Current issue: 58(5)

Scopus CiteScore 2023: 3.5
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Articles containing the keyword 'laser scanning'

Category : Climate resilient and sustainable forest management – Research article

article id 23042, category Climate resilient and sustainable forest management – Research article

Johanna Jääskeläinen, Lauri Korhonen, Mikko Kukkonen, Petteri Packalen, Matti Maltamo. (2024). Individual tree inventory based on uncrewed aerial vehicle data: how to utilise stand-wise field measurements of diameter for calibration? Silva Fennica vol. 58 no. 3 article id 23042. https://doi.org/10.14214/sf.23042

Keywords: laser scanning; calibration; mixed-effects model; single-tree detection

Highlights: A practical scheme to improve the accuracy of predicted tree and stand attributes in an uncrewed aerial vehicle based individual tree inventory; Accuracy was considerably improved with data from 2–4 sample trees from the target stand; Calibrated existing models and the construction of local models performed equally well; The laborious task of constructing a local model can be avoided by using a calibrated transferred model.

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

Uncrewed aerial vehicles (UAV) have great potential for use in forest inventories, but in practice they can be expensive for relatively small inventory areas as a large number of field measurements are needed for model construction. One proposed solution is to transfer previously constructed models to a new inventory area and to calibrate these with a small number of local field measurements. Our objective was to compare calibration of general models and the construction of new models to determine the best approach for UAV-based forest inventories. Our material included field measurements and UAV-based laser scanning data, from which individual trees were automatically identified. A general mixed-effects model for diameter at breast height (DBH) had been formulated earlier based on data from a geographically wider area. It was calibrated to the study area with field measurements from 2–10 randomly selected calibration trees. The calibrated diameters were used to calculate the diameter of a basal area median tree (DGM), tree volumes, and the volume of all trees at plot-level. Next, new DBH-models were formulated based on the 2–10 randomly selected trees and calibrated with plot-level random effects estimated during model construction. Finally, plot-specific height-diameter regression models were formulated by randomly selecting 10 trees from each plot. Calibration reduced the prediction errors of all variables. An increase in the number of calibration trees decreased error rates by 1–6% depending on the variable. Calibrated predictions from the general mixed-effects model were similar to the separately formulated mixed-effects models and plot-specific regression models.

Jääskeläinen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0009-0004-4127-7863 E-mail: johanna.jaaskelainen@uef.fi
Korhonen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-9352-0114 E-mail: lauri.korhonen@uef.fi
Kukkonen, Natural Resources Institute Finland, Yliopistokatu 6 B, FI-80100 Joensuu, Finland https://orcid.org/0000-0003-4206-1680 E-mail: mikko.kukkonen@luke.fi
Packalen, Natural Resources Institute Finland, Latokartanonkaari 9, FI-00790 Helsinki, Finland https://orcid.org/0000-0003-1804-0011 E-mail: petteri.packalen@luke.fi
Maltamo, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland https://orcid.org/0000-0002-9904-3371 E-mail: matti.maltamo@uef.fi

Category : Research article

article id 23023, category Research article

Lennart Noordermeer, Hans Ole Ørka, Terje Gobakken. (2023). Imputing stem frequency distributions using harvester and airborne laser scanner data: a comparison of inventory approaches. Silva Fennica vol. 57 no. 3 article id 23023. https://doi.org/10.14214/sf.23023

Keywords: forest inventory; airborne laser scanning; harvester data; inventory approaches

Highlights: We imputed stem frequency distributions using harvester reference data and predictor variables computed from airborne laser scanner data.; Stand-level distributions of stem diameter, tree height, volume, and sawn wood volume; (Enhanced) area-based and semi-individual tree crown approaches outperformed the individual tree crown method.

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Stem frequency distributions provide useful information for pre-harvest planning. We compared four inventory approaches for imputing stem frequency distributions using harvester data as reference data and predictor variables computed from airborne laser scanner (ALS) data. We imputed distributions and stand mean values of stem diameter, tree height, volume, and sawn wood volume using the k-nearest neighbor technique. We compared the inventory approaches: (1) individual tree crown (ITC), semi-ITC, area-based (ABA) and enhanced ABA (EABA). We assessed the accuracies of imputed distributions using a variant of the Reynold’s error index, obtaining the best mean accuracies of 0.13, 0.13, 0.10 and 0.10 for distributions of stem diameter, tree height, volume and sawn wood volume, respectively. Accuracies obtained using the semi-ITC, ABA and EABA inventory approaches were significantly better than accuracies obtained using the ITC approach. The forest attribute, inventory approach, stand size and the laser pulse density had significant effects on the accuracies of imputed frequency distributions, however the ALS delay and percentage of deciduous trees did not. This study highlights the utility of harvester and ALS data for imputing stem frequency distributions in pre-harvest inventories.

