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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles by Annika Kangas

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 9923, category Research article
Annika Kangas, Terje Gobakken, Stefano Puliti, Marius Hauglin, Erik Naesset. (2018). Value of airborne laser scanning and digital aerial photogrammetry data in forest decision making. Silva Fennica vol. 52 no. 1 article id 9923. https://doi.org/10.14214/sf.9923
Highlights: Airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) are nearly equally valuable for harvest scheduling decisions even though ALS data is more precise; Large underestimates of stand volume are most dangerous errors for forest owner because of missed cutting probabilities; Relative RMSE of stand volume and the mean volume in a test area explain 77% of the variation between the expected losses due to errors in the data in the published studies; Increasing the relative RMSE of volume by 1 unit, increased the losses in average by 4.4 € ha–1.

Airborne laser scanning (ALS) has been the main method for acquiring data for forest management planning in Finland and Norway in the last decade. Recently, digital aerial photogrammetry (DAP) has provided an interesting alternative, as the accuracy of stand-based estimates has been quite close to that of ALS while the costs are markedly smaller. Thus, it is important to know if the better accuracy of ALS is worth the higher costs for forest owners. In many recent studies, the value of forest inventory information in the harvest scheduling has been examined, for instance through cost-plus-loss analysis. Cost-plus-loss means that the quality of the data is accounted for in monetary terms through calculating the losses due to errors in the data in the forest management planning context. These costs are added to the inventory costs. In the current study, we compared the losses of ALS and DAP at plot level. According to the results, the data produced using DAP are as good as data produced using ALS from a decision making point of view, even though ALS is slightly more accurate. ALS is better than DAP only if the data will be used for more than 15 years before acquiring new data, and even then the difference is quite small. Thus, the increased errors in DAP do not significantly affect the results from a decision making point of view, and ALS and DAP data can be equally well recommended to the forest owners for management planning. The decision of which data to acquire, can thus be made based on the availability of the data on first hand and the costs of acquiring it on the second hand.

  • Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80170 Joensuu, Finland ORCID ID:E-mail: annika.kangas@luke.fi (email)
  • Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway ORCID ID:E-mail: terje.gobakken@nmbu.no
  • Puliti, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway ORCID ID:E-mail: stefano.puliti@nibio.no
  • Hauglin, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway ORCID ID:E-mail: marius.hauglin@nmbu.no
  • Naesset, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway ORCID ID:E-mail: erik.naesset@nmbu.no
article id 7743, category Research article
Sakari Tuominen, Timo Pitkänen, Andras Balazs, Annika Kangas. (2017). Improving Finnish Multi-Source National Forest Inventory by 3D aerial imaging. Silva Fennica vol. 51 no. 4 article id 7743. https://doi.org/10.14214/sf.7743
Highlights: 3D aerial imaging provides a feasible method for estimating forest variables in the form of thematic maps in large area inventories; Photogrammetric 3D data based on aerial imagery that was originally acquired for orthomosaic production was tested in estimating stand variables; Photogrammetric 3D data highly improved the accuracy of forest estimates compared to traditional 2D remote sensing imagery.

Optical 2D remote sensing techniques such as aerial photographing and satellite imaging have been used in forest inventory for a long time. During the last 15 years, airborne laser scanning (ALS) has been adopted in many countries for the estimation of forest attributes at stand and sub-stand levels. Compared to optical remote sensing data sources, ALS data are particularly well-suited for the estimation of forest attributes related to the physical dimensions of trees due to its 3D information. Similar to ALS, it is possible to derive a 3D forest canopy model based on aerial imagery using digital aerial photogrammetry. In this study, we compared the accuracy and spatial characteristics of 2D satellite and aerial imagery as well as 3D ALS and photogrammetric remote sensing data in the estimation of forest inventory variables using k-NN imputation and 2469 National Forest Inventory (NFI) sample plots in a study area covering approximately 5800 km2. Both 2D data were very close to each other in terms of accuracy, as were both the 3D materials. On the other hand, the difference between the 2D and 3D materials was very clear. The 3D data produce a map where the hotspots of volume, for instance, are much clearer than with 2D remote sensing imagery. The spatial correlation in the map produced with 2D data shows a lower short-range correlation, but the correlations approach the same level after 200 meters. The difference may be of importance, for instance, when analyzing the efficiency of different sampling designs and when estimating harvesting potential.

