Abstract
This study maps maritime pine (Pinus pinaster Ait.) productivity in Portugal based on the data provided by the fifth National Forest Inventory (2005–2006). In Portugal, the usual procedure for measuring productivity uses the height and age data measured from dominant trees (the three trees with the largest diameter at breast height) in several sample areas (plots). To be able to compare measurements of different trees with different ages, empirical functions are fitted to the tree data, which enables the distribution of tree heights at a base-age of 50 years to be calculated. These reference heights are usually presented in five classes, which correspond to productivity classes. In a first step, a preliminary statistical analysis was conducted to evaluate possible relationships of the tree variables with measured contextual variables of the plots such as altitude, terrain slope, and terrain aspect. No unequivocal relationships were found for the studied variables. Secondly, maps of maritime pine productivity at unsampled plots were produced by Direct Sequential Simulation (DSS) of the height distribution of trees at a base-age of 50 years; a map of average 50-year-old tree height was then computed and transformed into classes. The set of simulated images also quantifies the local uncertainty, which identifies locations at which field sampling/survey should be performed in future forest inventory campaigns. The map image of productivity classes shows the best and worst areas in Portugal for maritime pine forestry and constitutes an effective, fundamental tool for the planning and management of maritime pine forests.
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References
AFN (2010) 5º Inventário Florestal Nacional. Relatório Final, 2005/06, Autoridade Florestal Nacional, Lisboa, 209 pp
Alves A (1988) Técnicas de produção florestal, 2ª edição, INIC, 331 pp
Almeida J, Santos E, Bio A (2004) Characterization of population and recovery of Iberian hare in Portugal through direct sequential co-simulation. In: Sanchez-Vila X, Carrera J, Gómez-Hernández JJ (eds) geoENV IV—geostatistics for environmental applications. Kluwer Academic Publishers, Dordrecht, p 127–138
Almeida J, Lopes M (2005) Stochastic simulation of rainfall using a space-time geostatistical algorithm. In: Demougeot-Renard H, Froidevaux R, Renard P (eds) Geostatistics for environmental applications. Springer, Dordrecht, pp 455–466
Almeida JA (2010) Modelling of cement raw material compositional indices with direct sequential cosimulation. Eng Geol 114(1–2):26–33
Benavides R, Roig S, Osoro K (2009) Potential productivity of forested areas based on a biophysical model. A case study of a mountainous region in northern Spain. Ann For Sci 66 (1):108
Bravo-Oviedo A, Río M, Montero G (2004) Site index curves and growth model for Mediterranean maritime pine (Pinus pinaster Ait.) in Spain. For Ecol Manag 201(2–3):187–197
Caers J (2000) Direct sequential indicator simulation. In: Kleingeld WJ, Krige DG (eds) Proceedings of the 6th International geostatistics 2000 congress. Cape Town, South Africa, pp 39–48
Castedo-Dorado F, Diéguez-Aranda U, Álvarez-González JG (2007) A growth model for Pinus radiata D. Don stands in north-western Spain. Ann For Sci 64:453–465
DGF (2001) Inventário Florestal Nacional, Portugal Continental. 3ª Revisão, 1995–1998. 233 pp., Direção Geral das Florestas, Lisboa
DGRF (2006) Manual de instruções para a realização do trabalho de campo—5° Inventário Florestal Nacional, 67 pp. Direção Geral dos Recursos Florestais, Lisboa
Dias AC, Arroja L (2012) Environmental impacts of eucalypt and maritime pine wood production in Portugal. J Clean Prod 37:368–376
Figueiral I (1995) Charcoal analysis and the history of Pinus pinaster (cluster pine) in Portugal. Rev Palaeobot Palynol 89:441–454
Goovaerts P (1997) Geostatistics for natural resources characterization. Oxford University Press, New York, p 483
ICNF (2013) IFN6—Áreas dos usos do solo e das espécies florestais de Portugal continental. Resultados preliminaries., Instituto da Conservação da Natureza e das Florestas. Lisboa, 34 pp
Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, Oxford, p 561
Journel AG, Alabert FG (1989) Non Gaussian data expansion in the earth sciences. Terra Nova 1:123–134
Nanos M, Montero G (2002) Spatial prediction of diameter distribution models. For Ecol Manag 161:147–158
Nunes R, Almeida JA (2010) Parallelization of sequential Gaussian, indicator and direct simulation algorithms. Comput Geosci 36:1042–1052
Oliveira AC, Pereira JS, Correia AV (2000) A silvicultura do pinheiro bravo, Porto. Centro Pinus, 111 pp
Santos C, Almeida JA (2001) Geostatistical characterization of a pine tree productivity index. In: Monestier P, Allard D, Froidevaux R (eds) geoENV III—geostatistics for environmental applications, quantitative geology and geostatistics. p 169–179
Santos C, Almeida JA (2003) Caracterização espacial de um índice de produtividade nos povoamentos de pinheiro-bravo em Portugal. Finisterra: Rev Port Geogr 38(75):51–65
Santos E, Almeida J, Soares A (2000) Geostatistical characterization of the migration patterns and pathways of the Wood Pigeon in Portugal. In: Kleingeld WJ, Krige DG (eds) Proceedings of the 6th international geostatistics 2000 congress. Cape Town, South Africa, pp 615–622
Soares A (2001) Direct sequential simulation and cosimulation. Math Geol 33(8):911–926
Soares A, Almeida JA, Guerreiro L (2006) Incorporating secondary information using direct sequential cosimulation In: Coburn TC, Yarus JM, Chambers RL (eds) Stochastic modeling and geostatistics: principles, methods, and case studies, Volume II: AAPG computer applications in geology, vol 5, pp 35–43
Soares P, Tomé M, Skovsgaard JP, Vanclay JK (1995) Evaluating growth models for forest management using continuous forest inventory data. For Ecol Manag 71:251–265
Viana H, Aranha J, Lopes D, Cohenc WB (2012) Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecol Model 226:22–35
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Santos, C., Almeida, J.A. (2014). Spatial Characterization of Maritime Pine Productivity in Portugal. In: Reboredo, F. (eds) Forest Context and Policies in Portugal. World Forests, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-319-08455-8_7
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DOI: https://doi.org/10.1007/978-3-319-08455-8_7
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