Consulting Services
 
An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...
An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...
An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...KR Walters Consulting Services
 
Management of timber under a habitat conservation plan (HCP) in the Pacific N...
Management of timber under a habitat conservation plan (HCP) in the Pacific N...Management of timber under a habitat conservation plan (HCP) in the Pacific N...
Management of timber under a habitat conservation plan (HCP) in the Pacific N...KR Walters Consulting Services
 
A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...
A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...
A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...KR Walters Consulting Services
 
Defining adjacency and proximity of forest stands for harvest blocking
Defining adjacency and proximity of forest stands for harvest blockingDefining adjacency and proximity of forest stands for harvest blocking
Defining adjacency and proximity of forest stands for harvest blockingKR Walters Consulting Services
 
Barber Revisited: Aggregate Analysis in Harvest Schedule Models
Barber Revisited: Aggregate Analysis in Harvest Schedule ModelsBarber Revisited: Aggregate Analysis in Harvest Schedule Models
Barber Revisited: Aggregate Analysis in Harvest Schedule ModelsKR Walters Consulting Services
 
Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...
Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...
Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...KR Walters Consulting Services
 
Management of Timber Under A Habitat Conservation Plan in the Pacific Northwest
Management of Timber Under A Habitat Conservation Plan in the Pacific NorthwestManagement of Timber Under A Habitat Conservation Plan in the Pacific Northwest
Management of Timber Under A Habitat Conservation Plan in the Pacific NorthwestKR Walters Consulting Services
 

Mais de KR Walters Consulting Services (18)

SSAFR_2013_Walters_KR
SSAFR_2013_Walters_KRSSAFR_2013_Walters_KR
SSAFR_2013_Walters_KR
 
Operationalizing Analytics in Forestry
Operationalizing Analytics in ForestryOperationalizing Analytics in Forestry
Operationalizing Analytics in Forestry
 
SSAFR_2015_WaltersKR
SSAFR_2015_WaltersKRSSAFR_2015_WaltersKR
SSAFR_2015_WaltersKR
 
Barber Revisited: Aggregate Analysis in Harvest Scheduling Models
Barber Revisited: Aggregate Analysis in Harvest Scheduling ModelsBarber Revisited: Aggregate Analysis in Harvest Scheduling Models
Barber Revisited: Aggregate Analysis in Harvest Scheduling Models
 
Here’s how to decide between stumpage- or delivered price harvest scheduling ...
Here’s how to decide between stumpage- or delivered price harvest scheduling ...Here’s how to decide between stumpage- or delivered price harvest scheduling ...
Here’s how to decide between stumpage- or delivered price harvest scheduling ...
 
Subdivision of large uniform stands lacking natural bounding features
Subdivision of large uniform stands lacking natural bounding featuresSubdivision of large uniform stands lacking natural bounding features
Subdivision of large uniform stands lacking natural bounding features
 
Design and development of a generalized forest management modeling system: Wo...
Design and development of a generalized forest management modeling system: Wo...Design and development of a generalized forest management modeling system: Wo...
Design and development of a generalized forest management modeling system: Wo...
 
Spatial forest planning on industrial land: A problem in combinatorial optimi...
Spatial forest planning on industrial land: A problem in combinatorial optimi...Spatial forest planning on industrial land: A problem in combinatorial optimi...
Spatial forest planning on industrial land: A problem in combinatorial optimi...
 
An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...
An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...
An Empirical Evaluation of Spatial Restrictions in Industrial Harvest Schedul...
 
Management of timber under a habitat conservation plan (HCP) in the Pacific N...
Management of timber under a habitat conservation plan (HCP) in the Pacific N...Management of timber under a habitat conservation plan (HCP) in the Pacific N...
Management of timber under a habitat conservation plan (HCP) in the Pacific N...
 
A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...
A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...
A Comparative Study Of Analytical Tools For Strategic & Tactical Forest Manag...
 
Defining adjacency and proximity of forest stands for harvest blocking
Defining adjacency and proximity of forest stands for harvest blockingDefining adjacency and proximity of forest stands for harvest blocking
Defining adjacency and proximity of forest stands for harvest blocking
 
A system for solving spatial forest planning problems
A system for solving spatial forest planning problemsA system for solving spatial forest planning problems
A system for solving spatial forest planning problems
 
Harvest Scheduling and Policy Analysis
Harvest Scheduling and Policy AnalysisHarvest Scheduling and Policy Analysis
Harvest Scheduling and Policy Analysis
 
Barber Revisited: Aggregate Analysis in Harvest Schedule Models
Barber Revisited: Aggregate Analysis in Harvest Schedule ModelsBarber Revisited: Aggregate Analysis in Harvest Schedule Models
Barber Revisited: Aggregate Analysis in Harvest Schedule Models
 
National Forest Planning and NFMA Requirements
National Forest Planning and NFMA RequirementsNational Forest Planning and NFMA Requirements
National Forest Planning and NFMA Requirements
 
Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...
Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...
Forest Structure & Spatial Restrictions: Interactions & How They Affect Harve...
 