  • Tuominen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland ORCID ID:E-mail: sakari.tuominen@luke.fi (email)
  • Pitkänen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland ORCID ID:E-mail: timo.p.pitkanen@luke.fi
  • Balazs, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland ORCID ID:E-mail: andras.balazs@luke.fi
  • Kangas, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: Annika.Kangas@luke.fi
article id 1087, category Research article
Ilkka Korpela, Lauri Mehtätalo, Lauri Markelin, Anne Seppänen, Annika Kangas. (2014). Tree species identification in aerial image data using directional reflectance signatures. Silva Fennica vol. 48 no. 3 article id 1087. https://doi.org/10.14214/sf.1087
Highlights: Multispectral reflectance data showed a strong and spectrally correlated tree effect; There was no gain in species classification from using species-specific differences of directional reflectance in real data and only a marginal improvement in simulated data; The directional signatures extracted in multiple images are obscured by the intrinsic within-species variation, correlated observations and inherent reflectance calibration errors.
Tree species identification using optical remote sensing is challenging. Modern digital photogrammetric cameras enable radiometrically quantitative remote sensing and the estimation of reflectance images, in which the observations depend largely on the reflectance properties of targets. Previous research has shown that there are species-specific differences in how the brightness observed changes when the viewing direction in an aerial image is altered. We investigated if accounting for such directional signatures enhances species classification, using atmospherically corrected, real and simulated multispectral Leica ADS40 line-camera data. Canopy in direct and diffuse illumination were differentiated and species-specific variance-covariance structures were analyzed in real reflectance data, using mixed-effects modeling. Species classification simulations aimed at elucidating the level of accuracy that can be achieved by using images of different quality, number and view-illumination geometry. In real data, a substantial variance component was explained by tree effect, which demonstrates that observations from a tree correlate between observation geometries as well as spectrally. Near-infrared band showed the strongest tree effect, while the directionality was weak in that band. The gain from directional signatures was insignificant in real data, while simulations showed a potential gain of 1–3 percentage points in species classification accuracy. The quality of reflectance calibration was found to be important as well as the image acquisition geometry. We conclude that increasing the number of image observations cancels out random observation noise and reflectance calibration errors, but fails to eliminate the tree effect and systematic calibration inaccuracy. Directional reflectance constitutes a marginal improvement in tree species classification.
  • Korpela, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland ORCID ID:E-mail: ilkka.korpela@helsinki.fi (email)
  • Mehtätalo, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: lauri.mehtatalo@uef.fi
  • Markelin, Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, FI-02431 Masala, Finland ORCID ID:E-mail: lauri.markelin@fgi.fi
  • Seppänen, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: anne.seppanen@arbonaut.com
  • Kangas, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland ORCID ID:E-mail: annika.kangas@helsinki.fi
article id 55, category Research article
Antti Mäkinen, Annika Kangas, Mikko Nurmi. (2012). Using cost-plus-loss analysis to define optimal forest inventory interval and forest inventory accuracy. Silva Fennica vol. 46 no. 2 article id 55. https://doi.org/10.14214/sf.55
In recent years, optimal inventory accuracy has been analyzed with a cost-plus-loss methodology, where the total costs of inventory include both the measurement costs and the losses from the decisions based on the collected information. Losses occur, when the inaccuracies in the data lead to sub-optimal decisions. In almost all cases, it has been assumed that the accuracy of the once collected data remains the same throughout the planning period, and the period has been from 10 up to 100 years. In reality, the quality of the data deteriorates in time, due to errors in the predicted growth. In this study, we carried out a cost-plus-loss analysis accounting for the errors in (stand-level) growth predictions of basal area and dominant height. In addition, we included the inventory errors of these two variables with several different levels of accuracy, and costs of inventory with several different assumptions of cost structure. Using the methodology presented in this study, we could calculate the optimal inventory interval (life-span of data) minimizing the total costs of inventory and losses through the 30-year planning period. When the inventory costs only to a small extent depended on the accuracy, the optimal inventory period was 5 years and optimal accuracy RMSE 0%. When the costs more and more heavily depended on the accuracy, the optimal interval turned out to be either 10 or 15 years, and the optimal accuracy reduced from RMSE 0% to RMSE 20%. By increasing the accuracy of the growth models, it was possible to reduce the inventory accuracy or lengthen the interval, i.e. obtain clear savings in inventory costs.
  • Mäkinen, Simosol Oy, Rautatietori 4, FI-11130 Riihimäki, Finland ORCID ID:E-mail: antti.makinen@simosol.fi (email)
  • Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland ORCID ID:E-mail:
  • Nurmi, University of Helsinki, Department of Forest Sciences, Helsinki, Finland ORCID ID:E-mail:
article id 69, category Research article
