Current issue: 58(5)

Scopus CiteScore 2023: 3.5
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Articles by Annika Kangas

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

Keywords: forest inventories; mixed models; volume; nonparametric models; semiparametric models

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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, E-mail: ak@mm.unknown
Korhonen, E-mail: kk@mm.unknown

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

Keywords: models; forest inventories; estimation; systematic cluster sampling; covariance structure

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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, E-mail: ak@mm.unknown

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

Keywords: forest inventories; mixed estimator; prior information; model-based inference

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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, E-mail: ak@mm.unknown

Category : Research article

article id 23044, category Research article

Kyle Eyvindson, Annika Kangas, Olha Nahorna, Juliette Hunault-Fontbonne, Maria Potterf. (2024). Integrating wind disturbances into forest planning: a stochastic programming approach. Silva Fennica vol. 58 no. 4 article id 23044. https://doi.org/10.14214/sf.23044

Keywords: forest planning; wind damage; risk mitigation; stochastic optimization

Highlights: Assessing risk should focus on the objectives of the decision maker, not simply to minimize wind damage; We explored timber income-oriented objectives, maximizing net profit and maintaining a high even-flow of timber related income; Integrating wind disturbances had limited advantages when prioritizing net profits, however, the impact was dramatic when striving to maintain a high even-flow of timber.

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Forest disturbances challenge our ability to carefully plan for sustainable use of forest resources. As forest disturbances are stochastic, we cannot plan for the disturbance at any specific time or location. However, we can prepare for the possibility of a disturbance by integrating its potential intensity range and frequency when developing forest management plans. This study uses stochastic programming to integrate wind intensity (wind speed) and wind event frequency (number of occurrences) into the forest planning process on a small coastal Finnish forest landscape. We used a mechanistic model to quantify the critical wind speed for tree felling, with a Monte Carlo approach to include wind damage and salvage logging into forest management alternatives. We apply a stochastic programming model to explore two objectives: maximizing the expected forest net present value or maximizing the even-flow of income. To assess the effects of improper wind risk assumptions in planning, we compare the results when optimizing for correct versus incorrect wind intensity and frequency assumptions. When maximizing for net present value, the impacts of misidentifying wind intensity and frequency are minor, likely due to harvests planned immediately as trees reach maturity. For the case when maximizing even-flow of income, incorrectly identifying wind intensity and frequency severely impacts the ability to meet the required harvest targets and reduces the expected net present value. The specific utility of risk mitigation therefore depends on the planning problem. Overall, we show that incorporating wind disturbances into forest planning can inform forest owners about how they can manage wind risk based on their specific risk preferences.

Eyvindson, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Norway; Natural Resource Institute Finland (Luke), Bioeconomy and Environment, Laatokartanonkaari 9, 00790 Helsinki, Finland https://orcid.org/0000-0003-0647-1594 E-mail: kyle.eyvindson@nmbu.no
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, Yliopistokatu 6, 80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Nahorna, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Norway https://orcid.org/0000-0002-5497-0315 E-mail: olha.nahorna@nmbu.no
Hunault-Fontbonne, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Campus Ås, Norway https://orcid.org/0009-0004-1864-5162 E-mail: juliette.hunault@nmbu.no
Potterf, Ecosystem Dynamics and Forest Management Group, Technical University of Munich, Hans Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany https://orcid.org/0000-0001-6763-1948 E-mail: maria.potterf@tum.de

article id 23021, category Research article

Virpi Stenman, Annika Kangas, Markus Holopainen. (2023). Upper stem diameter and volume prediction strategies in the National Forest Inventory of Finland. Silva Fennica vol. 57 no. 3 article id 23021. https://doi.org/10.14214/sf.23021

Keywords: forest inventory; measurement errors; accuracy; Bland-Altman plot

Highlights: National Forest Inventory specific methods were applied with a number of measurement instruments, including a laser-based dendrometer, to collect tree stem diameter measurements; Bland-Altman plots and measurement error variances were used to determine measurement precision and accuracy; The laser-based dendrometer did not perform better than the other instruments in the study.

