Component Biomass Equations for the Principal Conifer Species of the Inland Northwest

Transcription

1 Motivations Sampling Strategy Modeling framework Results Component Biomass Equations for the Principal Conifer Species of the Inland Northwest David Affleck Department of Forest Management University of Montana Inland Northwest Growth & Yield Cooperative 2016 Technical Meeting Next steps

2 Summary 1 Motivations demand for biomass/carbon estimates, lack of regional data 2 Sampling strategy data distribution & assimilation 3 Analysis questions modeling aims & challenges 4 Preliminary results nonlinear multivariate models 5 Next steps reporting & further data collection

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4 61 sites across the inland northwest

5 Felled tree DBH distribution (through 2014) Species n mean min max st dev ABGR ABLA LAOC PIEN PICO PIPO PSME

6 120 LAOC PSME 100 Height (ft) DBH (in)

7 LAOC PSME 80 Crown ratio (%) DBH (in)

8 Stem biomass estimation PSME taper: DBH & height only Diameter (in) Height (ft)

9 Stem biomass estimation PSME taper: DBH, height, & 20 upperstem pairs Diameter (in) Height (ft)

10 Stem biomass estimation PSME taper: DBH, height, & 20 upperstem pairs Diameter (in) Height (ft) stem biomass = V ρ for V = estimated volume; ρ = published wood or bark density

11 Stem biomass estimation PSME taper: DBH, height, & 20 upperstem pairs Diameter (in) stem biomass = V ρ + height n discs Height (ft) [ ] disc dry mass i disc thickness i (pred. disc area i )ρ for V = estimated volume; ρ = published wood or bark density

12 Crown biomass estimation PSME: 444 branches from 65 trees Dry mass (lbs) branchwood r= foliage r= Branch basal area (in 2 ) Affleck & Gregoire (2015) Generalized and synthetic regression estimators for randomized branch sampling Forestry 88: 599

13 sites across the inland northwest

14 Felled trees + Brown s (1978) data Species trees crowns stems min DBH (in) max DBH (in) ABGR ABLA LAOC PIEN PICO PIPO PSME

15 Analysis questions 1 Are DBH-based biomass models adequate? 2 With additional predictors, how to address redundancy? 3 Can cross-correlations among components be exploited? 4 Differences with respect to existing estimators?

16 Analysis questions 1 Are DBH-based biomass models adequate? 2 With additional predictors, how to address redundancy? 3 Can cross-correlations among components be exploited? 4 Differences with respect to existing estimators? Modeling considerations: ˆ Nonlinear relationships ˆ Non-constant variance ˆ Correlated predictor variables ˆ Nested sampling design ˆ Incomplete data

17 PSME: 65 felled trees + 33 from Brown (1978) Whole tree dry mass (tons) crown n= stem n= Tree basal area x Height (ft 3 )

18 PSME: 65 felled trees + 33 from Brown (1978) Height (ft) r = Crown length (ft) r = Crown length (ft) r = DBH (in) DBH (in) Height (ft) Principal component Prop. var ln(dbh) ln(ht) ln(cl) 95.3% 0.27 ln(dbh) ln(ht) 0.85 ln(cl) 3.3% 0.69 ln(dbh) 0.71 ln(ht) 0.16 ln(cl) 1.3%

19 PSME: 65 felled trees + 33 from Brown (1978) stembark mass (ton) r = live branch mass (ton) r = stemwood mass (ton) foliage mass (ton)

20 PSME: 65 felled trees + 33 from Brown (1978) foliage live branches dead branches crown bark wood stem Sample trees Affleck & Diéguez-Aranda (2016) Additive Nonlinear Biomass Equations: A Likelihood-Based Approach Forest Science 62(2):129

21 Model formulation & selection Common form: expected biomass DBH b 1 HT b 2 CL b3

22 PSME: foliage biomass model residuals Crown ratio Pearson residual DBH DBH,HT,CL

23 PSME: stem mass model residuals Height (ft) Pearson residual DBH DBH,HT,CL

24 PSME: stem mass model forms Parameters Biomass AIC Rank 3 DBH 1.80 HT 1.37 CL DBH 1.62 HT 1.55 CL DBH 1.72 HT DBH 1.31 HT 0.98 CL DBH 1.76 HT DBH V 0.86 DBH 0.29 HT 0.28 C

25 2016 Assimilate 2015 felled tree data Complete model selection & calibration ˆ Fit component models jointly ˆ Evaluate spatial variation ˆ Contrast estimates against Jenkins, FIA, BC Report results ˆ submit to National Biomass Estimator Library

26 2016 Assimilate 2015 felled tree data Complete model selection & calibration ˆ Fit component models jointly ˆ Evaluate spatial variation ˆ Contrast estimates against Jenkins, FIA, BC Report results ˆ submit to National Biomass Estimator Library 2017 Collect additional data ˆ for aspen within inland northwest ˆ for range of species in southern ID, WY, UT, CO Develop & calibrate models at national level ˆ with partners at USFS, Oregon State, Michigan State, Virginia Tech, U. Maine, & U. Georgia

27 Motivations Sampling Strategy Modeling framework Acknowledgments Funding provided by Inland Northwest Growth & Yield Cooperative Spokane Tribe of Indians USDA Forest Service, Northern Region Joint Fire Science Program USDA Forest Service, Forest Inventory & Analysis Results Next steps