Tarja Wallenius, Risto Laamanen, Jussi Peuhkurinen, Lauri Mehtätalo, Annika Kangas. (2012). Analysing the agreement between an Airborne Laser Scanning based forest inventory and a control inventory – a case study in the state owned forests in Finland. Silva Fennica vol. 46 no. 1 article id 69. https://doi.org/10.14214/sf.69
Airborne laser scanning based forest inventories have recently shown to produce accurate results. However, the accuracy varies according to the test area and used methodology and therefore, an unambiguous and practical quality assessment will be needed as a part of each inventory project. In this study, the accuracy of an ALS inventory was evaluated with a field sampling based control inventory. The agreement between the ALS inventory and the control inventory was analysed with four methods: 1) root mean square error (RMSE) and bias, 2) scatter plots with 95% confidence intervals, 3) Bland-Altman plots and 4) tolerance limits within Bland-Altman plots. Each method has its own special features which have to be taken into account when the agreement is analysed. The pre-defined requirements of the ALS inventory were achieved. A simplified control inventory approach with a slightly narrower focus is proposed to be used in the future. The Bland-Altman plots with the tolerance limits are proposed to be used in quality assessments of operational ALS inventories. Further studies to improve the efficiency of quality assessment are needed.
  • Wallenius, Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland ORCID ID:E-mail: tarja.wallenius@metsa.fi (email)
  • Laamanen, Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland ORCID ID:E-mail:
  • Peuhkurinen, Oy Arbonaut Ltd, Helsinki, Finland ORCID ID:E-mail:
  • Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland ORCID ID:E-mail:
  • Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland ORCID ID:E-mail:
article id 101, category Research article
Risto Laamanen, Annika Kangas. (2011). Large-scale forest owner’s information needs in operational planning of timber harvesting - some practical views in Metsähallitus, Finnish state-owned enterprise. Silva Fennica vol. 45 no. 4 article id 101. https://doi.org/10.14214/sf.101
Metsähallitus in Finland is a state enterprise that manages about 3.5 million hectares of productive commercial state-owned forest land. Metsähallitus has a forest management planning system which uses information stored in a GIS-based forest resource information system. The information on forest resources is currently collected using a standwise inventory system with ocular estimation of stand characteristics. New promising inventory methods based on laser scanning have been introduced. Before taking a new system into use, the information needs of Metsähallitus must be analysed. In this study, information needs in operational harvest planning have been analysed with a qualitative approach. A total of eight team leaders in the forestry business unit were interviewed, six of them representing the process responsible for the operational harvest planning and two representing the process responsible for the harvest and deliveries. Based on the study, two main decision making points with different information needs were confirmed. The first decision making point is related to finding the areas potential for immediate or near future harvesting. Here, geographical information on the need for the treatment as well as rough information on the harvestable volume is needed. In the second decision making point, a final decision of sites to be harvested is made with rather intensive field work. Precise delineations of the treatment are needed as well as good estimates of volumes of different timber assortments. When considering a new inventory system it is justified to consider how much of the information needs in these decision making points can be covered. Two different approaches are proposed for further analysis. The interviews revealed a need for a more structured tactical planning system. Some of the findings of this study – especially the decision making points and information needs in them – may be transferable to other large-scale forest owners.
  • Laamanen, Metsähallitus, Vantaa, Finland ORCID ID:E-mail: risto.laamanen@metsa.fi (email)
  • Kangas, Metsähallitus, Vantaa, Finland ORCID ID:E-mail:
article id 100, category Research article
Annika Kangas, Lauri Mehtätalo, Antti Mäkinen, Kalle Vanhatalo. (2011). Sensitivity of harvest decisions to errors in stand characteristics. Silva Fennica vol. 45 no. 4 article id 100. https://doi.org/10.14214/sf.100
In forest planning, the decision maker chooses for each stand a treatment schedule for a predefined planning period. The choice is based either on optimization calculations or on silvicultural guidelines. Schedules for individual stands are obtained using a growth simulator, where measured stand characteristics such as the basal area, mean diameter, site class and mean height are used as input variables. These characteristics include errors, however, which may lead to incorrect decisions. In this study, the aim is to study the sensitivity of harvest decisions to errors in a dataset of 157 stands. Correct schedules according to silvicultural guidelines were first determined using error-free data. Different amounts of errors were then generated to the stand-specific characteristics, and the treatment schedule was selected again using the erroneous data. The decision was defined as correct, if the type of harvest in these two schedules were similar, and if the timings deviated at maximum ±2 for thinning and ±3 years for clear-cut. The dependency of probability of correct decisions on stand characteristics and the degree of errors was then modelled. The proposed model can be used to determine the required level of measurement accuracy for each characteristics in different kinds of stands, with a given accuracy requirement for the timing of treatments. This information can further be utilized in selecting the most appropriate inventory method.
