Developing a decision support approach to reduce wind damage risk - A case study on sugi (Cryptomeria japonica (L.f.) D.Don) forests in Japan

Forestry

Volumn 81, Issue 3, 2008, Pages 429-445

  Kamimura, Kana ^a,e   Gardiner, Barry ^b   Kato, Akio ^c   Hiroshima, Takuya ^d   Shiraishi, Norihiko ^a  

^a UNIVERSITY OF TOKYO   (Japan)

^b FOREST RESEARCH   (United Kingdom)

^c Toyama Forestry and Forest Products Research Center   (Japan)

^d UNIVERSITY OF TOKYO   (Japan)

^e FORESTRY AND FOREST PRODUCTS RESEARCH INSTITUTE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

CONIFEROUS TREE; DECISION SUPPORT SYSTEM; ENVIRONMENTAL MODELING; GIS; RISK ASSESSMENT; SILVICULTURE; STORM DAMAGE; THINNING; TYPHOON; WIND FORCING;

ASIA; CHUBU; EURASIA; FAR EAST; HIMI; HONSHU; JAPAN; TOYAMA [CHUBU];

CRYPTOMERIA JAPONICA;

EID: 49749129058     PISSN: 0015752X     EISSN: 14643626     Source Type: Journal    
DOI: 10.1093/forestry/cpn029     Document Type: Article

References (47)

