Effects of measurement errors on individual tree stem volume estimates for the Austrian national forest inventory

Forest Science

Volumn 60, Issue 1, 2014, Pages 14-24

  Berger, Ambros ^a   Gschwantner, Thomas ^a   McRoberts, Ronald E ^b   Schadauer, Klemens ^a  

^a FEDERAL RESEARCH AND TRAINING CENTRE FOR FORESTS   (Austria)

^b USDA Forest Service   (Austria)

Author keywords

Error propagation; Measurement error; Monte carlo error simulation; Stem volume; Uncertainty

Indexed keywords

AIRBORNE LASER SCANNING; ERROR PROPAGATION; ERROR SIMULATION; NATIONAL FOREST INVENTORIES; RELATIVE STANDARD DEVIATIONS; REMOTE SENSING APPLICATIONS; STEM VOLUME; UNCERTAINTY;

ESTIMATION; MEASUREMENT ERRORS; MONTE CARLO METHODS; UNCERTAINTY ANALYSIS;

FORESTRY;

AUSTRIA; ERRORS; FORESTRY; INVENTORIES; MEASUREMENT;

EID: 84893416818     PISSN: 0015749X     EISSN: None     Source Type: Journal    
DOI: 10.5849/forsci.12-164     Document Type: Article

References (44)