  • Kangas, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: annika.kangas@helsinki.fi (email)
  • Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland ORCID ID:E-mail:
  • Mäkinen, Simosol Oy, Riihimäki, Finland ORCID ID:E-mail:
  • Vanhatalo, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
article id 111, category Research article
Ilona Pietilä, Annika Kangas, Antti Mäkinen, Lauri Mehtätalo. (2010). Influence of growth prediction errors on the expected losses from forest decisions. Silva Fennica vol. 44 no. 5 article id 111. https://doi.org/10.14214/sf.111
In forest planning, forest inventory information is used for predicting future development of forests under different treatments. Model predictions always include some errors, which can lead to sub-optimal decisions and economic loss. The influence of growth prediction errors on the reliability of projected forest variables and on the treatment propositions have previously been examined in a few studies, but economic losses due to growth prediction errors is an almost unexplored subject. The aim of this study was to examine how the growth prediction errors affected the expected losses caused by incorrect harvest decisions, when the inventory interval increased. The growth models applied in the analysis were stand-level growth models for basal area and dominant height. The focus was entirely on the effects of growth prediction errors, other sources of uncertainty being ignored. The results show that inoptimality losses increased with the inventory interval. Average relative inoptimality loss was 3.3% when the inventory interval was 5 years and 11.6% when it was 60 years. Average absolute inoptimality loss was 230 euro ha–1 when the inventory interval was 5 years and 860 euro ha–1 when it was 60 years. The average inoptimality losses varied between development classes, site classes and main tree species.
  • Pietilä, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: annika.kangas@helsinki.fi (email)
  • Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
article id 155, category Research article
Minna Räty, Annika Kangas. (2010). Segmentation of model localization sub-areas by Getis statistics. Silva Fennica vol. 44 no. 2 article id 155. https://doi.org/10.14214/sf.155
Models for large areas (global models) are often biased in smaller sub-areas, even when the model is unbiased for the whole area. Localization of the global model removes the local bias, but the problem is to find homogenous sub-areas in which to localize the function. In this study, we used the eCognition Professional 4.0 (later versions called Definies Pro) segmentation process to segment the study area into homogeneous sub-areas with respect to residuals of the global model of the form height and/or local Getis statistics calculated for the residuals, i.e., Gi*-indices. The segmentation resulted in four different rasters: 1) residuals of the global model, 2) the local Gi*-index, and 3) residuals and the local Gi*-index weighted by the inverse of the variance, and 4) without weighting. The global model was then localized (re-fitted) for these sub-areas. The number of resulting sub-areas varied from 4 to 366. On average, the root mean squared errors (RMSEs) were 3.6% lower after localization than the global model RMSEs in sub-areas before localization. However, the localization actually increased the RMSE in some sub-areas, indicating the sub-area were not appropriate for local fitting. For 56% of the sub-areas, coordinates and distance from coastline were not statistically significant variables, in other words these areas were spatially homogenous. To compare the segmentations, we calculated an aggregate standard error of the RMSEs of the single sub-areas in the segmentation. The segmentations in which the local index was present had slightly lower standard errors than segmentations based on residuals.
  • Räty, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail: minna.s.raty@helsinki.fi (email)
  • Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
article id 467, category Research article
Annika Kangas, Ruut Haapakoski, Liisa Tyrväinen. (2008). Integrating place-specific social values into forest planning – Case of UPM-Kymmene forests in Hyrynsalmi, Finland. Silva Fennica vol. 42 no. 5 article id 467. https://doi.org/10.14214/sf.467
In participatory forest management planning, the perceived values of local inhabitants concerning the area under planning are collected. The results may, however, depend on the methods used. In this study, values of residents of Hyrynsalmi municipality concerning the nearby forests owned by UPM-Kymmene Ltd. were evaluated with a questionnaire consisting of general value questions and mapping of social values of forests. The data was collected from a public meeting and from a mailed survey from randomly sampled people and from members of municipal council. The aims of the study were to 1) test the social value mapping method in commercial forests in a rural-urban interface as well as to examine the benefits and drawbacks 2) in place-specific and non-specific data collection, and 3) in different data collection methods, from the viewpoint of forest management planning. We noted that while all respondents can claim to represent local values, different data collection methods produced statistically significantly different local values. This needs to be accounted for when planning a participatory process. In operational forest planning, place-specific information is more useful than questions concerning the general values, while the latter may help in defining forest policy goals. The social values mapping method is also relatively easy for the participants. However, in the studied case about one fifth of the area was delineated by the participants per each positive value. The answers were quite scattered, suggesting that most of the area had some social values for local people. This indicates that utilising a social values mapping method in planning needs further development in rural areas, where distinctive patches can not be easily detected.