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In forest inventories, field data are needed for the prediction of tree volumes. However, gathering field data requires resources, such as labour, equipment, and data management operations. This means that time and budget, as well as quality, must be carefully considered when National Forest Inventory (NFI) field measurement activities are planned. Therefore, the development of cost efficient, simple, safe and reliable measurement methods and tools are of great interest. To date, upper stem diameter (d6), which provides a more reliable estimation of tree stem volume, has typically been measured with a parabolic calliper. In this study, the performance of the Criterion laser-based dendrometer was examined for d6 measurements. A total of 326 sample trees were measured multiple times with three different measurement instruments. These instruments were used to measure diameter at breast height (dbh) as well as d6 measurements. Bland-Altman plots and measurement error variances were used to determine measurement instrument reliability. For all trees, the standard deviation for the laser based dendrometer was 18.73 mm at dbh and 15.36 mm for the d6 measurements. When the performance of Criterion was analysed with reference to the mean value of repeated measurements, the standard deviation in the dbh measurements was 12.21 mm, and 8.88 mm in the d6 measurements.

Stenman, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland https://orcid.org/0000-0003-1176-7840 E-mail: virpi.stenman@helsinki.fi
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 68, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: markus.holopainen@helsinki.fi

article id 22026, category Research article

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 10291, category Research article

Sakari Tuominen, Andras Balazs, Annika Kangas. (2020). Comparison of photogrammetric canopy models from archived and made-to-order aerial imagery in forest inventory. Silva Fennica vol. 54 no. 5 article id 10291. https://doi.org/10.14214/sf.10291

Keywords: distribution; prediction; forest resources; mapping; aerial imaging; digital stereo-photogrammetry

Highlights: Two photogrammetric canopy models were tested in forest inventory: one based on archived standard aerial imagery acquired for ortho-mosaic production and another based on stereo-photogrammetrically oriented aerial imaging adjusted for stereo-photogrammetric canopy modelling; Both data sets were tested in the estimation of forest variables; Despite the differences in imaging parameters, there was little difference in their performance in predicting the forest inventory variables.

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In remote sensing-based forest inventories 3D point cloud data, such as acquired from airborne laser scanning, are well suited for estimating the volume of growing stock and stand height, but tree species recognition often requires additional optical imagery. A combination of 3D data and optical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric 3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories, due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereo-photogrammetric canopy modeling can also be applied to previously acquired imagery, such as for aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In this study we compared two stereo-photogrammetric canopy models combined with contemporary satellite imagery in forest inventory. One canopy model was based on standard archived imagery acquired primarily for ortho-mosaic production, and another was based on aerial imagery whose acquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested in the estimation of growing stock volume, volumes of main tree species, basal area and diameter and height. Despite the better quality of the latter canopy model, the difference of the accuracy of the forest estimates based on the two different data sets was relatively small for most variables (differences in RMSEs were 0–20%, depending on variable). However, the estimates based on stereo-photogrammetrically oriented aerial data retained better the original variation of the forest variables present in the study area.

Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
Balazs, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and Environment, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: annika.kangas@luke.fi

article id 10347, category Research article

Matti Katila, Tuomas Rajala, Annika Kangas. (2020). Assessing local trends in indicators of ecosystem services with a time series of forest resource maps. Silva Fennica vol. 54 no. 4 article id 10347. https://doi.org/10.14214/sf.10347

Keywords: National Forest Inventory; satellite images; k-nearest neighbour estimation; Landsat; Mann-Kendall test; multitemporal inventory data; temporal trend analysis

Highlights: Untitled Document Contextual Mann-Kendall test detects significant trends in time-series of forest maps; Trends become more consistent as the areal unit size used for test input increases; Changes in different scales reflect different phenomena in forests; Significant trends were detected even after multiple testing correction.

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Since the 1990’s, forest resource maps and small area estimates have been produced by combining national forest inventory (NFI) field plot data, optical satellite images and numerical map data using a non-parametric k-nearest neighbour method. In Finland, thematic maps of forest variables have been produced by the means of multi-source NFI (MS-NFI) for eight to ten times depending on the geographical area, but the resulting time series have not been systematically utilized. The objective of this study was to explore the possibilities of the time series for monitoring the key ecosystem condition indicators for forests. To this end, a contextual Mann-Kendall (CMK) test was applied to detect trends in time-series of two decades of thematic maps. The usefulness of the observed trends may depend both on the scale of the phenomena themselves and the uncertainties involved in the maps. Thus, several spatial scales were tested: the MS-NFI maps at 16 × 16 m² pixel size and units of 240 × 240 m², 1200 × 1200 m² and 12 000 × 12 000 m² aggregated from the MS-NFI map data. The CMK test detected areas of significant increasing trends of mean volume on both study sites and at various unit sizes except for the original thematic map pixel size. For other variables such as the mean volume of tree species groups, the proportion of broadleaved tree species and the stand age, significant trends were mostly found only for the largest unit size, 12 000 × 12 000 m². The multiple testing corrections decreased the amount of significant p-values from the CMK test strongly. The study showed that significant trends can be detected enabling indicators of ecosystem services to be monitored from a time-series of satellite image-based thematic forest maps.

