Annals of Forest Science

, Volume 68, Issue 2, pp 267–274

Point process models for mixed sessile forest stands

  • Marie Ange Ngo Bieng
  • Christian Ginisty
  • François Goreaud
Original Paper

Abstract

Background

Growth modelling of complex stands calls for the use of spatially explicit single-tree models. Such models require spatially explicit tree locations as the initial state to run simulations. Given the cost of such data, virtual forest stands, where tree locations are simulated, are generally used as the initial state.

Purpose

The purpose of this study was to present models for simulating the spatial structure of complex stands. It focused on mixed oak–Scots pine stands of the Orleans forest (France) and on the spatial structure of canopy trees.

Methods

The spatial structure of the oak–pine stands was modelled with appropriate point process models. The models consisted of a combination of Poisson, Neyman–Scott and Soft core. Simulation of the point process models was based on precise characterisation of the studied stands. Twenty-five 1-ha oak–pine plots were characterised by the Ripley function. The models were then fitted to the identified spatial structure to reproduce univariate and bivariate spatial patterns in each spatial type.

Conclusion

This paper provides an approach for general modelling of a spatial structure of a particular mixture and may be enriched by other point process models for other types of mixed stand.

Keywords

Point process model Complex stand Spatial structure Ripley’s function Mixed oak–pine stand 