Noordermeer, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-8840-0345 E-mail: lennart.noordermeer@nmbu.no
Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
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

article id 22007, category Research article

Ilkka Korpela, Antti Polvivaara, Saija Papunen, Laura Jaakkola, Noora Tienaho, Johannes Uotila, Tuomas Puputti, Aleksi Flyktman. (2023). Airborne dual-wavelength waveform LiDAR improves species classification accuracy of boreal broadleaved and coniferous trees. Silva Fennica vol. 56 no. 4 article id 22007. https://doi.org/10.14214/sf.22007

Keywords: crown modeling; laser scanning; photogrammetry; individual tree detection; Scandinavia

Highlights: First study to assess dual-wavelength waveform data in tree species identification; New findings regarding waveform features of previously unstudied species; Waveform features correlated with tree size displaying wavelength- and species-specific differences linked to bark reflectance, height growth rate and foliage density; Effects by pulse length and beam divergence are highlighted.

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

Tree species identification constitutes a bottleneck in remote sensing applications. Waveform LiDAR has been shown to offer potential over discrete-return observations, and we assessed if the combination of two-wavelength waveform data can lead to further improvements. A total of 2532 trees representing seven living and dead conifer and deciduous species classes found in Hyytiälä forests in southern Finland were included in the experiments. LiDAR data was acquired by two single-wavelength sensors. The 1064-nm and 1550-nm data were radiometrically corrected to enable range-normalization using the radar equation. Pulses were traced through the canopy, and by applying 3D crown models, the return waveforms were assigned to individual trees. Crown models and a terrain model enabled a further split of the waveforms to strata representing the crown, understory and ground segments. Different geometric and radiometric waveform attributes were extracted per return pulse and aggregated to tree-level mean and standard deviation features. We analyzed the effect of tree size on the features, the correlation between features and the between-species differences of the waveform features. Feature importance for species classification was derived using F-test and the Random Forest algorithm. Classification tests showed significant improvement in overall accuracy (74→83% with 7 classes, 88→91% with 4 classes) when the 1064-nm and 1550-nm features were merged. Most features were not invariant to tree size, and the dependencies differed between species and LiDAR wavelength. The differences were likely driven by factors such as bark reflectance, height growth induced structural changes near the treetop as well as foliage density in old trees.

Korpela, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0002-1665-3984 E-mail: ilkka.korpela@helsinki.fi
Polvivaara, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: –
Papunen, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0001-5383-4314 E-mail: saija.papunen@outlook.com
Jaakkola, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: laura.jaakkola@helsinki.fi
Tienaho, University of Eastern Finland, Faculty of Science and Forestry, P.O. Box 111, FI-80101 Joensuu, Finland 0000-0002-6574-5797 E-mail: noora.tienaho@uef.fi
Uotila, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: johannes.uotila@helsinki.fi
Puputti, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0003-1972-1636 E-mail: tuomas.puputti@helsinki.fi
Flyktman, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland 0000-0002-5235-317X E-mail: aleksi.flyktman@helsinki.fi

article id 10712, category Research article

Kenneth Olofsson, Johan Holmgren. (2022). Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking. Silva Fennica vol. 56 no. 3 article id 10712. https://doi.org/10.14214/sf.10712

Keywords: airborne laser scanning; terrestrial laser scanning; field plot; mobile laser scanning; simultaneous location and mapping; stem map

Highlights: A stem diameter weighted linking algorithm for tree maps was introduced which improves linking accuracy; A new simultaneous location and mapping-based co-registration method for stem maps measured with moving sensors was introduced that operates with high linking accuracy.

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A new method for the co-registration of single tree data in forest stands and forest plots applicable to static as well as dynamic data capture is presented. This method consists of a stem diameter weighted linking algorithm that improves the linking accuracy when operating on diverse diameter stands with stem position errors in the single tree detectors. A co-registration quality metric threshold, Q_T, is also introduced which makes it possible to discriminate between correct and incorrect stem map co-registrations with high probability (>99%). These two features are combined to a simultaneous location and mapping-based co-registration method that operates with high linking accuracy and that can handle sensors with drifting errors and signal bias. A test with simulated data shows that the method has an 89.35% detection rate. The statistics of different settings in a simulation study are presented, where the effect of stem density and position errors were investigated. A test case with real sensor data from a forest stand shows that the average nearest neighbor distances decreased from 1.90 m to 0.51 m, which indicates the feasibility of this method.