Management of Timber Under A Habitat Conservation Plan in the Pacific Northwest
Management of Timber Under A Habitat Conservation Plan in the Pacific NorthwestManagement of Timber Under A Habitat Conservation Plan in the Pacific Northwest
Management of Timber Under A Habitat Conservation Plan in the Pacific Northwest
 

Error Propagation in Forest Planning Models

  • 1. Error Propagation in Forest Planning Models Karl R. Walters and Sean J. Canavan
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. Normal (0,1) PDF x 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 -5 -4 -3 -2 -1 0 1 2 3 4 5 f(x)
  • 7. Normal (O,1) CDF x 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -5 -4 -3 -2 -1 0 1 2 3 4 5 F(x) = P(X < x)
  • 8.
  • 9.
  • 10. Normal (O,1) CDF x 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 -5 -4 -3 -2 -1 0 1 2 3 4 5 F(x) = P(X < x)
  • 11. Fitted CDF Equation 0.00 0.20 0.40 0.60 0.80 1.00 -1.5 -1 -0.5 0 0.5 1 1.5 Error Size Cumulative Probability  Pr(  = 0)  Pr(  < 0)  Pr(  > 0) P(X = x ) = Pr(  < 0)*Negative Error CDF  < 0 Pr(  < 0) + Pr(  = 0)  = 0 Pr(  < 0) + Pr(  = 0) + Pr(  > 0)*Positive Error CDF  > 0
  • 12. Fits of Error Type Probabilities 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0.5 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 Dbh (inches) Probability P(e > 0) P(e = 0) P(e < 0)
  • 13. Empirical Dbh Error CDF Surface Cumulative Probability Dbh (inches) Error (inches) 0.25 0.50 0.75 1.00 0.0 20.0 30.0 40.0 -0.6 0.0 0.6 1.2 1.8 10.0
  • 14. Fitted Dbh CDF Surface
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21. Unconstrained 4,838,760 5,142,948 4,925,347 5,180,696 Total Harvest 33,076 32,206 33,355 28,352 HW saw logs 4,403 4,275 4,428 4,333 RC saw logs 18,073 16,731 47,399 16,736 WW oversize logs 591,469 613,119 615,886 606,089 WW saw logs 224,367 220,707 232,815 221,610 DF oversize logs 3,467,413 3,756,908 3,480,189 3,795,724 DF saw logs 499,959 499,000 511,276 507,851 DF export logs Dbh_Ht Err Ht_Err Dbh_Err Clean 50 Year Summary
  • 22.
  • 23. Unconstrained 36,766 36,144 36,423 35,213 commercial thin 49 19 49 - precomm thin 537tpa 36 - 19 266 precomm thin 436tpa - - - - precomm thin 360tpa - - - - precomm thin 300tpa 18,142 18,142 18,142 17,039 slashing - - - - planted NF 680tpa 5,656 5,656 5,656 5,108 planted NF 550tpa 19,381 20,504 19,381 30,342 planted DF 550tpa 30,912 29,820 30,850 19,586 planted DF 450tpa 55,948 55,980 55,886 55,036 clearcut Dbh_Ht Err Ht_Err Dbh_Err Clean 50 Year Summary
  • 24.
  • 25.
  • 26.
  • 27. Nondeclining Yield 3,498,616 3,624,749 3,561,097 3,654,132 Total Harvest 35,169 33,695 34,992 33,844 HW saw logs 5,815 5,767 6,181 6,099 RC saw logs 27,815 27,137 57,946 27,880 WW oversize logs 798,669 803,591 819,808 824,113 WW saw logs 351,540 316,824 352,489 322,816 DF oversize logs 1,636,676 1,764,109 1,631,819 1,761,552 DF saw logs 642,932 673,628 657,862 677,828 DF export logs Dbh_Ht Err Ht_Err Dbh_Err Clean 50 Year Summary
  • 28.
  • 29. Nondeclining Yield 22,493 21,884 21,600 21,368 commercial thin 228 19 1941 - precomm thin 537tpa 537 - 367 266 precomm thin 436tpa 161 256 - 716 precomm thin 360tpa 74 56 105 56 precomm thin 300tpa 18,142 17,094 17,145 17,094 slashing - - - - planted NF 680tpa 5,019 5,019 5,019 5,019 planted NF 550tpa 18,704 21,878 16,636 20,809 planted DF 550tpa 21,085 17,931 23,117 18,978 planted DF 450tpa 44,808 44,828 44,772 44,806 clearcut Dbh_Ht Err Ht_Err Dbh_Err Clean 50 Year Summary
  • 30.
  • 31.
  • 32.
  • 33.
  • 34. Thank you. Any questions?