Katila, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland; https://orcid.org/0000-0001-6946-5736 E-mail: matti.katila@luke.fi
Rajala, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: tuomas.rajala@luke.fi
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi

article id 10269, category Research article

Annika Kangas, Helena M. Henttonen, Timo P. Pitkänen, Sakari Sarkkola, Juha Heikkinen. (2020). Re-calibrating stem volume models – is there change in the tree trunk form from the 1970s to the 2010s in Finland? Silva Fennica vol. 54 no. 4 article id 10269. https://doi.org/10.14214/sf.10269

Keywords: TLS; OLS; regression; terrestrial laser scanning

Highlights: TLS data showed that trunk form has changed in Finland from the 1970s; Significant differences were observed for all tree species; The trees in TLS data are on average more slender than in the old data.

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The tree stem volume models of Norway spruce, Scots pine and silver and downy birch currently used in Finland are based on data collected during 1968–1972. These models include four different formulations of a volume model, with three different combinations of independent variables: 1) diameter at height of 1.3 m above ground (dbh), 2) dbh and tree height (h) and 3) dbh, h and upper diameter at height of 6 m (d6). In recent National Forest Inventories of Finland, a difference in the mean volume prediction between the models with and without the upper diameter as predictor has been observed. To analyze the causes of this difference, terrestrial laser scanning (TLS) was used to acquire a large dataset in Finland during 2017–2018. Field-measured predictors and volumes predicted using spline functions fitted to the TLS data were used to re-calibrate the current volume models. The trunk form is different in these two datasets. The form height is larger in the new data for all diameter classes, which indicates that the tree trunks are more slender than they used to be. One probable reason for this change is the increase in stand densities, which is at least partly due to changed forest management. In models with both dbh and h as predictors, the volume is smaller a given h class in the data new data than in the old data, and vice versa for the diameter classes. The differences between the old and new models were largest with pine and smallest with birch.

Kangas, Natural Resources Institute Finland (Luke), Yliopistokatu 6 B, FI-80100 Joensuu, Finland https://orcid.org/0000-0002-8637-5668 E-mail: annika.kangas@luke.fi
Henttonen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: helena.henttonen@luke.fi
Pitkänen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0001-5389-8713 E-mail: timo.p.pitkanen@luke.fi
Sarkkola, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: sakari.sarkkola@luke.fi
Heikkinen, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-3527-774X E-mail: juha.heikkinen@luke.fi

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

Keywords: forest inventory; value of information; uncertainty; sub-optimality loss

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.

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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 E-mail: arto.haara@luke.fi
Kangas, Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland 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 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

Keywords: forest inventory; value of information; uncertainty; remote sensing; cost-plus-loss; data quality

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.

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

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 E-mail: annika.kangas@luke.fi
Gobakken, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway 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 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 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 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

Keywords: forest inventory; remote sensing; spatial autocorrelation; spatial distribution; aerial imagery; stereo-photogrammetry

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.

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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 E-mail: sakari.tuominen@luke.fi
Pitkänen, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: timo.p.pitkanen@luke.fi
Balazs, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 2, FI-00791 Helsinki, Finland E-mail: andras.balazs@luke.fi
Kangas, Natural Resources Institute Finland (Luke), Economics and Society, P.O. Box 68, FI-80101 Joensuu, Finland 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

Keywords: forestry; reflectance calibration; BRDF; mixed-effects modeling; Monte-Carlo simulation

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.

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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 E-mail: ilkka.korpela@helsinki.fi
Mehtätalo, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.mehtatalo@uef.fi
Markelin, Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, FI-02431 Masala, Finland E-mail: lauri.markelin@fgi.fi
Seppänen, Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: anne.seppanen@arbonaut.com
Kangas, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014, Finland 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

Keywords: value of information; prediction error; inventory error

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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 E-mail: antti.makinen@simosol.fi
Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: ak@nn.fi
Nurmi, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: mn@nn.fi

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

Keywords: forest inventory; quality assessment; airborne laser scanning

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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 E-mail: tarja.wallenius@metsa.fi
Laamanen, Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland E-mail: rl@nn.fi
Peuhkurinen, Oy Arbonaut Ltd, Helsinki, Finland E-mail: jp@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, Helsinki, Finland E-mail: ak@nn.fi

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

Keywords: forest inventories; harvest planning; information needs

Abstract | View details | Full text in PDF | Author Info

Laamanen, Metsähallitus, Vantaa, Finland E-mail: risto.laamanen@metsa.fi
Kangas, Metsähallitus, Vantaa, Finland E-mail: ak@nn.fi

