Above-ground biomass equations for Pinus radiata D. Don in Asturias

  1. Canga, E.
  2. Dieguez Aranda, I.
  3. Afif Khouri, Elías
  4. Cámara Obregón, Asunción
Revue:
Forest systems

ISSN: 2171-5068

Année de publication: 2013

Volumen: 22

Número: 3

Pages: 408-415

Type: Article

DOI: 10.5424/FS/2013223-04143 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

D'autres publications dans: Forest systems

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Références bibliographiques

  • Balboa-Murias M, Rodríguez-Soalleiro R, Merino A, Álvarez-González JG, 2006. Temporal variations and distribution of carbon stocks in aboveground of radiata pine and maritime pine pure stands under different silvicultural alternatives. For Ecol Manage 237: 29-38.
  • Belsey DA, 1991. Conditioning diagnostics, collinearity and weak data in regresión. Wiley, New York, USA. 396 pp.
  • Borders BE, 1989. System of equations in forest stand modelling. For Sci 35: 548-556.
  • Breusch T, Pagan A, 1979. A simple test for heteroscedasticity and random coefficient variation. Econometrica 47: 1287-1294. http://dx.doi.org/10.2307/1911963
  • Burger H, 1945. Holz, Blattmenge und Zuwachs. VII: Die Lärche. In: Forest mensuration (Van Laar A, Akça A, eds, 1997). Cuvillier, Göttingen, Germany. Mitt Schw Anst fd Forstl Versw 24: 7-103.
  • Burger H, 1953. Holz, Blattmenge und Zuwachs. XIII: Fichten im gleichaltrigen Hochwald (Van Laar A, Akça A, eds, 1997). Mitt Schw Anst Forstl Versuchsw 29: 38-130.
  • Carvalho JP, Parresol BR, 2003. Additivity in tree components biomass of Pyrenean oak (Quercus pyrenaica Willd.). For Ecol Manage 179: 269-273.
  • Chiyenda SS, Kozak A, 1984. Additivity of component biomass regression equations when the underlying model is linear. Can J For Res 14: 441-446. http://dx.doi.org/10.1139/x84-078
  • Cunia T, 1986. Construction of tree biomass tables by linear regression techniques. In: Estimating tree biomass regressions and their error. Proceedings of the Workshop on tree biomass regression functions and their contribution to the error of forest inventory estimates. 26-30 May. Syracuse, New York, USA. pp: 27-37.
  • Cunia T, 1988. On the error of biomass regressions and the corresponding inventory estimates. In: Proceedings of the 9th Annual Southern Forest Biomass. Workshop (Daniels, RA, Watson WFa and Savelle IW, eds). Mississippi State Univ, Mississippi State, USA. pp: 93-109.
  • Cunia T, Briggs RD, 1984. Forcing additivity of biomass tables: some empirical results. Can J For Res 14: 376-384. http://dx.doi.org/10.1139/x84-067
  • Cunia T, Briggs RD, 1985. Forcing additivity of biomass tables: use of the generalizad least squares method. Can J For Res 15: 23-28. http://dx.doi.org/10.1139/x85-006
  • Diéguez-Aranda U, Barrio M, Castedo F. Ruiz AD, Álvarez- González MF, Álvarez-González JG, Rojo A, 2003. Dendrometría. Mundi-Prensa, Fundación Conde del Valle de Salazar, Madrid, Spain. 327 pp.
  • Drake JB, Dubayah RO, Knox RG, Clark, DB, Blair JB, 2002. Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sensing of Environment 81: 378-392. http://dx.doi.org/10.1016/S0034-4257(02)00013-5
  • Evert F, 1985. Systems for equations for estimating ovendry mass of 18 Canadian tree species. Can For Serv Petawawa Nat For Inst Information Report. PI-X-59.
  • Guerra J, Ganoso J, Schlatter V, Nespolo R, 2005. Análisis de la biomasa de raíces en diferentes tipos de bosques. Avances en la evaluación de Pinus radiata en Chile. Bosque 26(1): 5-21.
  • Hartley HO, 1961. The modified Gauss-Newton method for the f itting of nonlinear regression functions by least squares. Technometrics 3: 269-280. http://dx.doi.org/10.1080/00401706.1961.10489945
  • Harvey AC, 1976. Estimating regression models with multiplicative heteroscedasticity. Econometrica 44: 461- 465.
  • IPCC National Greenhouse Gas Inventories Programme (http://www.ipcc-nggip.iges.or.jp/ public/gpglulucf/gpglulucf.htm), Japan.