1. Altherr, E. 1960. Die Genauigkeit verschiedener Verfahren der Sektion-ierung in absoluter und relativer Schaftlänge. Allgem. Forst. Jagdzeit. 131:226-237.
2. Barker, J.R., M. Bollman, P.L. Ringold, J. Sackinger, and S.P. Cline. 2002. Evaluation of metric precision for a riparian forest survey. Environ. Monit. Assess. 75:51-72.
3. Berger, A., T. Gschwantner, K. Gabler, and K. Schadauer. 2012. Analysis of tree measurement errors in the Austrian National Forest Inventory. Aust. J. For. Sci. 129(3):149-177.
4. Braun, R. 1969. Österreichische Forstinventur, Methodik der Auswertung und Standardfehler-Berechnung. Mitteilungen der Forstlichen Bundes-versuchsanstalt Wien No. 84. Österreichischer Agrarverlag, Wien, Austria. 60 p.
5. Braun, R. 1974. The methodological development of Austrian forest inventories. In 100 Jahre Forstliche Bundesversuchsanstalt. Forstliche Bundesversuchsanstalt, Vienna, Austria. 359 p.
6. Breidenbach, J., C. AntÓn-FernÁndez, H. Petersson, R.E. McRob-erts, and R. Astrup. 2014. Quantifying the model-related variability of biomass stock and change estimates in the Norwegian National Forest Inventory. For. Sci. In press.
7. Canavan, S.J., and D.W. Hann. 2004. The two-stage method for measurement error characterization. For. Sci. 50:743-756.
8. Clark, A. III, R.A. Souter, and B.E. Schlaegel. 1991. Stem profile equations for southern tree species. USDA For. Serv., Res. Pap. SE-282, Asheville, NC. 113 p.
9. Cunia, T. 1965. Some theory on reliability of volume estimates in a forest inventory sample. For. Sci. 11:115-128.
10. Cunia, T. 1987. On the error of continuous forest inventory estimates. Can. J. For. Res. 17:436-441.
11. Elzinga, C., R.C. Shearer, and G. Elzinga. 2005. Observer variation in tree diameter measurements. West. J. Appl. For. 20:134-137.
12. Gabler, K., and K. Schadauer. 2008. Methods of the Austrian Forest Inventory 2000/02-Origins, approaches, design, sampling, data models, evaluation and calculation of standard error. BFW-Berichte, No. 142, Vienna, Austria. 121 p.
13. Gertner, G.Z. 1990. The sensitivity of measurement error in stand volume estimation. Can. J. For. Res. 20:800-804.
14. Gertner, G., and M. Köhl. 1992. An assessment of some nonsampling errors in a national survey using an error budget. For. Sci. 38:525-538.
15. GNU Octave. (2012). GNU Octave 3.2.4. Available online at www.gnu.org/software/octave; last accessed July 18, 2011.
16. Grosenbaugh, L.R. 1967. Tree form: Definition, Interpolation, Extrapolation. In Wood measurement, conference proceedings, Buckingham, F. (ed.). University of Toronto Faculty of Forestry, Toronto, ON, Canada. 222 p.
17. Hollaus, M., W. Dorigo, W. Wagner, K. Schadauer, B. HÖfle, and B. Maier. 2009. Operational wide-area stem volume estimation based on airborne laser scanning and national forest inventory data. Int. J. Remote Sens. 30(19):5159-5175.
18. Holmgren, J. 2004. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scand. J. For. Res. 19:543-553.
19. Hosmer, D.W., and S. Lemeshow. 1989. Applied logistic regression. John Wiley & Sons, New York. 307 p.
20. Intergovernmental Panel on Climate Change. 2006. 2006 IPCC guidelines for national greenhouse gas inventories, prepared by the National Greenhouse Gas Inventories Programme, Eggleston, H.S., L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.). Institute for Global Environmental Strategies, Japan.
21. Kalliovirta, J., J. Laasasenaho, and A. Kangas. 2004. Evaluation of the laser-relascope. For. Ecol. Manage. 204:181-194.
22. Kangas, A. 1996. On the bias and variance in tree volume predictions due to model and measurement errors. Scand. J. For. Res. 11:281-290.
23. Kaufmann, E., and A. Schwyzer. 2001. Control survey of the terrestrial inventory. In Swiss National Forest Inventory: Methods and models of the second assessment, Brassel, P., and H. Lischke. (eds.). Swiss Federal Research Institute WSL, Birmensdorf, Switzerland. 336 p.
24. Kitahara, F., N. Mizoue, and S. Yoshida. 2009. Evaluation of data quality in Japanese National Forest Inventory. Environ. Monit. Assess. 159:331-340.
25. Li, R., and A.R. Weiskittel. 2010. Comparison of model forms for estimating stem taper and volume in the primary conifer species of the North American Acadian Region. Ann. For. Sci. 67:302.
26. McRoberts, R.E., J.T. Hahn, G.J. Hefty, and J.R. Van Cleve. 1994. Variation in forest inventory field measurements. Can. J. For. Res. 24:1766-1770.
27. McRoberts, R.E., and J. Westfall. 2014. The effects of uncertainty in model predictions of individual tree volume on large area volume estimates. For. Sci. In press.
28. Mowrer, H.T., and W.E. Frayer. 1986. Variance propagationingrowth and yield projections. Can. J. For. Res. 16:1196-1200.
29. Næsset, E. 2007. Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia. Scand. J. For. Res. 22:433-442.
30. ÖzÇelik, R., M.J. Diamantopoulou, J.R. Brooks, and H.V. Wiant Jr. 2010. Estimating tree bole volume using artificial neural network models for four species in Turkey. J. Environ. Manage. 91:743-753.
31. Phillips, D.L., S.L. Brown, P.E. Schroeder, and R.A. Birdsey. 2000. Toward error analysis of large-scale forest carbon budgets. Global Ecol. Biogeogr. 9:305-313.
32. PollanschÜtz, J. 1974. Formzahlfunktionen der Hauptbaumarten Ö s-terreichs. Allgem. Forstzeit. 85:341-343.
33. Schieler, K. 1988. Methodische Fragen in Zusammenhang mit der Ö ster-reichischen Forstinventur. Diploma thesis, University of Natural Resources and Life Sciences, Vienna, Austria. 99 p.
34. Schmid, P., P. Roiki-Jokela, P. Mingard, and M. Zobeiry. 1971. The optimal determination of the volume of standing trees. P. 33-54 in Beiträge zur Zuwachsforschung, 15th IUFRO World Congress. Ö ster-reichische Agrarverlag, Vienna, Austria.
35. Schmid-Haas, P., and K. Winzeler. 1981. Efficient determination of volume and volume growth. P. 231-257 in Proc. of Forest Resource Inventory, Growth Models, Management Planning, and Remote Sensing, 17th IUFRO World Congress. International Union of Forestry Research Organizations, Niigata, Japan.
36. Skovsgaard, J.P., V.K. Johannsen, and J.K. Vanclay. 1998. Accuracy and precision of two laser dendrometers. Forestry 71:131-139.
37. Ståhl, G. 1992. A study on the quality of compartment-wise forest data acquired by subjective inventory methods. Dept. of Forest Mensuration and Management, Rep. 24, Swedish University of Agricultural Sciences, Umeå, Sweden. (In Swedish with English summary.)
38. Ståhl, G., J. Heikkinen, H. Petersson, J. Repola, and S. Holm. 2014. Sample based estimation of greenhouse gas emissions from forests-A new approach to account for both sampling and model errors. For. Sci. In press.
39. Taylor, J.R. 1997. An introduction to error analysis: The study of uncertainties in physical measurements. University Science Books, Sausalito, CA. 327 p.
40. Tomppo, E., T. Gschwantner, M. Lawrence, and R.E. McRoberts (eds.) 2010. National forest inventories: Pathways for common reporting. Springer, Berlin, Germany. 612 p.
41. Westfall, J.A., and P.L. Patterson. 2007. Measurement variability error for estimates of volume change. Can. J. For. Res. 37:2201-2210.
42. Williams, M.S., K.L. Cormier, R.G. Briggs, and D.L. Martinez. 1999. Evaluation of the Barr & Stroud FP15 and Criterion 400 laser dendrometers for measuring upper stem diameters and heights. For. Sci. 45:53-61.
43. Williams, M.S., and H.T. Schreuder. 2000. Guidelines for choosing volume equations in the presence of measurement error in height. Can. J. For. Res. 30:306-310.
44. Wood, G.B., H.V. Wiant Jr., R.J. Loy, and J.A. Miles. 1990. Centroid sampling: A variant of importance sampling for estimating the volume of sample trees of radiata pine. For. Ecol. Manage. 36:233-243.