  • Kangas, University of Helsinki, Department of Forest Management, P.O. Box 27, FI-00014 University of Helsinki ORCID ID:E-mail: annika.kangas@helsinki.fi (email)
  • Haapakoski, University of Helsinki, Department of Forest Management, P.O. Box 27, FI-00014 University of Helsinki ORCID ID:E-mail:
  • Tyrväinen, Finnish Forest Research Institute, P.O. Box 16, FI-96301 Rovaniemi, Finland. ORCID ID:E-mail:
article id 282, category Research article
Annika Kangas, Lauri Mehtätalo, Matti Maltamo. (2007). Modelling percentile based basal area weighted diameter distribution. Silva Fennica vol. 41 no. 3 article id 282. https://doi.org/10.14214/sf.282
In percentile method, percentiles of the diameter distribution are predicted with a system of models. The continuous empirical diameter distribution function is then obtained by interpolating between the predicted values of percentiles. In Finland, the distribution is typically modelled as a basal-area weighted distribution, which is transformed to a traditional density function for applications. In earlier studies it has been noted that when calculated from the basal-area weighted diameter distribution, the density function is decreasing in most stands, especially for Norway spruce. This behaviour is not supported by the data. In this paper, we investigate the reasons for the unsatisfactory performance and present possible solutions for the problem. Besides the predicted percentiles, the problems are due to implicit assumptions of diameter distribution in the system. The effect of these assumptions can be somewhat lessened with simple ad-hoc methods, like increasing new percentiles to the system. This approach does not, however, utilize all the available information in the estimation, namely the analytical relationships between basal area, stem number and diameter. Accounting for these, gives further possibilities for improving the results. The results show, however, that in order to achieve further improvements, it would be recommendable to make the implicit assumptions more realistic. Furthermore, height variation within stands seems to have an important contribution to the uncertainty of some forest characteristics, especially in the case of sawnwood volume.
  • Kangas, Department of Forest Resources Management, P.O.Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Mehtätalo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
article id 333, category Research article
Lauri Mehtätalo, Matti Maltamo, Annika Kangas. (2006). The use of quantile trees in the prediction of the diameter distribution of a stand. Silva Fennica vol. 40 no. 3 article id 333. https://doi.org/10.14214/sf.333
This study deals with the prediction of the basal area diameter distribution of a stand without using a complete sample of diameters from the target stand. Traditionally, this problem has been solved by either the parameter recovery method or the parameter prediction method. This study uses the parameter prediction method and the percentile based diameter distribution with a recent development that makes it possible to improve these predictions by using sample order statistics. A sample order statistic is a tree whose diameter and rank at the plot are known, and is referred to in this paper as a quantile tree. This study tested 13 different strategies for selection of the quantile trees from among the trees of horizontal point sample plots, and compared them with respect to RMSE and the bias of four criterion variables in a dataset of 512 stands. The sample minimum was found to be the most promising alternative with respect to RMSE, even though it introduced a rather large amount of bias in the criterion variables. Other good and less biased alternatives are the second and third smallest trees and the tree closest to the plot centre. The use of minimum is recommended for practical inventories because its rank is probably easiest to determine correctly in the field.
  • Mehtätalo, Yale School of Forestry and Environmental Studies, 205 Prospect Street, New Haven, CT 06511, USA ORCID ID:E-mail: lauri.mehtatalo@metla.fi (email)
  • Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Kangas, University of Helsinki, Department of Forest Resources Management, P.O.Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
article id 387, category Research article
Sanna Laukkanen, Teijo Palander, Jyrki Kangas, Annika Kangas. (2005). Evaluation of the multicriteria approval method for timber-harvesting group decision support. Silva Fennica vol. 39 no. 2 article id 387. https://doi.org/10.14214/sf.387
The decision support methods most often used in timber-harvesting planning are based on a single criterion. In this study, a voting-theory-based method called multicriteria approval (MA) is introduced to the group decision support of timber-harvesting. The use of voting methods alleviates the problems caused by the multitude of decision objectives involved in forestry decision-making and by the poor quality of information concerning both the preferences of decision-makers and the evaluation of decision alternatives with respect to the objectives often faced in practical timber-harvesting planning. In the case study, the tactical forest management plan of a forest holding jointly owned by three people was specified at the operative timber-harvesting level. The task was to find the best actual operative alternatives for the harvesting that had been proposed in the tactical plan. These timber-harvesting alternatives were combinations of treatment, timber-harvesting system and the timing of logging. Forest owners established multiple criteria under which the alternatives were evaluated. Two versions of MA were tested, one of them based on individual decision analyses and other one based on a composite analysis. The first was markedly modified from the original MA, combining properties of MA and Borda count voting. The other was an original MA with the order of importance for criteria estimated either using Borda count or cumulative voting. The results of the tested MA versions produced were very similar to each other. MA was found to be a useful tool for the group decision support of timber-harvesting.
