Research article

Visualization of optimized solution space using a simulation system for the development of local forest management planning

Nakajima Tohru , Kanomata Hidesato, Mitsuo Matsumoto

Nakajima Tohru
University of Tokyo, Laboratory of Forest Management, Graduate School of Agricultural and Life Sciences, the University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan. Email: nakajima@fr.a.u-tokyo.ac.jp
Kanomata Hidesato
Forestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba 305-8687, Japan
Mitsuo Matsumoto
Research coordinator, Forestry and Forest Product Research Institute, Japan

Online First: March 17, 2016
Tohru, N., Hidesato, K., Matsumoto, M. 2016. Visualization of optimized solution space using a simulation system for the development of local forest management planning. Annals of Forest Research DOI:10.15287/afr.2016.556


A simulation system, based on pre-existing models, was used to investigate and visualize the solution space for optimizing forest management planning. The simulation system used existing forestry profits estimation models to test the outcomes of different crop rotations by previous studies. The simulation enabled us to predict forestry profits and labor requirements under different forestry management plans, and to examine the consequences for harvesting strategies. A part of the Kyushu region was selected as the study site, because the basic sub-models for predicting timber production, labor requirements and forestry profits were developed in the area. This study has investigated and visualized the solution space optimized for forest economics using various combinations of short and long rotation silvicultural practices implemented at a local scale. Based on the simulations, optimization of the plans was formulated under the forestry scale of compartment and total level. Visualizing the 3-dimensional optimized solution space by using a simulation system is useful for decision-makers involved with local forest management planning. The differences of forestry profits, labor requirements and timber volume depending on the intensity of silvicultural practices were analyzed. The simulation system is also useful for sustainable forest management under the Japanese forestry planning system.


Beier C.M., Lovecraft A.L., Chapin F.S., 2009. Growth and collapse of a resource system: an adaptive cycle of change in public lands governance and forest management inAlaska. Ecology and Society, 14 (2): 5

Bettinger P.,BostonK., Siry J. P., Grebner, D. L., 2009.ForestManagement and Planning. Academic Press,Burlington.

Bettinger, P., D. Greatz, K. Boston, J. Sessions, W. Chung., 2002. Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems. Silva Fennica, 36:561–584. DOI: 10.14214/sf.545

DavisL.S., Johanson K.N., 1987.Forestmanagement.McGraw-Hill,New York.

DavisL.S., Johanson K.N., Bettinger P., Howard T.E., 2001.Forestmanagement to sustain ecological, economic and social values.McGraw-Hill,New York

Falcão A.O., Borges J.G. 2001. Designing an evolution program for solving integer forest management scheduling models: An application inPortugal.Forest. Science. 47:158 –168.

Forestry Agency 2007. Annual Report on Trends ofForestand Forestry—Fiscal Year 2006.Tokyo:JapanForestry Association, 17 pp.

Forestry Agency 2014. Forestry statistics.Tokyo:JapanForestry Association, 260 pp. (in Japanese)

ForestEcosystem Management Assessment Team, 1993.Forestecosystem management: an ecological, economic, and social assessment. US Department of Agriculture, Forest Service; US Department of Commerce, National Oceanic and Atmospheric Administration, US Department of Interior, Bureau of Land Management, U.S. Fish and Wildlife Service, and National Park Service; and Environmental Protection Agency, Washington DC, 139 p.

Gu C., 2002. Smoothing spline ANOVA models. Springer-Verlag Inc. DOI: 10.1007/978-1-4757-3683-0

Gulbrandsen L. H., 2005. Mark of sustainability? Challenges for fishery and forestry ecolabeling. Environment, 47 (5), 8-23. DOI: 10.3200/ENVT.47.5.8-23

Helms J. A. 1998. The Dictionary of Forestry.Bethesda,MD: Society of American Foresters, 125 p.

Holland, J.H., 1975. Adaptation in natural and artificial systems.UniversityofMichiganPress,AnnHarbor, MI.

Millennium Ecosystem Assessment Board. 2005. Millennium ecosystem assessment: ecosystems and human well-being: synthesis. Island Press:Washington, 137 pp.

Moseley C., Sandoval G., DavisE. J., 2014. Comparing conditions of labor-intensive forestry and fire suppression workers. Society & Natural Resources 27 (5) :540-556. DOI: 10.1080/08941920.2014.888792

Nakajima T., Kanomata H., Matsumoto M., Tatsuhara S., Shiraishi N., 2009a. The application of "Wood Max" for total optimization of forestry profits based on joint implementation silvicultural practices. Kyushu Journal ofForestResearch, 62: 176–180.

Nakajima T., Kanomata H., Matsumoto M., Tatsuhara S., Shiraishi N., 2011a. Cost-effectiveness analysis of subsidy schemes for industrial timber development and carbon sequestration in Japanese forest plantations. Journal of Forestry Research. 22 (1):1-12. DOI: 10.1007/s11676-011-0117-4

Nakajima T., Kanomata H., Tatsuhara S., Shiraishi N. 2011b. Simulation of the spatial distribution of thinning area under different silvicultural subsidy systems in Japanese plantation forests. Folia Forestalia Polonica. 53 (1): 3–16.

Nakajima T., Matsumoto M., Shiraishi N., 2011c. Modeling diameter growth and self-thinning in planted sugi (Cryptomeria japonica) stands. The OpenForestScience Journal. 4: 49-56. DOI: 10.2174/187439860 1104010049

Nakajima T., Matsumoto M., Tatsuhara S., 2009b. Development and application of an algorithm to estimate and maximize stumpage price based on timber market and stand conditions. Journal ofForestPlanning, 15:21–27.

Nakajima T., Matsumoto M., Sasakawa H., Ishibashi S., Tatsuhara S., 2010. Estimation of growth parameters within the Local Yield table Construction System for planted forests throughoutJapan. Journal ofForestPlanning, 15: 99–108.

OkaM., 2006. The study of analysis and valuation of harvesting operation by mechanization. PhD thesis of theUniversityofTokyo. (in Japanese. The title was translated from Japanese to English)

Pukkala, T., 2002. Multi-objective forest planning. Boston: Kluwer Academic, 207pp. DOI: 10.1007/978-94-015-9906-1

Science Council, 2008. The policy to progress research development of global environmental science technology. Japanese Ministry of Education, Culture, Sports, Science and Technology,Tokyo, 40 p. (in Japanese)

Stankey G.H., Clark R.N., Bormann B.T., 2005. Adaptive management of natural resources: theory, concepts, and management institutions. US Department of Agriculture, Forest Service, Pacific Northwest Research Station,Portland, pp73.

Yousefpour, R., Hanewinkel,M. 2014. Balancing Decisions for Adaptive and Multipurpose Conversion of Norway Spruce (Picea abies L. Karst) Monocultures in the Black Forest Area of Germany. ForestScience. 60: 73-84. DOI: 10.5849/forsci.11-125


No Supplimentary Material available for this article.
No metrics available for this article.

Related Articles

Related Authors

 



In Google Scholar

In Annals of Forest Research

In Google Scholar

 
  • Nakajima Tohru
  • Kanomata Hidesato
  • Mitsuo Matsumoto
  • Nakajima Tohru
  • Kanomata Hidesato
  • Mitsuo Matsumoto