Olofsson, Section of Forest Remote Sensing, Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden https://orcid.org/0000-0002-2836-2316 E-mail: kenneth.olofsson@slu.se
Holmgren, Section of Forest Remote Sensing, Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden https://orcid.org/0000-0002-7112-8015 E-mail: johan.holmgren@slu.se

article id 10244, category Research article

Hans Ole Ørka, Endre H. Hansen, Michele Dalponte, Terje Gobakken, Erik Næsset. (2021). Large-area inventory of species composition using airborne laser scanning and hyperspectral data. Silva Fennica vol. 55 no. 4 article id 10244. https://doi.org/10.14214/sf.10244

Keywords: airborne laser scanning; Dirichlet regression; hyperspectral; species proportions; species-specific forest inventory

Highlights: A methodology for using hyperspectral data in the area-based approach is presented; Hyperspectral data produced satisfactory results for species composition in 90% of the cases; Parametric Dirichlet regression is an applicable method to predicting species proportions; Normalization and a tree-based selection of pixels provided the overall best results; Both visible to near-infrared and shortwave-infrared sensors gave acceptable results.

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Tree species composition is an essential attribute in stand-level forest management inventories and remotely sensed data might be useful for its estimation. Previous studies on this topic have had several operational drawbacks, e.g., performance studied at a small scale and at a single tree-level with large fieldwork costs. The current study presents the results from a large-area inventory providing species composition following an operational area-based approach. The study utilizes a combination of airborne laser scanning and hyperspectral data and 97 field sample plots of 250 m² collected over 350 km² of productive forest in Norway. The results show that, with the availability of hyperspectral data, species-specific volume proportions can be provided in operational forest management inventories with acceptable results in 90% of the cases at the plot level. Dominant species were classified with an overall accuracy of 91% and a kappa-value of 0.73. Species-specific volumes were estimated with relative root mean square differences of 34%, 87%, and 102% for Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and deciduous species, respectively. A novel tree-based approach for selecting pixels improved the results compared to a traditional approach based on the normalized difference vegetation index.

Ørka, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0002-7492-8608 E-mail: hans-ole.orka@nmbu.no
Hansen, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway; Norwegian Forest Extension Institute, Honnevegen 60, NO-2836 Biri, Norway https://orcid.org/0000-0001-5174-4497 E-mail: eh@skogkurs.no
Dalponte, Department of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, Via E. Mach 1, 38010 San Michele all’Adige, TN, Italy https://orcid.org/0000-0001-9850-8985 E-mail: michele.dalponte@fmach.it
Gobakken, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway https://orcid.org/0000-0001-5534-049X E-mail: terje.gobakken@nmbu.no
Næsset, Norwegian University of Life Sciences, Faculty of Environmental Sciences and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway E-mail: erik.naesset@nmbu.no

article id 10272, category Research article

Ana de Lera Garrido, Terje Gobakken, Hans Ole Ørka, Erik Næsset, Ole M. Bollandsås. (2020). Reuse of field data in ALS-assisted forest inventory. Silva Fennica vol. 54 no. 5 article id 10272. https://doi.org/10.14214/sf.10272

Keywords: airborne laser scanning; data reuse; temporal model transferability

Highlights: Six biophysical forest attributes were estimated for small stands without using up-to-date field data; The approaches included reused model relationships and forecasted field data; The accuracy of height estimates was comparable with the accuracy of an ordinary forest inventory with up-to-date field- and ALS data; Both approaches tended to produce estimates systematically different from the ground reference.

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Forest inventories assisted by wall-to-wall airborne laser scanning (ALS), have become common practice in many countries. One major cost component in these inventories is the measurement of field sample plots used for constructing models relating biophysical forest attributes to metrics derived from ALS data. In areas where ALS-assisted forest inventories are planned, and in which the previous inventories were performed with the same method, reusing previously acquired field data can potentially reduce costs, either by (1) temporally transferring previously constructed models or (2) projecting field reference data using growth models that can serve as field reference data for model construction with up-to-date ALS data. In this study, we analyzed these two approaches of reusing field data acquired 15 years prior to the current ALS acquisition to estimate six up-to-date forest attributes (dominant tree height, mean tree height, stem number, stand basal area, volume, and aboveground biomass). Both approaches were evaluated within small stands with sizes of approximately 0.37 ha, assessing differences between estimates and ground reference values. The estimates were also compared to results from an up-to-date forest inventory relying on concurrent field- and ALS data. The results showed that even though the reuse of historical information has some potential and could be beneficial for forest inventories, systematic errors may appear prominent and need to be overcome to use it operationally. Our study showed systematic trends towards the overestimation of lower-range ground references and underestimation of the upper-range ground references.

