1 / 35

Evaluating Crown Ratio Estimations for Tree Diameter Growth Predictions

This study evaluates the usefulness of crown ratio estimations on predicting diameter growth and stand basal area increment using the Forest Vegetation Simulator. The differences between measured and predicted crown ratios are quantified, and the effects of using measured crown ratios on diameter growth predictions are assessed. The study also examines the effect of using measured crown ratios on predictions of basal area increment at the plot level.

bettyrbrown
Download Presentation

Evaluating Crown Ratio Estimations for Tree Diameter Growth Predictions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. + b7DBH + b8DBH2 + b4 CCF + b5ln(CFF) + b9ln(DBH) + b10HT + b11HT2 + b12PCT + b14ln(PCT) ln(CR) = HAB + b1BA + b2BA2 + b3ln(BA) Should tree crown ratio be measured to obtain reliable tree diameter growth predictions? byLaura Leites, Nicholas Crookston, Andrew Robinson

  2. + b7DBH + b8DBH2 + b4 CCF + b5ln(CFF) + b9ln(DBH) + b10HT + b11HT2 + b12PCT + b14ln(PCT) ln(CR) = HAB + b1BA + b2BA2 + b3ln(BA) An evaluation of the utility of crown ratio estimations on the predictions of diameter growth and stand basal area increment for the Forest Vegetation Simulator, North Idaho (NI) and South Central Oregon/North Eastern California(SO) variants.

  3. Objectives • We evaluate the CR models used in two major variants of FVS: NI and SO, and quantify the differences between measured (CRm) and FVS predicted CR (CRp). • We evaluate the effect of using CRm against using CRp on the diameter growth (DG) predictions at the tree level.

  4. Objectives 3. We evaluate the effect of using CRm against using CRp at the plot level through predictions of basal area increment (BAI).

  5. Introduction • CR and diameter growth (DG) predictions: • Indirect measure of the tree’s photosynthetic capacity & a measure of stand density. • As the CR increases so does the DG rate.

  6. Introduction • CR and diameter growth (DG) predictions: • FGYM DG models: CR as a predictor variable. • The FVS 10-year squared basal diameter increment model (dds). • CR: measured and predicted

  7. Introduction On CR models: • CR at a point in time vs. change in CR. • Mathematical forms for allometric CR models: • exponential, logistic, Weibull distribution based models, Richards. • Predictor variables - 3 groups: • Tree size, competition level, stand productivity.

  8. FVS NI and SO Variants: CR predictions at a point in time. NI variant: Hatch (1980) exponential model

  9. SO variant: • Small trees use a logistic model:

  10. Large trees use Dixon’s (1985) Weibull based model. Specify Stand CR distribution: a & c: species-specific constants b for a given species: Calculate mean stand CR (MCR) from relative stand density index (RSDI): MCR = d0 + d1*RSDI (d0 and d1 are species-specific) Use MCR to calculate b: b = j0 + j1*MCR ( j0 and j1 are species-specific) Assign CR values based on tree’s DBH ranking

  11. CNF WNF Methods Data • Acquired from the USDA Forest Service, Pacific Northwest Region's Current Vegetation Survey (CVS) project. • Data collected at the Winema National Forest (WNF) and the Colville National Forest (CNF) in 1993-1996.

  12. Methods Data • Sampling design: five 0.076 ha subplots within 1 ha main plot. Different grid sizes. • The CNF comprised 2,611 0.076 ha subplots, used as our simulation units. • The WNF comprised 2,426 0.076 ha simulation units.

  13. Measurements in each simulation unit: • Species • DBH (in) • Total tree height (HT, ft ) • 10-yr radial growth (cores) • CR (measured in 10%-wide classes) • Crown width (ft) • Age (rings count), crown class, damages/injuries, and defects. • Variables used to FVS runs were in English units. • Simulations results were converted to Metric units.