References

  1. Batista JLF, Maguire DA (1998) Modeling the spatial structure of tropical forests. For Ecol Manage 110(1–3):293–314CrossRefGoogle Scholar
  2. Biging GS, Dobbertin M (1995) Evaluation of competition indexes in individual tree growth-models. For Sci 41:360–377Google Scholar
  3. Buongiorno J, Peyron JL, Houllier F, Bruciamacchie M (1995) Growth and management of mixed-species, uneven-aged forests in the French Jura: implications for economic returns and tree diversity. For Sci 41(3):397–429Google Scholar
  4. Caquet B, Montpied P, Dreyer E, Epron D, Collet C (2010) Response to canopy opening does not act as a filter to Fagus sylvatica and Acer sp. advance regeneration in a mixed temperate forest. Ann For Sci 67(105):11Google Scholar
  5. Comas C, Mateu J (2007) Modelling forest dynamics: a perspective from point process methods. Biom J 49:176–196PubMedCrossRefGoogle Scholar
  6. Courbaud B, Goreaud F, Dreyfus P, Bonnet FR (2001) Evaluating thinning strategies using a tree distance dependent growth model: some examples based on the Capsis software uneven-aged spruce forests module. For Ecol Manage 145(1–2):15–28CrossRefGoogle Scholar
  7. Cressie NAC (1993) Statistics for spatial data. Wiley, New York, p 900Google Scholar
  8. de Coligny F, Ancelin P, Cornu G, Courbaud B, Dreyfus P, Goreaud F, Gourlet-Fleury S, Meredieu C, Orazio C, Saint-André L (2004) Capsis: computer-aided projection for strategies in silviculture : open architecture for a shared forest-modelling platform.In: Proceedings of the IUFRO Working Party S5.01–04 Conference, Harrison, British Columbia, Canada, 8–15 September 2002, pp. 371–380Google Scholar
  9. Diggle PJ (1983) Statistical analysis of spatial point patterns. Academic, New York, p 148Google Scholar
  10. Diggle PJ, Gratton RJ (1984) Monte Carlo methods of inference for implicit statistical models. J R Stat Soc 46(2):193–227Google Scholar
  11. Dobbertin M (2009) Forest ecosystems in a changing environment: what are future monitoring and research needs? Ann For Sci 66(4):400CrossRefGoogle Scholar
  12. Goreaud F. (2000) Apports de l’analyse de la structure spatiale en forêt tempérée à l’étude et la modélisation des peuplements complexes. PhD thesis in Forest Sciences. ENGREF, p. 362 http://pastel.paristech.org/54/ (unpublished).
  13. Goreaud F, Allain R, Courbaud B, Ngo Bieng MA, Perot T, Piroche JN (2007) Simuler des peuplements de structures variées pour faciliter l’utilisation des modèles «arbre» spatialisés. Revue Forestière Française 59(2):137–161Google Scholar
  14. Gourlet-Fleury S, Houllier F (2000) Modelling diameter increment in a lowland evergreen rain forest in French Guiana. For Ecol Manage 131(1–3):269–289CrossRefGoogle Scholar
  15. Illian J, Penttinen A, Stoyan H, Stoyan D (2008) Statistical analysis and modelling of spatial point patterns. Wiley, Chichester, p 534Google Scholar
  16. Kokkila T, Mäkelä A, Nikinmaa E (2002) A method for generating stand structures using Gibbs marked point process. Silva Fenn 36(1):265–277Google Scholar
  17. Lotwick HW, Silvermann BW (1982) Methods for analysing spatial processes of several types of points. J R Stat Soc 44(3):406–413Google Scholar
  18. Mateu J, Usó JL, Montes F (1998) The spatial pattern of a forest ecosystem. Ecol Modell 108(1–3):163–174CrossRefGoogle Scholar
  19. Moeur M (1997) Spatial models of competition and gap dynamics in old-growth Tsuga heterophylla Thuja plicata forests. For Ecol Manage 94(1–3):175–186CrossRefGoogle Scholar
  20. Neeff T, Biging GS, Dutra LV, Freitas CC, Dos Santos JR (2005) Markov point processes for modeling of spatial forest patterns in Amazonia derived from interferometric height. Remote Sens Environ 97(4):484–494CrossRefGoogle Scholar
  21. Ngo Bieng MA (2007) Construction de modèles de structure spatiale permettant de simuler des peuplements virtuels réalistes. Application aux peuplements mélangés chêne sessile - pin sylvestre de la région centre. PhD thesis in Forest Sciences. ENGREF, p. 209. http://pastel.paristech.org/3350/ (unpublished).
  22. Ngo Bieng MA, Ginisty C, Goreaud F, Perot T (2006) A first typology of oak and Scots pine mixed stands in the Orleans forest (France), based on the canopy spatial structure. NZ J For Sci 36(2–3):325–346Google Scholar
  23. Pacala SW, Canham CD, Saponara J, Silander JA, Kobe RK, Ribbens E (1996) Forest models defined by field measurements: estimation, error analysis and dynamics. Ecol Monogr 66:1–43CrossRefGoogle Scholar
  24. Parrott L, Lange H (2004) Use of interactive forest growth simulation to characterise spatial stand structure. For Ecol Manage 194(1–3):29–47CrossRefGoogle Scholar
  25. Pélissier R (1998) Tree spatial patterns in three contrasting plots of a southern Indian tropical moist evergreen forest. J Trop Ecol 14(1):1–16CrossRefGoogle Scholar
  26. Perry GLW, Miller BP, Enright NJ (2006) A comparison of methods for the statistical analysis of spatial point patterns in plant ecology. Plant Ecol 187:59–82CrossRefGoogle Scholar
  27. Pommerening A (2006) Evaluating structural indices by reversing forest structural analysis. For Ecol Manage 224(3):266–277CrossRefGoogle Scholar
  28. Ripley BD (1977) Modelling spatial patterns. J R Stat Soc 39:172–212Google Scholar
  29. Stoyan D, Penttinen A (2000) Recent applications of point process methods in forestry statistics. Stat Sci 15(1):61–78CrossRefGoogle Scholar
  30. Tomppo E (1986) Models and methods for analysing spatial patterns of trees. Communicationes Instituti Forestalis Fenniae n°138. The Finnish Forest Research Institute, Helsinki, p 65Google Scholar

Copyright information

© INRA and Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Marie Ange Ngo Bieng
    • 1
    • 2
    • 3
  • Christian Ginisty
    • 1
  • François Goreaud
    • 2
  1. 1.Unité de Recherche Ecosystèmes Forestiers, CEMAGREFNogent sur VernissonFrance
  2. 2.Laboratoire d’Ingénierie des Systèmes Complexes, CEMAGREFAubière Cedex 1France
  3. 3.CIRAD - UMR SYSTEMMontpellier Cedex 1France

Personalised recommendations