  • Laukkanen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: sanna.laukkanen@joensuu.fi (email)
  • Palander, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Kangas, UPM-Kymmene Forest, P.O. Box 32, FI-37601 Valkeakoski, Finland ORCID ID:E-mail:
  • Kangas, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
article id 522, category Research article
Annika Kangas, Matti Maltamo. (2002). Anticipating the variance of predicted stand volume and timber assortments with respect to stand characteristics and field measurements. Silva Fennica vol. 36 no. 4 article id 522. https://doi.org/10.14214/sf.522
Several models and/or several variable combinations could be used to predict the diameter distribution of a stand. Typically, a fixed model and a fixed variable combination is used in all conditions. The calibration procedure, however, makes it possible to choose the measurement combination from among many possibilities, although the model used is fixed. In this study, the usefulness of utilizing additional stand characteristics for calibrating the predicted diameter distribution is examined. Nine measurement strategies were tested in predicting the total stand volume, sawlog volume and pulpwood volume. The observed errors of these variables under each strategy were modeled as a function of basal area, basal area median diameter and number of stems. The models were estimated in three steps. First, an Ordinary Least Squares (OLS) model was fitted to the observed errors. Then, a variance function was estimated using the OLS residuals. Finally, a weighted Seemingly Unrelated Regression (SUR) analysis was used to model the observed errors, using the estimated variance functions as weights. The estimated models can be used to anticipate the precision and accuracy of predicted volume characteristics for each stand with different variable combinations and, consequently, to choose the best measurement combination in different stands.
  • Kangas, University of Helsinki, Dept. of Forest Resources Management, P.O. Box 27, 00014 University of Helsinki, Finland ORCID ID:E-mail: annika.kangas@helsinki.fi (email)
  • Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland ORCID ID:E-mail:
article id 580, category Research article
Susanna Sironen, Annika Kangas, Matti Maltamo, Jyrki Kangas. (2001). Estimating individual tree growth with the k-nearest neighbour and k-Most Similar Neighbour methods. Silva Fennica vol. 35 no. 4 article id 580. https://doi.org/10.14214/sf.580
The purpose of this study was to examine the use of non-parametric methods in estimating tree level growth models. In non-parametric methods the growth of a tree is predicted as a weighted average of the values of neighbouring observations. The selection of the nearest neighbours is based on the differences between tree and stand level characteristics of the target tree and the neighbours. The data for the models were collected from the areas owned by Kuusamo Common Forest in Northeast Finland. The whole data consisted of 4051 tally trees and 1308 Scots pines (Pinus sylvestris L.) and 367 Norway spruces (Picea abies Karst.). Models for 5-year diameter growth and bark thickness at the end of the growing period were constructed with two different non-parametric methods: the k-nearest neighbour regression and k-Most Similar Neighbour method. Diameter at breast height, tree height, mean age of the stand and basal area of the trees larger than the subject tree were found to predict the diameter growth most accurately. The non-parametric methods were compared to traditional regression growth models and were found to be quite competitive and reliable growth estimators.
  • Sironen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland ORCID ID:E-mail: susanna.sironen@forest.joensuu.fi (email)
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail:
  • Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland ORCID ID:E-mail:
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail:
article id 597, category Research article
Annika Kangas, Jyrki Kangas, Jouni Pykäläinen. (2001). Outranking methods as tools in strategic natural resources planning. Silva Fennica vol. 35 no. 2 article id 597. https://doi.org/10.14214/sf.597
Two outranking methods, ELECTRE III and PROMETHEE II, commonly used as decision-aid in various environmental problems, and their applications to decision support for natural resources management are presented. These methods represent ‘the European school’ of multi-criteria decision making (MCDM), as opposed to ‘the American school’, represented by, for instance, the AHP method. On the basis of a case study, outranking methods are compared to so far more usually applied techniques based on the ideas of multi attribute utility theory (MAUT). The outranking methods have been recommended for situations where there is a finite number of discrete alternatives to be chosen among. The number of decision criteria and decision makers may be large. An important advantage of outranking methods, when compared to decision support techniques most often applied in today’s natural resources management, is the ability to deal with ordinal and more or less descriptive information on the alternative plans to be evaluated. Furthermore, the uncertainty concerning the values of the criterion variables can be taken into account using fuzzy relations, determined by indifference and preference thresholds. The difficult interpretation of the results, on the other hand, is the main drawback of the outranking methods.
