Abstract

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.

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

Published in

Acta Universitatis Agriculturae Sueciae
2017, number: 2017:11
ISBN: 978-91-576-8795-1, eISBN: 978-91-576-8796-8
Publisher: Department of Forest Resource Management, Swedish University of Agricultural Sciences

SLU Authors

• Saad, Rami

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Forest Science


Permanent link to this page (URI)

https://res.slu.se/id/publ/79524