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

Keywords: forest planning; inventory; measurement errors; decision making; logistic regression; regression tree

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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 E-mail: annika.kangas@helsinki.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, Joensuu, Finland E-mail: lm@nn.fi
Mäkinen, Simosol Oy, Riihimäki, Finland E-mail: am@nn.fi
Vanhatalo, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: kv@nn.fi

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

Keywords: growth prediction; uncertainty; forest information; updating; inoptimality loss

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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 E-mail: ip@nn.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: annika.kangas@helsinki.fi
Mäkinen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: am@nn.fi
Mehtätalo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi

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

Keywords: eCognition; form height; Getis statistics; image segmentation; local indicators of spatial association

Abstract | View details | Full text in PDF | Author Info

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., G_i^*-indices. The segmentation resulted in four different rasters: 1) residuals of the global model, 2) the local G_i^*-index, and 3) residuals and the local G_i^*-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 E-mail: minna.s.raty@helsinki.fi
Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland E-mail: ak@nn.fi

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

Keywords: collaborative planning; experienced values of forests; social value mapping; survey

Abstract | View details | Full text in PDF | Author Info

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 E-mail: annika.kangas@helsinki.fi
Haapakoski, University of Helsinki, Department of Forest Management, P.O. Box 27, FI-00014 University of Helsinki E-mail: rh@nn.fi
Tyrväinen, Finnish Forest Research Institute, P.O. Box 16, FI-96301 Rovaniemi, Finland. E-mail: lt@nn.fi

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

Keywords: stand structure; diameter distribution; prediction; interpolation

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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 E-mail: ak@nn.fi
Mehtätalo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lm@nn.fi
Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi

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

Keywords: stand structure; inventory; percentile; order statistics

Abstract | View details | Full text in PDF | Author Info

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 E-mail: lauri.mehtatalo@metla.fi
Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
Kangas, University of Helsinki, Department of Forest Resources Management, P.O.Box 27, FI-00014 University of Helsinki, Finland E-mail: ak@nn.fi

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

Keywords: group decision support; multicriteria approval; timber-harvesting planning; voting methods

Abstract | View details | Full text in PDF | Author Info

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 E-mail: sanna.laukkanen@joensuu.fi
Palander, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tp@nn.fi
Kangas, UPM-Kymmene Forest, P.O. Box 32, FI-37601 Valkeakoski, Finland E-mail: jk@nn.fi
Kangas, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ak@nn.fi

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

Keywords: diameter distribution; timber assortment; prediction; calibration estimation; volume; accuracy; measurement combination

Abstract | View details | Full text in PDF | Author Info

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 E-mail: annika.kangas@helsinki.fi
Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: mm@nn.fi

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

Keywords: pine; spruce; single tree growth models; non-parametric models; local estimates

Abstract | View details | Full text in PDF | Author Info

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 E-mail: susanna.sironen@forest.joensuu.fi
Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: ak@nn.fi
Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi
Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi

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

Keywords: uncertainty; fuzzy relations; multicriteria decision support; multiple-use planning

Abstract | View details | Full text in PDF | Author Info

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 E-mail: annika.kangas@metla.fi
Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi
Pykäläinen, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: jp@nn.fi

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

Keywords: stand structure; calibration estimation; Weibull function; diameter distribution prediction; distribution-free method; nearest neighbour method

Abstract | View details | Full text in PDF | Author Info

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 E-mail: annika.kangas@metla.fi
Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi

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

Keywords: stand structure; diameter distribution prediction; distribution-free method; rational spline

Abstract | View details | Full text in PDF | Author Info

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 E-mail: annika.kangas@metla.fi
Maltamo, Finnish Forest Research Institute, Joensuu Research Station, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi

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

Keywords: National Forest Inventory; satellite; Landsat time series

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.

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

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 https://orcid.org/0000-0003-2730-8892 E-mail: ninni.saarinen@helsinki.fi
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 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 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 E-mail: annika.kangas@luke.fi
Tuominen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, FI-00790 Helsinki, Finland E-mail: sakari.tuominen@luke.fi
Kankare, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ville.kankare@helsinki.fi
Holopainen, Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 University of Helsinki, Finland 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 E-mail: juha.hyyppa@nls.fi
Vastaranta, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland 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

Keywords: peer review; open access; altmetrics; citation index

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

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 E-mail: annika.kangas@luke.fi
Hujala, Natural Resources Institute Finland (Luke), Bio-based business and industry, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: teppo.hujala@luke.fi

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