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail: annika.kangas@metla.fi (email)
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail:
  • Pykäläinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland ORCID ID:E-mail:
article id 620, category Research article
Annika Kangas, Matti Maltamo. (2000). Performance of percentile based diameter distribution prediction and Weibull method in independent data sets. Silva Fennica vol. 34 no. 4 article id 620. https://doi.org/10.14214/sf.620
Diameter distribution is used in most forest management planning packages for predicting stand volume, timber volume and stand growth. The prediction of diameter distribution can be based on parametric distribution functions, distribution-free parametric prediction methods or purely non-parametric methods. In the first case, the distribution is obtained by predicting the parameters of some probability density function. In a distribution-free percentile method, the diameters at certain percentiles of the distribution are predicted with models. In non-parametric methods, the predicted distribution is a linear combination of similar measured stands. In this study, the percentile based diameter distribution is compared to the results obtained with the Weibull method in four independent data sets. In the case of Scots pine, the other methods are also compared to k-nearest neighbour method. The comparison was made with respect to the accuracy of predicted stand volume, saw timber volume and number of stems. The predicted percentile and Weibull distributions were calibrated using number of stems measured from the stand. The information of minimum and maximum diameters were also used, for re-scaling the percentile based distribution or for parameter recovery of Weibull parameters. The accuracy of the predicted stand characteristics were also compared for calibrated distributions. The most reliable results were obtained using the percentile method with the model set including number of stems as a predictor. Calibration improved the results in most cases. However, using the minimum and maximum diameters for parameter recovery proved to be inefficient.
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail: annika.kangas@metla.fi (email)
  • Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland ORCID ID:E-mail:
article id 619, category Research article
Annika Kangas, Matti Maltamo. (2000). Percentile based basal area diameter distribution models for Scots pine, Norway spruce and birch species. Silva Fennica vol. 34 no. 4 article id 619. https://doi.org/10.14214/sf.619
Information about diameter distribution is used for predicting stand total volume, timber volume and stand growth for forest management planning. Often, the diameter distribution is obtained by predicting the parameters of some probability density function, using means and sums of tree characters as predictors. However, the results have not always been satisfactory: the predicted distributions practically always have a similar shape. Also, multimodal distributions cannot be obtained. However, diameter distribution can also be predicted using distribution-free methods. In the percentile method, the diameters at certain percentiles of the distribution are predicted with models. The empirical diameter distribution function is then obtained by interpolating between the predicted diameters. In this paper, models for diameters at 12 percentiles of stand basal area are presented for Scots pine, Norway spruce and birch species. Two sets of models are estimated: a set with and one without number of stems as a predictor. Including the number of stems as a predictor improved the volume and saw timber volume estimates for all species, but the improvements were especially high for number of stems estimates obtained from the predicted distribution. The use of number of stems as predictor in models is based on the possibility of including this characteristic to measured stand variables.
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail: annika.kangas@metla.fi (email)
  • Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland ORCID ID:E-mail:
article id 651, category Research article
Annika S. Kangas, Jyrki Kangas. (1999). Optimization bias in forest management planning solutions due to errors in forest variables. Silva Fennica vol. 33 no. 4 article id 651. https://doi.org/10.14214/sf.651
The yield of various forest variables is predicted by means of a simulation system to provide information for forest management planning. These predictions contain many kinds of uncertainty, for example, prediction and measurement errors. Inevitably, this has an effect on forest management planning. It is well known that uncertainty in the forest yields causes optimistic bias in the observed values of the objective function. This bias increases with the error variances. The amount of bias, however, also depends on the error structure and the relations between the objective variables. In this paper, the effect of uncertainty in forest yields on optimization is studied by simulation. The effect of two different sources of error, the correlation structure of these errors and relations among the objective variables are considered, as well as the effect of two different optimization approaches. The relations between the objective variables and the error structure had a notable effect on the optimization results.
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail: annika.kangas@metla.fi (email)
  • Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland ORCID ID:E-mail:

