Abstract
The most common scientific approach to numerical landscape-level forest management planning is combinatorial optimization aimed at finding the optimal combination of the treatment alternatives of stands. The selected combination of treatments depends on the conditions of the forest, and the objectives of the forest landowners. A two-step procedure is commonly used to derive the plan. First, treatment alternatives are generated for the stands using an automated simulation tool. Second, the optimal combination of the simulated treatment schedules is found by using mathematical programming or various heuristics. Simulation of treatment schedules requires models for stand dynamics and volume for all important tree species and stand types present in the forest. A forest planning system was described for Northeast China. The necessary models for stand dynamics and tree volume were presented for the main tree species of the region. The developed models were integrated into the simulation tool of the planning system. The simulation and the optimization tools of the planning system were described. The optimization tool was used with heuristic methods, making it possible to easily solve also spatial forest planning problems, for instance aggregate cuttings. Finally, the use of the system is illustrated with a case study, in which nonspatial and spatial management plans are developed for the Mengjiagang Forest District.
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References
Adams DM, Ek AR (1974) Optimizing the management of uneven-aged forest stands. Can J For Res 4:274–287
Bettinger P, Boston K, Sessions J (1999) Intensifying a heuristic forest harvest scheduling procedure with 2-opt decision choices. Can J For Res 29:1784–1792
Bettinger P, Boston K, Siry JP, Grebner DL (2009) Forest management and planning. Academic Press, New York, p 331
Bettinger P, Graetz D, Boston K, Sessions J, Chung W (2002) Eight heuristic planning techniques applied to three increasingly difficult wildlife planning problems. Silva Fenn 36(2):561–584
Borges J, Hoganson H, Falçao A (2002) Heuristics in multi-objective forest management. In: Pukkala T (ed) Multi-objective forest planning. Kluwer Academic Publishers, Netherlands, pp 119–151
Boston K, Bettinger P (1999) An analysis of Monte Carlo integer programming, simulated annealing and tabu search for solving spatial harvest scheduling problems. For Sci 45:292–301
Burkhart HE, Tomé M (2012) Modeling forest trees and stands. Springer, Dordrecht, p 457
Clutter JL, Forston JC, Piennar LV, Brister GH, Bailey RL (1983) Timber management—a quantitative approach. Willey, New York, p 333
de-Miguel S, Mehtätalo L, Shater Z, Kraid B, Pukkala T (2012) Evaluating marginal and conditional predictions of taper models in the absence of calibration data? Can J For Res 42:1383–1394
de-Miguel S, Guzmán G, Pukkala T (2013) A comparison of fixed- and mixed-effects modeling in tree growth and yield prediction of an indigenous neotropical species (Centrolobium tomentosum) in a plantation system. For Ecol Manag 291:249–258
Dowsland KA (1993) Simulated annealing. In: Reeves Colin R (ed) Modern heuristic techniques for combinatorial problems. Blackwell Scientific Publications, New York, pp 20–69
Garber SM, Maguire DA (2003) Modeling stem taper of three central Oregon species using nonlinear mixed effects models and autoregressive error structures. For Ecol Manag 179:507–522
Glover F, Laguna M (1993) Tabu search. In: Reeves Colin R (ed) Modern heuristic techniques for combinatorial problems. Blackwell Scientific Publications, New York, pp 70–150
Gray CD, Kinnear PR (2011) IBM SPSS Statistics 19 made simple. Routledge. ISBN 9781848722255, p 668
Heinonen T, Pukkala T (2004) A comparison between one- and two-neighbourhoods in heuristic search with spatial forest management goals. Silva Fenn 38(3):319–332
Hoganson HM, Rose DW (1984) A simulation approach for optimal timber management scheduling. For Sci 30:220–238
Hooke R, Jeeves T (1961) “Direct search” solution of numerical and statistical problems. J Ass Comp Mach 8:212–229
Kozak A (1997) Effects of multicollinearity and autocorrelation on the variable-exponent taper functions. Can J For Res 27:619–629
Kozak A (2004) My last words on taper equations. For Chron 80:507–515
Kurttila M, Pukkala T, Loikkanen J (2002) The performance of alternative spatial objective types in forest planning calculations: a case for flying squirrel and moose. For Ecol Manag 166:245–260
Öhman K (2000) Creating contiguous areas of old forest in long term forest planning. Can J For Res 30:1817–1823
Päivinen R, Roihuvuo L, Siitonen M (Eds) (1996) Large-scale forestry scenario models: experiences and requirements. EFI Proceedings No. 5, p 318
Pukkala T (2002) Introduction to multi-objective forest planning. In: Pukkala T (ed) Multi-objective forest planning. Kluwer Academic Publishers, Dordrecht, pp 1–19
Pukkala T (2004) Dealing with ecological objectives in the Monsu planning system. Silva Lusitana Spec Issue 2004:1–15
Pukkala T, Heinonen T (2006) Optimizing heuristic search in forest planning. Nonlinear Anal 7:1284–1297
Pukkala T, Kurttila M (2005) Examining the performance of six heuristic optimization techniques in different forest planning problems. Silva Fenn 39(1):67–80
Pukkala T, Heinonen T, Kurttila M (2009) An application of the reduced cost approach to spatial forest planning. For Sci 55(1):13–22
Pukkala T, Lähde E, Laiho O (2014) Optimizing any-aged management of mixed boreal under residual basal area constraints. J For Res 25(3):627–636
Reeves CR (1993) Modern heuristic techniques for combinatorial problems. Blackwell Scientific Publications, New York, p 320
Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot 10:290–300
Shater Z, de Miguel S, Kraid B, Pukkala T, Palahí M (2011) A growth and yield model for even-aged Pinus brutia stands in Syria. Ann For Sci 68(1):149–157
State Forestry Administration (2014) The seventh forest resource survey report. Chinese Forestry Press, Beijing (In Chinese)
Valsta L (1992) An optimization model for Norway spruce management based on individual-tree growth models. Acta For Fenn 232:20
Vanclay JK (1994) Modelling forest growth and yield: applications to mixed tropical forests. CABI, Walingford 312
Zeng H, Pukkala T, Peltola H, Kellomäki S (2007) Application of ant colony optimization for the risk management of wind damage in forest planning. Silva Fenn 41(2):315–332
Acknowledgments
This research was financially supported by the Ministry of Science and Technology of the People’s Republic of China (2015BAD09B01), and the Fundamental Research Funds for the Central Universities of the People’s Republic of China (2572014BA09). The authors thank the teachers and students of the Department of Forest Management, Northeast Forestry University (NEFU), PR China, who provided and collected the data for this study.
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Project funding: This research was financially supported by the Ministry of Science and Technology of the People’s Republic of China (2015BAD09B01), and the Fundamental Research Funds for the Central Universities of the People’s Republic of China (2572014BA09).
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Corresponding editor: Yu Lei
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Jin, X., Pukkala, T. & Li, F. A management planning system for even-aged and uneven-aged forests in northeast China. J. For. Res. 27, 837–852 (2016). https://doi.org/10.1007/s11676-016-0216-3
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DOI: https://doi.org/10.1007/s11676-016-0216-3