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Research article2017Peer reviewedOpen access

Potential of using data assimilation to support forest planning

Saad, Rami; Eyvindson, Kyle; Gong, Peichen; Lamas, Tomas; Stahl, Goran


Uncertainty in forest information typically results in economic and ecological losses as a consequence of suboptimal management decisions. Several techniques have been proposed to handle such uncertainties. However, these techniques are often complex and costly. Data assimilation (DA) has recently been advocated as a tool that may reduce the uncertainty, thereby improving the quality of forest planning results. It offers an opportunity to make use of all new sources of information in a systematic way and thus provides more accurate and up-to-date information to forest planning. In this study, we refer to literature on handling uncertainties in forest planning, as well as related literature from other scientific fields, to assess the potential benefits of using DA in forest planning. We identify five major potential benefits: (i) the accuracy of the information will be improved; (ii) the information will be kept up to date; (iii) the DA process will provide information with estimated accuracy; (iv) stochastic decision making can be applied whereby the accuracy of the information can be utilized in the decision making process; and (v) DA data allows for the analysis of optimal data acquisition decisions.


uncertainty; suboptimal loss; remote sensing; Bayesian statistics; stochastic optimization

Published in

Canadian Journal of Forest Research
2017, Volume: 47, number: 5, pages: 690-695