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The effect of forest information quality on the planning and decision process in forestry

Saad, Rami (2017). The effect of forest information quality on the planning and decision process in forestry. Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta Universitatis Agriculturae Sueciae, 1652-6880 ; 2017:11
ISBN 978-91-576-8795-1
eISBN 978-91-576-8796-8
[Doctoral thesis]

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Managing the forests is important for the provision of timber, environmental benefits,recreation, etc., to both forest owners and society. In order to manage the forests,
information on the state of the forest is required. However, forest information is not free from errors, that is, it suffers from some degree of uncertainty leading to suboptimal decisions and potential economic losses.

Remote sensing data are frequently used to acquire forest information. In this thesis the use of remote sensing techniques was elaborated further. In Paper I airborne laser
scanning (ALS) data was used to estimate stand stem diameter distributions and, thus improving forest information compared to traditional stand mean values. In Paper II ALS data was used first as auxiliary data when using the local pivotal method (LPM) and a micro-stand approach for locating reference plots. Second, ALS data was used as auxiliary data when imputing forest information to evaluation plots. The combined approached showed a potential for improvement and has the potential to be a competitive method when considering cost efficiency.

Improving forest information can be done through acquiring new information or
through assimilating new with old information. Paper III presents the benefits of data assimilation process. It provides more accurate estimates as compared to traditional
methods and it also provides the associated uncertainty. Paper IV presents a visual illustration method to ncorporate estimates of uncertainty in forest planning. The method is applicable in current decision support systems (DSSs) for use in stand level decision making situations.

The results of this thesis may also shed light on the reason why uncertainty so far is typically ignored in forest planning. Taking the results and the potential benefits of this thesis forward could lead to the development of new DSSs considering uncertainty.
Moreover, the data assimilation process should be further investigated, as it is a promising framework for assimilating different sources of information into a single
useful source of information for forest planners and decision makers.

Authors/Creators:Saad, Rami
Title:The effect of forest information quality on the planning and decision process in forestry
Series Name/Journal:Acta Universitatis Agriculturae Sueciae
Year of publishing :February 2017
Depositing date:24 January 2017
Number of Pages:47
ISaad, R., Wallerman, J. & Lämås, T.(2015). Estimating stem diameter distributions from airborne laser scanning data and their effects on long term forest management planning. Scandinavian Journal of Forest Research 30(2):186-196.
IISaad, R., Lämås, T., Wallerman, J. & Holmström, J.(2016) Local pivotal method sampling design combined with micro stands utilizing airborne laser scanning data in a long term forest management planning setting. Silva Fennica vol. 50 no. 2 article id 1414.
IIISaad, R., Eyvindson, K., Gong, P., Lämås, T. & Ståhl, G. (2017).Potential of using data assimilation to support forest planning. Canadian Journal of Forest Research. DOI: 10.1139/cjfr-2016-0439.
IVEyvindson, K., Saad, R., & Eriksson, L.O. Incorporating stand level risk management options into forest decision support systems (submitted manuscript)
Place of Publication:Umeå
Publisher:Department of Forest Resource Management, Swedish University of Agricultural Sciences
ISBN for printed version:978-91-576-8795-1
ISBN for electronic version:978-91-576-8796-8
Publication Type:Doctoral thesis
Full Text Status:Public
Agris subject categories.:E Economics, development, and rural sociology > E10 Agricultural economics and policies
K Forestry > K01 Forestry - General aspects
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Agrovoc terms:forest management planning, decision support systems, methods, remote sensing, stochastic models
Keywords:forest management planning, suboptimal loss, Heureka, decision support system, local pivotal method (LPM), segmentation, most similar neighbor (MSN) imputation, remote sensing, uncertainty, data assimilation, risk preferences, stochastic optimization
Permanent URL:
ID Code:13994
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: Rami Rami Saad
Deposited On:26 Jan 2017 08:59
Metadata Last Modified:10 Sep 2020 13:41

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