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
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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.
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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
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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.
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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
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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.
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Kangas,
E-mail:
ak@mm.unknown
Category :
Research article
article id 23044,
category
Research article
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.
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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
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.
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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
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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
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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
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.
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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
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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
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.
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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
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.
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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
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.
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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
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.
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Haara,
Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80101 Joensuu, Finland
E-mail:
arto.haara@luke.fi
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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
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.
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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.
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Kangas,
Natural Resources Institute Finland (Luke), Bioeconomy and environment, P.O. Box 68, FI-80170 Joensuu, Finland
E-mail:
annika.kangas@luke.fi
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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
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.
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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
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
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Mehtätalo,
Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland
E-mail:
lauri.mehtatalo@uef.fi
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Markelin,
Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, P.O. Box 15, FI-02431 Masala, Finland
E-mail:
lauri.markelin@fgi.fi
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Seppänen,
Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland
E-mail:
anne.seppanen@arbonaut.com
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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
Abstract |
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Author Info
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.
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Mäkinen,
Simosol Oy, Rautatietori 4, FI-11130 Riihimäki, Finland
E-mail:
antti.makinen@simosol.fi
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Kangas,
University of Helsinki, Department of Forest Sciences, Helsinki, Finland
E-mail:
ak@nn.fi
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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
Abstract |
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Author Info
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.
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Wallenius,
Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland
E-mail:
tarja.wallenius@metsa.fi
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Laamanen,
Metsähallitus, P.O. Box 94, FI-01301 Vantaa, Finland
E-mail:
rl@nn.fi
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Peuhkurinen,
Oy Arbonaut Ltd, Helsinki, Finland
E-mail:
jp@nn.fi
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Mehtätalo,
University of Eastern Finland, School of Forest Sciences, Joensuu, Finland
E-mail:
lm@nn.fi
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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