1. Achim, A., Ruel, J.-C., Gardiner, B.A., Laflamme, G. and Meunier, S. 2005 Modelling the vulnerability of balsam fir forests to wind damage. For. Ecol. Manage. 204, 35-50.
2. Avery, T.E. and Burkhart, H.E. 2002 Forest Measurements. 5th edn. McGraw-Hill, New York, 456 pp.
3. Berry, M.J.A. and Linoff, G.S. 2004 Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. 2nd. Wiley, Indianapolis, IN, 643 pp.
4. Blennow, K. and Sallnas, O. 2004 WINDA-a system of models for assessing the probability of wind damage to forest stands within a landscape. Ecol. Model. 175, 87-99.
5. Breiman, L., Friedman, J.F., Olshen, R.A. and Stone, C.J. 1984 Classification and Regression Trees. Chapman & Hall, Boca Raton, FL, 358 pp.
6. Bunnell, F.L. and Boyland, M. 2003 Decision-support systems: it ' s the question not the model. J. Nat. Conserv. 10, 269-279.
7. Cucchi, V., Meredieu, C., Stokes, A., de Coligny, F., Suarez, J. and Gardiner, B.A. 2005 Modelling the windthrow risk for simulated forest stands of Maritime pine (Pinus pinaster Ait.). For. Ecol. Manage. 213, 184-196.
8. Drew, T.J. and Flewelling, J.W. 1979 Stand density management: an alternative approach and its application to Douglas-fir plantations. For. Sci. 25, 518-532.
9. ESDU 1987 World-Wide Extreme Wind Speeds, Part 1: Origins and Methods of Analysis. ESDU data item No.87034, ESDU International, London.
10. Fan, Z.F., Kabrick, J.M. and Shifley, S.R. 2006 Classification and regression tree based survival analysis in oak-dominated forests of Missouri's Ozark highlands. Can. J. For. Res. 36, 1740-1748.
11. Forest Agency 1992 Statistics of Forest Insurance 1991. Forest Agency, Tokyo, Japan. (in Japanese).
12. Forest Agency 1999 Growth and Yield Models for Planted Forests (Models and a Guidebook). Japan Forest Technology Association, Tokyo, Japan. (in Japanese) 15 pp.
13. Forest Agency 2005 Statistics of Forest Insurance 2004. Forest Agency, Tokyo, Japan. (in Japanese).
14. Foster, D.R. and Boose, E.R. 1995 Hurricane disturbance regimes in temperate and tropical forest ecosystem. In Wind and Trees. M.P. Coutts and J. Grace (eds). Cambridge University Press, Cambridge, UK, pp. 305-339.
15. Gardiner, B., Peltola, H. and Kellomaki, S. 2000 Comparison of two models for predicting the critical wind speeds required to damage coniferous trees. Ecol. Model. 129, 1-23.
16. Gardiner, B., Byrne, K., Hale, S., Kamimura, K., Mitchell, S.J., Peltola, H. and Ruel, J.-C. 2008 A review of mechanistic modelling of wind damage risk to forests. Forestry. (in press).
17. Gardiner, B.A., Suárez, J. and Quine, C.P. 2003 Development of a GIS based wind risk system for British forestry. In Ruck, B., Kottmeier, C., Mattheck, C., Quine, C., Wilhelm, G. (Eds.), International Conference 'Wind Effects on Trees', University of Karlsruhe, Germany.
18. Jackson, P.S. and Hunt, J.C.R. 1975 Turbulent wind flow over a low hill. Q. J. R. Meteorol. Soc. 101, 929-955.
19. Kamimura, K. 2007 Developing a decision-support system for wind risk modelling as a part of forest management in Japan. Ph.D. thesis, The University of Tokyo, Tokyo, Japan, 126 pp.
20. Kamimura, K., Gardiner, B.A. and Shiraishi, N. 2007 Estimating long-term critical wind speed for wind damage by using mechanistic wind risk model, GALES. In FORMATH Vol. 6. A. Yoshimoto, T. Hiroshima and H. Kondoh (eds). Japan Society of Forest Planning Press, Utsunomiya, Japan, (in Japanese, English summary), pp. 19-28.
21. Kass, G.V. 1980 An exploratory technique for investigating large quantities of categorical data. Appl. Stat. 29, 119-127.
22. KNMI. 2005 Land-use and roughness classes in LGN3+. http://www.knmi.nl/ samenw/hydra/energy/classes.htm.
23. Kuboyama, H. and Oka, H. 2000 Climate risks and age-related damage probabilities-effects on the economically optimal rotation length for forest stand management in Japan. Silva Fenn. 34, 155-166.
24. Kuboyama, H., Zheng, Y. and Oka, H. 2003 Study about damage probabilities on major forest climatic risks according to age-classes. J. Jpn. For. Soc. 85, 191-198 (in Japanese, English summary).
25. Loh, W.Y. and Shih, Y.S. 1997 Split selection methods for classification trees. Stat. Sinica. 7, 815-840.
26. Matsuzaki, T. and Nakata, M. 1994 Study on forest damage at Sado Island by strong wind. Report on Grant-in-Aid for Scientific Research by the Ministry of Education, Science, Sports, and Culture 1992-1993, Niigata University, Tokyo. 66 pp. (in Japanese, English summary).
27. Miller, K.F. 1985 Windthrow hazard classification. Forestry Commission Bulletin No.85. HMSO, London, 14 pp.
28. Moisen, G.G., Frescino, T.S. 2000 Comparing five modelling techniques for predicting forest characteristics. Ecol. Model. 157, 209-225.
29. Moore, J. and Quine, C.P. 2000 A comparison of the relative risk of wind damage to planted forests in Border Forest Park, Great Britain, and the Central North Island, New Zealand. For. Ecol. Manage. 135, 345-353.
30. Mortensen, N.G., Heathfield, D.N., Myllerup, L., Landberg, L. and Rathmann, O. 2004 Getting Started with WAsP 8. Risø National Laboratory, Roskilde, Denmark, 88 pp.
31. Olofsson, E. and Blennow, K. 2005 Decision support for identifying spruce forest stand edges with high probability of wind damage. For. Ecol. Manage. 207, 87-98.
32. Peltola, H., Kellomaki, S., Vaisanen, H. and Ikonen, V.P. 1999 A mechanistic model for assessing the risk of wind and snow damage to single trees and stands of Scots pine, Norway spruce, and birch. Can. J. For. Res. 29, 647-661.
33. Quine, C. 1995 Assessing the risk of wind damage to forests: practice and pitfalls. In Wind and Trees. M.P. Coutts and J. Grace (eds). Cambridge University Press, Cambridge, UK, pp. 379-403.
34. Quine, C.P. and White, I.M.S. 1993 Revised Windiness Scores for the Windthrow Hazard Classification: The Revised Scoring Methods. Forestry Commission Research Information Note 230. Forestry Commission, Edinburgh, UK.
35. Ruel, J.-C., Quine, C.P., Menunier, S., Suarez, J. 2000 Estimating windthrow risk in balsam fir stands with the ForestGALES model. For. Chron. 76(2), 329-337.
36. Seely, B., Nelson, J., Wells, R., Peter, B., Meitner, M. and Anderson, A. et al. 2004 The application of a hierarchical, decision-support system to evaluate multi-objective forest management strategies: a case study in northeastern British Columbia, Canada. For. Ecol. Manage. 199, 283-305.
37. Slodičák, M. 1995 Thinning regime in stands of Norway spruce subjected to snow and wind damage. In Wind and Trees. M.P. Coutts and J. Grace (eds). Cambridge University Press, Cambridge, UK, pp. 436-447.
38. Solomon, S., Qin, D., Manning, M., Marquis, M., Averyt, K., Tignor, M.M.B., Miller, H.L. and Chen, Z. (eds). 2007 Climate Change 2007 the physical Science Basis. (IPCC Working Group 1 Final Report). Cambridge University Press, Cambridge, UK, 996 pp. http://ipcc-wg1.ucar.edu/wg1/wg1- report.html.
39. SPSS, 2004 SPSS Classification Trees 13.0J. SPSS Inc, Chicago, IL, 111 pp.
40. Suárez, J.C., Gardiner, B.A. and Quine, C.P. 1999 A comparison of three methods for predicting wind speeds in complex forested terrain. Meteorol. Appl. 6, 329-342.
41. Tamate, S. 1967 Wind caused damages in forests and preventive approaches. Ringyo Gijutsu. 306, 21-25 (in Japanese).
42. Tanaka, K. 1991 Forest growth model. Shinrinkagaku. 10, 28-31 (in Japanese).
43. Toyama Forestry and Forest Products Research Center, 2005 Silve-no-Mori Manual. Toyama Prefecture, Toyama. 22 pp. (in Japanese).
44. Ture, M., Kurt, I., Kurum, A.T. and Ozdamar, K. 2005 Comparing classification techniques for predicting essential hypertension. Expert Syst. Appl. 29, 583-588.
45. Venäläinen, A., Zeng, H.C., Peltola, H., Talkkari, A., Strandman, H. and Wang, K.Y. et al. 2004 Simulations of the influence of forest management on wind climate on a regional scale. Agric. For. Meteorol. 123, 149-158.
46. Yoshida, M., Yamamoto, M., Takagi, K. and Ohkuma, T. 2006 Prediction of typhoon wind by level 2.5 closure model. J. Wind Eng. 108, 189-192.
47. Zeng, H., Talkkari, A., Peltola, H. and Kellomäki, S. 2007 A GIS-based decision support system for wind assessment of wind damage in forest management. Environ. Model. Softw. 22, 1240-1249.