List of red lines

DRAFT

1. 4.1 \(\looparrowright\) Red line dataset

2. 5.1 \(\looparrowright\) Exploring the biomass–dbh relation

3. 5.2 \(\looparrowright\) Exploring the biomass–\(D^2H\) relation

4. 5.3 \(\looparrowright\) Conditioning on wood density

5. 5.4 \(\looparrowright\) Exploring the biomass–dbh relation: variables transformation

6. 5.5 \(\looparrowright\) Exploring the biomass–\(D^2H\) relation: variables transformation

7. 6.1 \(\looparrowright\) Simple linear regression between \(\ln(B)\) and \(\ln(D)\)

8. 6.2 \(\looparrowright\) Simple linear regression between \(\ln(B)\) and \(\ln(D^2H)\)

9. 6.3 \(\looparrowright\) Polynomial regression between \(\ln(B)\) and \(\ln(D)\)

10. 6.4 \(\looparrowright\) Multiple regression between \(\ln(B)\), \(\ln(D)\) and \(\ln(H)\)

11. 6.5 \(\looparrowright\) Weighted linear regression between \(B\) and \(D^2H\)

12. 6.6 \(\looparrowright\) Weighted polynomial regression between \(B\) and \(D\)

13. 6.8 \(\looparrowright\) Polynomial regression between \(B\) and \(D\) with variance model

14. 6.9 \(\looparrowright\) Linear regression between \(B/D^2\) and \(H\)

15. 6.17 \(\looparrowright\) Non-linear regression between \(B\) and a polynomial of \(\ln(D)\)

16. 6.10 \(\looparrowright\) Linear regression between \(B/D^2\) and \(1/D\)

17. 6.11 \(\looparrowright\) Weighted non-linear regression between \(B\) and \(D\)

18. 6.12 \(\looparrowright\) Weighted non-linear regression between \(B\) and \(D^2H\)

19. 6.13 \(\looparrowright\) Weighted non-linear regression between \(B\), \(D\) and \(H\)

20. 6.14 \(\looparrowright\) Non-linear regression between \(B\) and \(D\) with variance model

21. 6.15 \(\looparrowright\) Non-linear regression between \(B\) and \(D^2H\) with variance model

22. 6.16 \(\looparrowright\) Non-linear regression between \(B\), \(D\) and \(H\) with variance model

23. 6.18 \(\looparrowright\) Selecting variables

24. 6.19 \(\looparrowright\) Testing nested models: \(\ln(D)\)

25. 6.20 \(\looparrowright\) Testing nested models: \(\ln(H)\)

26. 6.21 \(\looparrowright\) Selecting models with \(B\) as response variable

27. 6.22 \(\looparrowright\) Selecting models with \(\ln(B)\) as response variable

28. 6.23 \(\looparrowright\) Power model fitting methods

29. 6.24 \(\looparrowright\) Specific biomass model

30. 6.25 \(\looparrowright\) Specific wood density-dependent biomass model

31. 6.26 \(\looparrowright\) Individual wood density-dependent biomass model

32. 7.1 \(\looparrowright\) Confidence interval of \(\ln(B)\) predicted by \(\ln(D)\)

33. 7.2 \(\looparrowright\) Confidence interval of \(\ln(B)\) predicted by \(\ln(D)\) and \(\ln(H)\)

34. 7.3 \(\looparrowright\) Correction factor for predicted biomass

35. 7.4 \(\looparrowright\) “Smearing” estimation of biomass