Abstract
When an inventory is planned for, the decision makers typically strive for maximizing accuracy of the information with a given budget, or even maximize accuracy without considering any budget at all. Recent developments in airborne laser scanning and other remote sensing techniques facilitate the use of data obtained from such sources as an integrated part of the forest inventory process. However, a rational decision maker would not pay for information that is more expensive than the expected improvement in the value of the decisions based on the new information. The statistical accuracy that usually is provided in the remote sensing literature does not dictate the usefulness of the data in decision making. In this chapter we present methods to assess the value of information and go through the recent research related to this where we in different ways try to establish the links between the inventory effort level, decisions to be made and the value of information.
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References
Ades AE, Lu G, Claxton K (2004) Expected value of sample information calculations in medical decision modeling. Med Decis Mak 24:207–227
Bergseng E, Ørka HO, Næsset E, Gobakken T (2013) Valuation of information obtained from different forest inventory approaches and remote sensing data sources (Unpublished manuscript)
Bevers M (2007) A chance constrained estimation approach to optimizing resource management under uncertainty. Can J For Res 37:2270–2280
Birchler U, Bütler M (2007) Information economics. Routledge advanced texts in economics and finance. Routledge, London, 462 p
Borders BE, Harrison WM, Clutter ML, Shiver BD, Souter RA (2008) The value of timber inventory information for management planning. Can J For Res 38:2287–2294
Boychuck D, Martell DL (1996) A multistage stochastic programming for sustainable forest-level timber supply under risk of fire. For Sci 42:10–26
Burkhart HE, Stuck RD, Leuschner WA, Reynolds MA (1978) Allocating inventory resources for multiple-use planning. Can J For Res 8:100–110
Duvemo K, Lämås T, Eriksson L-O, Wikström P (2012) Introducing cost-plus-loss analysis into a hierarchical forestry planning environment. Ann Oper Res. doi:10.1007/s10479-012-1139-9
Eid T (2000) Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. Silva Fenn 34:89–100
Eid T (2003) Model validation by means of cost-plus-loss analyses. In: Amaro A, Reed D, Soares P (eds) Modelling forest systems. CABI Publishing, Wallingford, UK, pp 295–305
Eid T, Gobakken T, Næsset E (2004) Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses. Scand J For Res 19:512–523
Eriksson L-O (2006) Planning under uncertainty at the forest level: a systems approach. Scand J For Res 21:111–117
Gertner G (1987) Approximating precision in simulation projections: an efficient alternative to Monte Carlo methods. For Sci 33:230–239
Gilabert H, McDill M (2010) Optimizing inventory and yield data collection for forest management planning. For Sci 56:578–591
Hamilton DA (1978) Specifying precision in natural resource inventories. In: Integrated inventories of renewable resources: proceedings of the workshop. USDA Forest Service, General technical report RM-55, pp 276–281
Holmström H, Kallur H, Ståhl G (2003) Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fenn 37:381–398
Islam N, Kurttila M, Mehtätalo L, Haara A (2009) Analyzing the effects of inventory errors on holding-level forest plans: the case of measurement error in the basal area of the dominated tree species. Silva Fenn 43:71–85
Islam N, Kurttila M, Mehtätalo L, Pukkala T (2010) Inoptimality losses in forest management decisions caused by errors in an inventory based on airborne laser scanning and aerial photographs. Can J For Res 40:2427–2438
Kangas A (1997) On the prediction bias and variance of long-term growth predictions. For Ecol Manag 96:207–216
Kangas A (2010) Value of forest information. Eur J For Res 129:863–874
Kangas A (2013) Effect of sustainability constraints on the value of forest information (in review)
Kangas A, Horne P, Leskinen P (2010a) Measuring the value of information in multi-criteria decision making. For Sci 56:558–566
Kangas A, Lyhykäinen H, Mäkinen H (2010b) Value of quality information in timber bidding. Can J For Res 40:1781–1790
Kangas A, Lyhykäinen H, Mäkinen H, Lappi J (2012) Value of quality information in selecting stands to be purchased. Can J For Res 42:1347–1358
Ketzenberg ME, Rosenzweig ED, Marucheck AE, Metters RD (2007) A framework for the value of information in inventory replenishment. Eur J Oper Res 182:1230–1250
Kim JB, Hobbs BF, Koonce JF (2003) Multicriteria Bayesian analysis of lower trophic level uncertainties and value of research in Lake Erie. Hum Ecol Risk Assess 9:023–1057
Lawrence DB (1999) The economic value of information. Springer, New York, 393 p
Mäkinen A, Kangas A, Mehtätalo L (2010) Correlations, distributions, and trends in forest inventory errors and their effects on forest planning. Can J For Res 40:1386–1396
Mäkinen A, Kangas A, Nurmi M (2012) Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy. Silva Fenn 46:211–226
Mowrer HT (1991) Estimating components of propagated variance in growth simulation model projections. Can J For Res 21:379–386
Palma CD, Nelson JD (2009) A robust optimization approach protected harvest scheduling decisions against uncertainty. Can J For Res 39:342–355
Pietilä I, Kangas A, Mäkinen A, Mehtätalo L (2010) Influence of growth prediction errors on the expected losses from forest decisions. Silva Fenn 44:829–843
Rasinmäki J, Kangas A, Mäkinen A, Kalliovirta J (2013) Value of information in DSS: models & data. In: Tuček J, Smreček R, Majlingová A, Garcia-Gonzalo J (eds) Implementation of DSS tools into the forestry practice, Reviewed conference proceedings. Published by Technical University in Zvolen, Slovakia, 167p. ISBN 978-80-228-2510-8
Ståhl G (1994) Optimizing the utility of forest inventory activities. Swedish University of Agricultural Sciences, Department of Biometry and Forest Management, Umeå. Report 27
Ståhl G, Holm S (1994) The combined effect of inventory errors and growth prediction errors on estimations of future forestry states. Manuscript. In: Ståhl G (1994) Optimizing the utility of forest inventory activities. Swedish University of Agricultural Sciences, Department of Biometry and Forest Management, Umeå. Report 27
Ståhl G, Carlson D, Bondesson L (1994) A method to determine optimal stand data acquisition policies. For Sci 40:630–649
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Kangas, A., Eid, T., Gobakken, T. (2014). Valuation of Airborne Laser Scanning Based Forest Information. In: Maltamo, M., Næsset, E., Vauhkonen, J. (eds) Forestry Applications of Airborne Laser Scanning. Managing Forest Ecosystems, vol 27. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8663-8_16
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DOI: https://doi.org/10.1007/978-94-017-8663-8_16
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