Category: Research note

article id 9986, category Research note
Ninni Saarinen, Joanne C. White, Michael A. Wulder, Annika Kangas, Sakari Tuominen, Ville Kankare, Markus Holopainen, Juha Hyyppä, Mikko Vastaranta. (2018). Landsat archive holdings for Finland: opportunities for forest monitoring. Silva Fennica vol. 52 no. 3 article id 9986. https://doi.org/10.14214/sf.9986
Highlights: The 45-year Landsat archive contained 30 076 images for Finland by December 31, 2017; 16.3% of these were acquired within ±30 days of August 1 (northern hemisphere summer), have <70% cloud cover, and a 30 m spatial resolution; Using time series analyses, these data provide unique information that complements other datasets available for forest monitoring and assessment in Finland.

There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30 076 images (1972–2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 ±30 days, <70% cloud cover, 30 m spatial resolution). Data from the Landsat archive can augment forest monitoring efforts in Finland, provide new information for science and applications, and enable retrospective, systematic analyses to characterize the development of Finnish forests over the past three decades. The capacity to monitor trends based upon this multi-decadal record with the addition of new measurements is of benefit to multisource inventories and offers nationally comprehensive spatially-explicit datasets for a wide range of stakeholders and applications.

  • Saarinen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID: https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@helsinki.fi (email)
  • White, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland; Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada ORCID ID: http://orcid.org/0000-0003-4674-0373 E-mail: joanne.white@canada.ca
  • Wulder, Canadian Forest Service, (Pacific Forestry Center), Natural Resources Canada, 506 West Burnside Road, Victoria, BC, V8Z 1M5, Canada ORCID ID: https://orcid.org/0000-0002-6942-1896 E-mail: mike.wulder@canada.ca
  • Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland ORCID ID:E-mail: annika.kangas@luke.fi
  • Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland ORCID ID:E-mail: sakari.tuominen@luke.fi
  • Kankare, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: ville.kankare@helsinki.fi
  • Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: markus.holopainen@helsinki.fi
  • Hyyppä, Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, FI-02431 Masala, Finland ORCID ID:E-mail: juha.hyyppa@nls.fi
  • Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID: https://orcid.org/0000-0001-6552-9122 E-mail: mikko.vastaranta@uef.fi

Category: Discussion article

article id 1304, category Discussion article
Annika Kangas, Teppo Hujala. (2015). Challenges in publishing: producing, assuring and communicating quality. Silva Fennica vol. 49 no. 4 article id 1304. https://doi.org/10.14214/sf.1304

This paper is based on a session “How to make forest science available for all? Publishers’, editors’, and authors’ challenges” at the IUFRO XXIV world conference, organized by Pekka Nygren and Eeva Korpilahti from the Finnish Society of Forest Science. The presenters dealt with the topical problems of publishing scientific knowledge from different perspectives. The talks covered the development of journals, publications and submissions, benefits and drawbacks of open access publishing as well as electronic and traditional publishing, and possibilities to promote interesting papers either from the journal’s or from the author’s perspective, and the problems of disseminating the scientific results to the end users. In this paper, a few prevalent viewpoints, inspired by the session, are raised and discussed with some suggestions included.

  • Kangas, Natural Resources Institute Finland (Luke), Economics and society, P.O. Box 68, FI-80101 Joensuu, Finland ORCID ID:E-mail: annika.kangas@luke.fi (email)
  • Hujala, Natural Resources Institute Finland (Luke), Bio-based business and industry, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: teppo.hujala@luke.fi

Category: Article

article id 5553, category Article
Annika Kangas, Kari T. Korhonen. (1995). Generalizing sample tree information with semiparametric and parametric models. Silva Fennica vol. 29 no. 2 article id 5553. https://doi.org/10.14214/sf.a9204

Semiparametric models, ordinary regression models and mixed models were compared for modelling stem volume in National Forest Inventory data. MSE was lowest for the mixed model. Examination of spatial distribution of residuals showed that spatial correlation of residuals is lower for semiparametric and mixed models than for parametric models with fixed regressors. Mixed models and semiparametric models can both be used for describing the effect of geographic location on stem form.

  • Kangas, ORCID ID:E-mail:
  • Korhonen, ORCID ID:E-mail:
article id 5524, category Article
Annika Kangas. (1994). Classical and model based estimators for forest inventory. Silva Fennica vol. 28 no. 1 article id 5524. https://doi.org/10.14214/sf.a9158

In this study, model-based and design-based inference methods are used for estimating mean volume and its standard error for systematic cluster sampling. Results obtained with models are compared to results obtained with classical methods. The data are from the Finnish National Forest Inventory. The variation of volume in ten forestry board districts in Southern Finland is studied. The variation is divided into two components: trend and correlated random errors. The effect of the trend and the covariance structure on the obtained mean volume and standard error estimates is discussed. The larger the coefficient of determination of the trend model, the smaller the model-based estimates of standard error, when compared to classical estimates. On the other hand, the wider the range and level of autocorrelation between the sample plots, the larger the model-based estimates of standard error.

  • Kangas, ORCID ID:E-mail:
article id 5453, category Article
Annika Kangas. (1991). Updated measurement data as prior information in forest inventory. Silva Fennica vol. 25 no. 3 article id 5453. https://doi.org/10.14214/sf.a15611

Old inventory data has widely been used as prior information in forest inventory using the method of sampling with partial replacement (SPR). In this method knowledge about forest growth has not been utilized. However, the accuracy of the inventory results can be improved if this knowledge is utilized. The usability of the inventory results can be improved if the prior information is updated by treewise growth models. In this paper a statistical basis is presented for a method in which such information can be used. The applicability of the method is also discussed. An example is given to demonstrate the method.

The PDF includes an abstract in Finnish.

  • Kangas, ORCID ID:E-mail:

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