Optimizing inventory and yield data collection for forest management planning

Forest Science

Volumn 56, Issue 6, 2010, Pages 578-591

  Gilabert, Horacio ^a   McDill, Marc E ^b  

^a PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Cost plus loss; Growth and yield; Harvest scheduling; Linear programming; Uncertainty

Indexed keywords

CURRENT PRACTICES; DATA COLLECTION; EXPECTED VALUES; FOREST MANAGEMENT PLANNING; FOREST PLANNING; GROWTH AND YIELD; HARVEST MODEL; HARVEST SCHEDULING; HARVEST SCHEDULING MODEL; INVENTORY INFORMATION; OPTIMAL INVESTMENTS; OPTIMAL NUMBER; RADIATA PINE; UNCERTAINTY;

ECONOMICS; ESTIMATION; FORESTRY; HARVESTING; INVESTMENTS; OPTIMIZATION; PLANNING; SCHEDULING;

COST BENEFIT ANALYSIS;

COST ANALYSIS; ESTIMATION; FORESTRY; GROWTH; HARVESTING; INVESTMENT; LINEAR PROGRAMING; OPTIMIZATION; PLANNING; SCHEDULING; YIELD;

RADIATA;

EID: 78650834307     PISSN: 0015749X     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Bell, D.E. 1982. Regret in decision making under uncertainty. Oper. Res. 30(5):961-981.
2. Bell, D.E. 1985. Disappointment in decision making under uncertainty. Oper. Res. 33(1):1-27.
3. Borders, B.E., W.M. Harrison, M.L. Clutter, B.D. Shiver, and R.A. Souter. 2008. The value of timber inventory information for management planning. Can. J. For. Res. 38:2287-2294.
4. Boyd, S., and L. Vandenberghe. 2004. Convex optimization. Cambridge University Press, Cambridge, UK. 716 p.
5. Cochran, W.G. 1977. Sampling techniques. John Wiley & Sons, New York, NY. 428 p.
6. Duvemo, K., A. Barth, and J. Wallerman. 2007. Evaluating sample plot imputation techniques as input for forest management planning. Can. J. For. Res. 37:2069-2079.
7. Duvemo, K., and T. Lämås. 2006. The influence of forest data quality on planning processes in forestry. Scand. J. For. Res. 21:327-339.
8. Eid, T. 2000. Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. Silva Fenn. 34(2):89-100.
9. Fundación Chile. 2005. Tablas auxiliares de Producción. Informe Proyecto FONDEF D01/1021. 100 p.
10. Gertner, G.Z., and M. Köhl. 1992. An assessment of some non-sampling errors in a national survey using an error budget. For. Sci. 38(3):525-538.
11. Gilabert, H. 2007. Optimizing yield data collection efforts for forest management planning, Ph.D. thesis, The Pennsylvania State Univ., University Park, PA. 172 p.
12. Hamilton, D.A. 1978. Specifying precision in natural resource inventories. P. 276-281 in Integrated inventories on renewable natural resources, Lund, H.G., V.J. LaBau, P.F. Ffolliot, and D.W. Robinson (eds.). US For. Serv. Gen. Tech. Rep. RMGTR- 55. 482 p.
13. Hof, J.G., B.M. Kent, and J.B. Pickens. 1992. Chance constraints and chance maximization with random yield coefficients in renewable resource optimization. For. Sci. 38(2):305-323.
14. Hof, J.G., and J.B. Pickens. 1991. Chance-constrained and Chance-maximizing mathematical programs in renewable resource management. For. Sci. 37(1):308-325.
15. Hof, J.G., K.S. Robinson, and D.R Betters. 1988. Optimization with expected values of random yield coefficients in renewable resource linear programs. For. Sci. 34(3):634-646.
16. Hoganson, H.M., and D.W. Rose. 1987. A model for recognizing forestwide risk in timber management scheduling. For. Sci. 33(2):268-282.
17. Holopainen, M., and M. Talvitie. 2006. Effect of data acquisition accuracy on timing of stand harvests and expected net present value. Silva Fenn. 40(3):531-543.
18. Ignizio, J.P., and T.M. Cavalier. 1994. Linear programming. Prentice Hall, Upper Saddle River, NJ. 666 p.
19. Kangas, A.S., and J. Kangas. 1999. Optimization bias in forest management planning solutions due to errors in forest variables. Silva Fenn. 33(4):303-315.
20. Marshall, P.L. 1988. A procedure for constructing timber management strategies under uncertainty. Can. J. For. Res. 18:398-405.
21. Martell, D.L., E.A. Gunn, and A. Weintraub. 1998. Forest management challenges for operational researchers. Eur. J. Oper. Res. 104:1-7.
22. Mowrer, T.H. 2000. Uncertainty in natural resource decision support systems: Sources, interpretation, and importance. Comp. Elect. Agric. 27:139-154.
23. Pickens, J.B., and P.E. Dress. 1988. Use of stochastic production coefficients in linear programming models: Objective function distributions, feasibility and dual activities. For. Sci. 34(3):574-591.
24. Reed, D., and E.A. Jones. 2003. How good is good enough? Information quality needs for management decision making. P. 359-366 in Modelling forest systems, Amaro, A., D. Reed, and P. Soares (eds.). CABI, Wallingford, Oxfordshire, UK. 432 p.
25. Simon, C.P., and L. Blume. 1994. Mathematics for economists. W.W. Norton Inc., New York, NY. 930 p.
26. Thompson, E.F., and R.W. Haynes. 1971. A linear programming- probabilistic approach to decision making under uncertainty. For. Sci. 17(2):224-229.
27. Vanclay, J.K. 1994. Modelling forest growth and yield: Applications to mixed tropical forests. CABI, Wallingford, Oxfordshire, UK. 312 p.
28. Weintraub, A., and A. Abramovich. 1995. Analysis of uncertainty of future timber yields in forest management. For. Sci. 41(2):217-234.