  • Kittredge J, 1944. Estimation of the amount of foliage of trees and stands. J of Forestry 42: 905-912.
  • Kozak A, 1970. Methods of ensuring additivity of biomass components by regression analysis. For Chron 46(5): 402-404.
  • Madgwick HAI, 1983. Estimation of the oven-dry weight of stems, needles, and branches of individual Pinus radiata trees. New Zealand Journal of Forestry Sciencen 13(1): 108-109.
  • Merino A, Rodríguez A, Brañas J, Rodríguez-Soalleiro R, 2003. Nutrition and growth in newly established plantations of Eucalyptus globulus in Northwest Spain. Ann For Sci 60: 509-517. http://dx.doi.org/10.1051/forest:2003044
  • Montero G, Ruiz-Peinado R, Muñoz M, 2005. Producción de biomasa y fijación de CO2 por los bosques espa-oles. Monografías INIA: Serie Forestal nº 13.
  • Montes N, Gauquelin T, Badri W, Bertaudiere V, Zaoui E, 2000. A non-destructive method for estimating aboveground forest biomass in threatened woodlands. For Ecol Manage 130: 37-46.
  • Moore JR, 2010. Allometric equations to predict the total above-ground biomass of radiata pine trees. Ann For Sci 67(8): 806. http://dx.doi.org/10.1051/forest/2010042
  • Myers RH, 1990. Classical and modern regression with applications, 2nd. Duxbury Press, Belmont, CA. 488 pp.
  • Neter J, Kutner MH, Nachtsheim J, Wasserman W, 1996. Applied linear statistical models, 4th ed. McGraw-Hill, New York. 1408 pp.
  • Ovington JD, 1957. Dry matter production of Pinus sylvestris. Ann Bot 21: 287-314.
  • Pardé JD, 1980. Forest Biomass. For Abstr 41(8): 343-362.
  • Parresol BR, 1999. Assessing tree and stand biomass: a review with examples and critical comparisons. For Sci 45: 573-593.
  • Parresol BR, 2001. Additivity of nonlinear biomass equations. Can J For Res 31: 865-878. http://dx.doi.org/10.1139/x00-202
  • Paulo JA, Tomé J, Tomé M, 2002. Ajustamento simultâneo de equações de biomassa de azinheira. En: Actas do X Congresso da Sociedades Portuguesa de Estadística. Porto, Portugal.
  • Reed DD, Green EJ, 1985. A method of forcing additivity of biomass tables when using nonlinear models. Can J For Res 15: 1184-1187. http://dx.doi.org/10.1139/x85-193
  • Reed DD, Liechty HO, Jones EA, Zhang Y, 1996. Above and belowground dry matter accumulation pattern derived from dimensional biomass relationships. For Sci 42: 236-241.
  • Rey C, Bra-as J, Rodríguez-Soalleiro R, Merino A, 2001. Biomasa y acumulación de nutrientes en plantaciones de Pinus radiata D. Don del norte de Espa-a. Actas del III Congreso Forestal Espa-ol, Tomo I, pp: 500- 504.
  • SAS Institute Inc, 2004a. SAS/STAT®. 9.1. User's Guide. SAS Institute Inc, Cary, NC.
  • SAS Institute Inc, 2004b. SAS/ETS®. 9.1. User's Guide. SAS Institute Inc, Cary, NC.
  • Schlaegel BE, 1982. Acer negundo biomass component regression analysis for the Mississipi Delta. For Sci 28: 355-358.
  • Snowdon P, 1985. Alternative sampling strategies and regression models for estimating forest biomass. Aust For Res 15: 353-366.
  • Wang C, 2006. Biomass allometric equations for 10 cooccurring tree species in Chinese temperate forest. For Ecol Manage 222: 9-16.
  • Waring RH, Runnig SW, 1998. Forest ecosystems: analysis at multiple scales, 2ed. Academic Press, San Diego, CA, USA. 370 pp.
  • White H, 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48(4): 817-838. http://dx.doi.org/10.2307/1912934
  • Zeide B, 1987. Areas of biomass research. In: Estimating tree biomass regressions and their error (Wharton EH, Cunia T (comps). Prof of the Workshop on Tree biomass regression functions and their contribution to the error of forest inventory estimates. USDA FOR. SERV. Gen Tech Rep. pp: 193-196.
  • Zellner A, 1962. An efficient method of estimating seemingly unrelated regressions and test for aggregation bias. J Am Stat Assoc 57: 348-368. http://dx.doi.org/10.1080/01621459.1962.10480664
  • Zianis D, Muukkonen P, Mäkipää R, Mencuccini M, 2005. Biomass and stem volume equations for tree species in Europe. Silva Fennica, Monograps 4.