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Articles by Arto Haara

Category: Research article

article id 10089, category Research article
Arto Haara, Annika Kangas, Sakari Tuominen. (2019). Economic losses caused by tree species proportions and site type errors in forest management planning. Silva Fennica vol. 53 no. 2 article id 10089. https://doi.org/10.14214/sf.10089
Highlights: Errors in tree species proportions caused more economic losses for forest owners than site type errors; Economic losses due to sub-optimal treatments were observed from 26.5% to 31.7% of plots, depending on the remote sensing data set used; Even with the most accurate remote sensing data set, namely ALS data set, NPV losses were on average 124.4 € ha–1 with 3% interest rate.

The aim of this study was to estimate economic losses, which are caused by forest inventory errors of tree species proportions and site types. Our study data consisted of ground truth data and four sets of erroneous tree species proportions. They reflect the accuracy of tree species proportions in four remote sensing data sets, namely 1) airborne laser scanning (ALS) with 2D aerial image, 2) 2D aerial image, 3) 3D and 2D aerial image data together and 4) satellite data. Furthermore, our study data consisted of one simulated site type data set. We used the erroneous tree species proportions to optimise the timing of forest harvests and compared that to the true optimum obtained with ground truth data. According to the results, the mean losses of Net Present Value (NPV) because of erroneous tree species proportions at an interest rate of 3% varied from 124.4 € ha–1 to 167.7 € ha–1. The smallest losses were observed using tree species proportions predicted using ALS data and largest using satellite data. In those stands, respectively, in which tree species proportion errors actually caused economic losses, they were 468 € ha–1 on average with tree species proportions based on ALS data. In turn, site type errors caused only small losses. Based on this study, accurate tree species identification seems to be very important with respect to operational forest inventory.

  • Haara, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: arto.haara@luke.fi (email)
  • Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID: https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
  • Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 2, FI-00791 Helsinki, Finland ORCID ID: https://orcid.org/0000-0001-5429-3433 E-mail: sakari.tuominen@luke.fi
article id 219, category Research article
Arto Haara, Pekka Leskinen. (2009). The assessment of the uncertainty of updated stand-level inventory data. Silva Fennica vol. 43 no. 1 article id 219. https://doi.org/10.14214/sf.219
Predictions of growth and yield are essential in forest management planning. Growth predictions are usually obtained by applying complex simulation systems, whose accuracy is difficult to assess. Moreover, the computerised updating of old inventory data is increasing in the management of forest planning systems. A common characteristic of prediction models is that the uncertainties involved are usually not considered in the decision-making process. In this paper, two methods for assessing the uncertainty of updated forest inventory data were studied. The considered methods were (i) the models of observed errors and (ii) the k-nearest neighbour method. The derived assessments of uncertainty were compared with the empirical estimates of uncertainty. The practical utilisation of both methods was considered as well. The uncertainty assessments of updated stand-level inventory data using both methods were found to be feasible. The main advantages of the two studied methods include that bias as well as accuracy can be assessed.
  • Haara, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: arto.haara@joensuu.fi (email)
  • Leskinen, Finnish Environment Institute, Research Programme for Production and Consumption, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
article id 218, category Research article
Md. Nurul Islam, Mikko Kurttila, Lauri Mehtätalo, Arto Haara. (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 Fennica vol. 43 no. 1 article id 218. https://doi.org/10.14214/sf.218
Accurate inventory data are required for ensuring optimal net return on investment from the forest. Erroneous data can lead to the formulation of a non-optimal plan that can cause inoptimality losses. Little is known of the effect of using erroneous stand inventory data in preparing holding-level forest plans. This study reports on an approach for analyzing such inoptimality losses. Furthermore, inoptimality losses caused by measurement errors in the basal area of the dominated tree species were investigated in a case study. Based on the inventory data including routine measurements by 67 measurers, four measurer groups were created with different measurement error profiles for the basal area of the dominated tree species. This was followed by measurement error simulations for each group and by adding these to the accurate control inventory data to create erroneous data of different error profiles. Three different forest plans were then constructed by using erroneous data of each group. The plans were then analyzed and compared with plans based on correct data. The effect of measurement errors on the net present value from the whole planning period, and on the amount of remaining growing stock at the end of planning period, were analyzed and utilized in calculating the inoptimality losses. It was concluded that even errors involving dominated tree species can cause significant changes in the holding-level forest plans.
  • Islam, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail: nurul.islam@joensuu.fi (email)
  • Kurttila, Finnish Forest Research Institute, Joensuu Research Unit, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Mehtätalo, University of Helsinki, Dept. of Forest Resource Management, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Haara, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
article id 486, category Research article
Arto Haara. (2003). Comparing simulation methods for modelling the errors of stand inventory data. Silva Fennica vol. 37 no. 4 article id 486. https://doi.org/10.14214/sf.486
Forest management planning requires information about the uncertainty inherent in the available data. Inventory data, including simulated errors, are infrequently utilised in forest planning studies for analysing the effects of uncertainty on planning. Usually the errors in the source material are ignored or not taken into account properly. The aim of this study was to compare different methods for generating errors into the stand-level inventory data and to study the effect of erroneous data on the calculation of specieswise and standwise inventory results. The material of the study consisted of 1842 stands located in northern Finland and 41 stands located in eastern Finland. Stand-level ocular inventory and checking inventory were carried out in all study stands by professional surveyors. In simulation experiments the methods considered for error generation were the 1nn-method, the empirical errors method and the Monte Carlo method with log-normal and multivariate log-normal error distributions. The Monte Carlo method with multivariate error distributions was found to be the most flexible simulation method. This method produced the required variation and relations between the errors of the median basal area tree characteristics. However, if the reference data are extensive the 1nn-method, and in certain conditions also the empirical errors method, offer a useful tool for producing error structures which reflect reality.
  • Haara, Finnish Forest Research Institute, Joensuu Research Centre, P.O.Box 68, FIN-80101 Joensuu, Finland ORCID ID:E-mail: arto.haara@metla.fi (email)

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