  14. Colville National Forest

  15. Winema National Forest

  16. Methods Analysis Step 1. Evaluation of CR predictions. By species and by CRm classes.

  17. Methods • Step 2. Assessment of the effect of using CRm against using CRp on the DG predictions at the tree level. • We ran FVS NI and SO variants twice, once using CRm and once using CRp. • All the rest of the variables were the same in both runs. • FVS was ran using default mode.

  18. HT growth driven DG small trees (ST) DG dds prediction DBH large trees (LT) dds prediction = DG Methods • The FVS DG at tree-level:

  19. Methods FVS dds base model: SO variant incorporates other predictor variables.

  20. Methods Predicted 10-year-period tree-level DG: with CRm (DGmCR) & with CRp (DGpCR) • RMSE by CRm classes and species. • Equivalence tests: • non-parametric bootstrap procedure by Robinson et al. (2005). •  = 0.05, region of similarity for slope and intercept were set equal to  10% of the mean.

  21. Methods • Step 3. Assessment of the effect of using CRm against using CRp on the BAI at the simulation unit (SU) level • We ran FVS NI and SO variant models twice for a 30-year-period. • All the rest of the variables were the same in both runs. • FVS was ran using default mode.

  22. Methods BAImCR v.s. BAIpCR • Equivalence tests: • non-parametric bootstrap procedure by Robinson et al. (2005). •  = 0.05, region of similarity for slope and intercept were set equal to  10% of the mean.

  23. Results Step 1. Evaluation of CR predictions.

  24. Results Step 1. Evaluation of CR predictions.

  25. % of DGpCR Results Step 2. DGmCR v.s.DGpCR ST = small trees LT = large trees

  26. Results Step 2. DGmCR v.s.DGpCR

  27. Step 3. BAImCR v.s. BAIpCR

  28. Step 3. BAImCR v.s. BAIpCR

  29. Conclusions • The three CR equations were biased. • The larger the difference between RMSE of CRm and CRp, the larger the difference between RMSE of DGpCR and DGmCR. • Overall RMSE values for the NI variant were lower than those for the SO variant.

  30. Conclusions • Equivalence tests resulted in similarity for more species in the NI variant than in the SO variant. • Equivalence tests of BAImCR v.s. BAIpCR resulted in similarity for intercept and slope for both variants.

  31. + b7DBH + b8DBH2 + b4 CCF + b5ln(CFF) + b9ln(DBH) + b10HT + b11HT2 + b12PCT + b14ln(PCT) ln(CR) = HAB + b1BA + b2BA2 + b3ln(BA) Literature Dixon, G.E. 1985. Crown ratio modeling using stand density index and the Weibull distribution. Internal Report. Fort Collins, CO: USDA Forest Service. Forest Management Service Center. 13p. Hatch, C.R. 1980. Modelling crown size using inventory data. Mitt.Forstl. Bundes- Versuchsanst. Wien, 130: 93-97. Robinson, A.P., Duursma, R.A., and Marshall, J.D. 2005. A regression-based equivalence test for model validation: shifting the burden of proof. Tree Physiology. 25:903-913.

  32. + b7DBH + b8DBH2 + b4 CCF + b5ln(CFF) + b9ln(DBH) + b10HT + b11HT2 + b12PCT + b14ln(PCT) ln(CR) = HAB + b1BA + b2BA2 + b3ln(BA) • Acknowledgements: • Gary E. Dixon • Charles R. Hatch • This study was funded by USFS Grant 04DG11010000037

  33. + b7DBH + b8DBH2 + b4 CCF + b5ln(CFF) + b9ln(DBH) + b10HT + b11HT2 + b12PCT + b14ln(PCT) ln(CR) = HAB + b1BA + b2BA2 + b3ln(BA) Thank you Questions?

  34. Colville National Forest Mean DGp mean DGp for CRm class = 40-60%

  35. Winema National Forest ST: Mean DGp mean DGp for CRm class = 40-60% BT: LP mean CRm= 61, mean CRp= 68

More Related