Annika Kangas, Mari Myllymäki, Lauri Mehtätalo. (2023). Understanding uncertainty in forest resources maps. Silva Fennica vol. 57 no. 2 article id 22026. https://doi.org/10.14214/sf.22026

Keywords: autocorrelation; ensemble modelling; kriging; quantile; random forest; sequential Gaussian simulation

Highlights: Forest resources maps without uncertainty assessment may lead to false impression of precision; Suitable tools for visualization of map products are lacking; Kriging method provided accurate uncertainty assessment for pixel-level predictions; Quantile random forest algorithm slightly underestimated the pixel-level uncertainties; With simulation it is possible to assess the uncertainty also for landscape-level characteristics.

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Maps of forest resources and other ecosystem services are needed for decision making at different levels. However, such maps are typically presented without addressing the uncertainties. Thus, the users of the maps have vague or no understanding of the uncertainties and can easily make wrong conclusions. Attempts to visualize the uncertainties are also rare, even though the visualization would be highly likely to improve understanding. One complication is that it has been difficult to address the predictions and their uncertainties simultaneously. In this article, the methods for addressing the map uncertainty and visualize them are first reviewed. Then, the methods are tested using laser scanning data with simulated response variable values to illustrate their possibilities. Analytical kriging approach captured the uncertainty of predictions at pixel level in our test case, where the estimated models had similar log-linear shape than the true model. Ensemble modelling with random forest led to slight underestimation of the uncertainties. Simulation is needed when uncertainty estimates are required for landscape level features more complicated than small areas.

Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Myllymäki, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0002-2713-7088 E-mail: mari.myllymaki@luke.fi
Mehtätalo, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, 80101 Joensuu, Finland https://orcid.org/0000-0002-8128-0598 E-mail: lauri.mehtatalo@luke.fi

article id 10217, category Research article

Xingji Jin, Timo Pukkala, Fengri Li, Lihu Dong. (2019). Developing growth models for tree plantations using inadequate data – a case for Korean pine in Northeast China. Silva Fennica vol. 53 no. 4 article id 10217. https://doi.org/10.14214/sf.10217

Keywords: Pinus koraiensis; optimization-based modeling; quantile regression; self-thinning

Highlights: The permanent sample plots of Chinese plantation trees have not been designed for producing data for growth modeling; We used various methods to deal with the inadequacies of sample plot data; Optimization was used to fit diameter increment and survival models using data with varying measurement intervals and tree identification errors; Quantile regression was used to model self-thinning limit.

Abstract | Full text in HTML | Full text in PDF | Author Info

Korean pine (Pinus koraiensis Siebold & Zucc.) is economically the most important tree species in northeast China. Korean pine plantations are established and managed for the production of timber and seeds. Despite the importance of the species, few models have been developed for the comparison of alternative management schedules. Model development is affected by the fact that permanent sample plots and thinning experiments have not been designed and managed for modeling purposes. The permanent sample plots include few non-thinned plots, and weak trees are removed in thinning treatments, leading to low mortality rate. Moreover, the measurement interval is irregular. This study used optimization-based modeling approach in tree-level diameter increment and survival modeling to deal with the above problems. Models for self-thinning limit were developed to alleviate the problem of underestimated mortality arising from the features of the data. In addition, improved site index and individual-tree height models were developed. The model of Lundqvist and Korf was used as the site index model and the model proposed by Schumacher as the height model. Quantile regression was used to model the maximum stand basal area and maximum number of trees as a function of mean tree diameter and site index. Tree diameter, stand basal area, basal area in larger trees and site index were used as the predictors of diameter increment and tree survival. The models developed in this study constitute a model set that is suitable for simulation and optimization studies. The models produced simulation results that correspond to measured stand development.

Jin, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China http://orcid.org/0000-0003-2971-2709 E-mail: xingji_jin@163.com
Pukkala, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China; University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: timo.pukkala@uef.fi
Li, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China http://orcid.org/0000-0002-4058-769X E-mail: fengrili@126.com
Dong, Key Laboratory of Sustainable Forest Ecosystem Management - Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, People’s Republic of China http://orcid.org/0000-0002-3985-9475 E-mail: ldonglihu2006@163.com

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