Measuring Non-Wood Forest Outputs in Numerical Forest Planning

Published Date

Volume 6 of the series Managing Forest Ecosystems pp 173-207

Title 

Measuring Non-Wood Forest Outputs in Numerical Forest Planning

A review of Finnish research

• Author 

• Timo Pukkala

Abstract

This chapter reviews some alternatives for numerically measuring the amount of forest outputs other than timber or economic profit. A common feature of the presented methods is that they can be used in numerical optimisation, either as a component of the objective or penalty function, or as a constraint. The chapter classifies the approaches for dealing with non-wood forest outputs into three categories, namely economic approach, numerical optimisation, and multi-attribute utility theory. The reviewed models devised for non-wood outputs are applicable to the numerical optimisation and utility theoretic approaches. The chapter gives several examples of both empirical and expert models, which have been developed in Finland to predict scenic beauty, amount of forest berries and mushrooms, and ecological quality of a forested landscape. The emphasis, in the description of ecological measures, is on variables, which help to mitigate the fragmentation problem of forest landscapes. In addition to models and approaches, the chapter also provides planning examples that utilise the discussed numerical models for non-wood outputs.

References

1. Alho J. M., Kolehmainen, O. and Leskinen, P. 2001. Regression methods for pairwise comparisons data. In: Schmoldt, D.L., Kangas, J., Mendoza, G.A. & Pesonen, M. (Eds.). The Analytic Hierarchy Process in Natural Resources and Environmental Decision Making. Kluwer Academic Publishers. Dordrech. Pp. 235–251.
2. Chou, Y., Minnich, R. A., Salazae, L. A., Power, J. D. and Dezzani, R. J. 1990. Spatial autocorrelation of wildfire distribution in the Idyllwild quadrangle, San Jacinto Mountain, California. Photogrammetric Engineering & Remote Sensing 56: 1507–1513.
3. Dykstra, D. 1984. Mathematical programming for natural resource management. McGraw-Hill, Inc. 318 pp.
4. Gadow, K. v. and Puumalainen, J. 2000. Scenario planning for sustainable forest management. In. Gadow, K. v., Pukkala, T. and Tomé, M. (Eds.). Sustainable Forest Management. Kluwer Academic Publishers. Dordrecht. Pp. 319–356.CrossRef
5. Gustke, L.D. and Hodgson, R.W. 1980. Rate of travel along an interpretive trail. The effect of an environmental discontinuity. Environment and Behaviour 12: 53–63.CrossRef
6. Hof, J. and Revers, M. 1998. Spatial optimisation for managed ecosystems. Columbia University Press. New York. 258 pp.
7. Hytönen, L., Leskinen, P. and Store, R. 2002. A spatial approach to participatory planning in forestry decision making. Scandinavian Journal, of Forest Research 17: 62–71
8. Ihalainen, M. and Pukkala, T. 2001. Modelling cowberry and bilberry yields from mineral soils and peatlands on the basis of visual estimation. Silva Fennica 35 (3): 329–340.
9. Ihalainen, M., Alho, J., Kolehmainen, O. and Pukkala, T. 2002. Expert models for berry and cowberry yields in Finnish forests. Forest Ecology and Management 5413: 1–8.
10. Ihalainen, M. and Pukkala, T. 2001. Modelling cowberry (Vaccinium vitis-idaea) and bilberry (Vaccinium myrtillus)yields from mineral soils and peatlands on the basis of field estimates. Silva Fennica 35 (3): 329–340.
11. Ihalainen, R. 1990. Structural changes in Finnish nonindustrial private forest ownership: A survey of the literature 1960–89. Folia Forestalia 750. 52 pp.
12. Ihalainen, R. 1992. Yksityismetsänomistuksen rakenne 1990. The Finnish Forest Research Institute, Research Notes 405. 41 pp.
13. Kangas, A. and Kangas, J. 1998. Ekologiset mallit ja ekologisten riskien hallinta metsäsuunnittelussa. Folia Forestalia 2: 201–222.
14. Kangas, J., Karsikko, J., Laasonen, L. and Pukkala, T. 1993a. A Method for estimating habitat suitability on the basis of expertise. Silva Fennica 27 (4): 259–268.
15. Kangas, J., Laasonen, L. and Pukkala, T. 1993b. A method for estimating forest landowner’s landscape preferences. Scandinavian Journal of Forest Research 8: 408–417.CrossRef
16. Kangas, J. and Pukkala, T. 1996. Operationalization of biological diversity as a decision objective in tactical forest planning. Canadian Journal of Forest Research 26 (1): 103–111.CrossRef
17. Kangas, J. and Mononen, A. 1997. Ekologiseen asiantuntemukseen perustuvan numeerisen mallin tuottaminen metsäalueen biodiversiteetin arviointiin. Folia Forestalia 2: 225–238.
18. Kangas, J., Store, R., Leskinen, P. and Mehtätalo, L. 2000. Improving the quality of landscape ecological forest planning by utilizing advanced decision-support tools. Forest Ecology and Management 132: 157–171.CrossRef
19. Kangas, J., Pukkala, T. and Kangas, A. 2001. HERO: Heuristic optimisation for multi-criteria decision analysis. In: Schmoldt. D. L., Kangas, J., Mendoza, G. A. and Pesonen, M. (Eds.). The Analytic Hierarchy Process in Natural Resource and Environmental Decision Making. Kluwer Academic Publishers. pp. 51–65.
20. Korhonen, P. and Laakso, J. 1986. A visual interactive method for solving multiple criteria problems. European Journal of Operational Research 24: 277–287.CrossRef
21. Kouki, J. 1993. Luonnon monimuotoisuus valtion metsissä — katsaus ekologisiin tutkimustarpeisiin ja suojelin mandollisuuksiin. Metsähallituksen luonnonsuojelujulkaisuja All. 88 pp.
22. Kurttila, M. 2001. The spatial structure of forests in the optimization calculations of forest planning — a landscape ecological perspective. Forest Ecology and Management 142: 129–142.CrossRef
23. Kurttila, M., Pukkala, T. and Loikkanen, J. 2002. The performance of alternative spatial objective types in forest planning calculations: a case for flying squirrel and moose. Forest Ecology and Management 166: 245–260.CrossRef
24. McGarical, K. and Marks B. J. 1995. FRAGSTATS: Spatial pattern analysis program for quantifying landscape structure. USDA Forest Service, PNW, General Technical Report 351. 122 pp.
25. Mykrä, S. and Kurki, S. 1998. ESC-strategy for rational operationalisation of forest biodiversity maintenance in Finland. Silva Fennica 32: 389–399.
26. Nalli, A., Nuutinen, T. and Päivinen, R. 1996. Site-specific constraints in integrated forest planning. Scandinavian Journal of Forest Research 11: 85–96.CrossRef
27. Naskali, A. 1991. Monikäytön ekonomia. In: Tapion Taskukirja. Kustannusosakeyhtiö Metsälehti. Helsinki. Pp. 271–285.
28. Næsset, E. 1997a. Geographical information systems in long-term forest management and planning with special reference to preservation of biological diversity. Forest Ecology and Management 93: 121–136.CrossRef
29. Næsset, E. 1997b. A spatial decision support system for long-term forest management planning by means of linear programming and geographical information systems. Scandinavian Journal of Forest Research 12: 77–88.CrossRef
30. Nousiainen, I. and Pukkala, T. 1992. Use of computer graphics for predicting the amenity of forest trails. Silva Fennica 26 (4): 241–250.
31. Öhman, K. 2000. Creating contiguous areas of old forest in long term forest planning. Canadian Journal of Forest Research 30: 1817–1823.CrossRef
32. Öhman, K. 2001. Forest planning with consideration to spatial relationships. Acta Universitatis Agriculturae Sueciae, Silvestria 198. 32 pp. + App.
33. Öhman, K. and Eriksson, L. O. 1998. The core area concept in forming contiguous areas for long term forest planning. Canadian Journal of Forest Research 28: 1032–1039.CrossRef
34. Oksanen-Peltola, L. 1999. Metsäsuunnittelun lähtökohta. Metsäntutkimuslaitoksen tiedonantoja 741: 8–11.
35. Ovaskainen, V. and Kuuluvainen, J. (Eds.) 1994. Yksityismetsänomistuksen rakennemuutos ja metsien käyttö. Metsäntutkimuslaitoksen tiedonantoja 484. 144 pp.
36. Pukkala, T., Kangas, J., Kniivilä, M. and Tiainen, A-M. 1997. Integrating forest-level and compartmentlevel indices of species diversity with numerical forest planning. Silva Fennica 31 (4): 417–429.
37. Pukkala, T., Nuutinen, T. and Kangas, J. 1995. Integrating the amenity of forest area into numerical forest planning. Landscape and Urban Planning 132: 185–195.CrossRef
38. Pukkala, T., Kellomäki, S. and Mustonen, E. 1988. Prediction of the amenity of a tree stand. Scandinavian Journal of Forest Research 3: 533–544.CrossRef
39. Pukkala, T. 1988. Methods to incorporate the amenity of landscape into forest management planning. Silva Fennica 22 (2): 135–146.
40. Pukkala, T. and Pykäläinen, J. 2000. Super-HERO: Visuaalinen käyttöliittymä vuorovaikutteiseen optimointiin. University of Joensuu, Faculty of Forestry, Research Notes 105: 33–40.
41. Reed, D. D. and Burkhart, H. E. 1985. Spatial autocorrelation of individual tree characteristics in loblolly pine stands. Forest Science 31: 575–587.
42. Saaty, T. 1977. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15: 234–281.CrossRef
43. Saaty, T. 1980. The Analytic Hierarchy Process. McGraw-Hill. New York. 287 pp.
44. Shafer, E. and Brush, R. 1977. How to measure preferences for photographs of natural landscapes. Landscape Planning 4: 237–256.CrossRef
45. Siitonen, M. 1999. Metsien käsittely endogeenisesta analyysista — näkemyksiä ja näkökulmia. The Finnish Forest Research Institute, Research Notes 725, Pp 10–26. [In Finnish]
46. Silvennoinen, H., Alho, J., Kolehmainen, O. and Pukkala, T. 2001. Prediction models of landscape preferences at the forest stand level. Landscape and Urban Planning 56 (1–2): 11–20.CrossRef
47. Store, R. 1996. Maiseman huomioonottavan metsikkökuvioinnin tuottaminen paikkatietojärjestelmällä. Folia Forestalia. 1996 (3): 245–262.
48. Store, R. and Kangas, J. 2001. Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modelling. Landscape and Urban Planning 55: 79–83.CrossRef
49. Tyrväinen, L. 1999. Monetary valuation of urban forest amenities in Finland. Finnish Forest Research Institute, Research Papers 739. 55 pp + App.

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