de Lera Garrido, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ana.maria.lera.garrido@nmbu.no
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
Ørka, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: hans-ole.orka@nmbu.no
Næsset, 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
Bollandsås, Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway E-mail: ole.martin.bollandsas@nmbu.no

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 2021, category Research article

Jonas Bohlin, Inka Bohlin, Jonas Jonzén, Mats Nilsson. (2017). Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory. Silva Fennica vol. 51 no. 2 article id 2021. https://doi.org/10.14214/sf.2021

Keywords: airborne laser scanning; National Forest Inventory; photogrammetry; aerial images; forest attribute estimation; image matching; large area

Highlights: Image based forest attribute map generated using NFI plots show similar accuracy as currently used LiDAR based forest attribute map; Also similar accuracies were found for different forest types; Aerial images from leaf-off season is not recommended.

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Exploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid- and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image- and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0002-3318-5967 E-mail: jonas.bohlin@slu.se
Bohlin, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0003-1224-6684 E-mail: inka.bohlin@slu.se
Jonzén, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden E-mail: jonas.jonzen@slu.se
Nilsson, Department of Forest Resource Management, Swedish University of Agricultural Sciences, S-901 35 Umeå, Sweden http://orcid.org/0000-0001-7394-6305 E-mail: mats.nilsson@slu.se

article id 983, category Research article

Sakari Tuominen, Juho Pitkänen, Andras Balazs, Kari T. Korhonen, Pekka Hyvönen, Eero Muinonen. (2014). NFI plots as complementary reference data in forest inventory based on airborne laser scanning and aerial photography in Finland. Silva Fennica vol. 48 no. 2 article id 983. https://doi.org/10.14214/sf.983

Keywords: airborne laser scanning; National Forest Inventory; aerial imagery; plot sampling

Highlights: Using NFI plots in forest management inventories could provide a way for rationalising forest inventory data acquisition; NFI plots were used as additional reference data in laser scanning and aerial image based forest inventory; NFI plots improved the estimates of some forest variables; There are differences between the two inventory types that cause difficulties in combining the data.

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In Finland, there are currently two, parallel sample-plot-based forest inventory systems, which differ in their methodologies, sampling designs, and objectives. One is the National Forest Inventory (NFI), aimed at unbiased inventory results at national and regional level. The other is the Forest Centre’s management-oriented forest inventory based on interpretation of airborne laser scanning and aerial images, with the aim of locally accurate stand-level forest estimates. The National Forest Inventory utilises relascope sample plots with systematic cluster sampling. This inventory method is optimised for accuracy of regional volume estimates. In contrast, the management-oriented forest inventory utilises circular sample plots with an allocation system covering certain pre-defined forest classes in the inventory area. This method is optimised to produce reference data for interpretation of the remote-sensing materials in use. In this study, we tested the feasibility of the National Forest Inventory sample plots in provision of additional reference data for the management-oriented inventory. Various combinations of NFI plots and management inventory plots were tested in the interpretation of the laser and aerial-image data. Adding NFI plots in the reference data generally improved the accuracy of the volume estimates by tree species but not the estimates of total volume or stand mean height and diameter. The difference between the plot types in the NFI and management inventories causes difficulties in combination of the two datasets.

Tuominen, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
Pitkänen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: juho.pitkanen@metla.fi
Balazs, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland E-mail: andras.balazs@metla.fi
Korhonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: kari.t.korhonen@metla.fi
Hyvönen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: pekka.hyvonen@metla.fi
Muinonen, Finnish Forest Research Institute, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: eero.muinonen@metla.fi

article id 943, category Research article

Terje Gobakken, Lauri Korhonen, Erik Næsset. (2013). Laser-assisted selection of field plots for an area-based forest inventory. Silva Fennica vol. 47 no. 5 article id 943. https://doi.org/10.14214/sf.943

Keywords: forest inventory; LIDAR; airborne laser scanning; stratified sampling; area-based approach

Highlights: Using laser data as auxiliary information in the selection of field plot locations helps to decrease costs in forest inventories based on airborne laser scanning; Two independent, differently selected sets of field plots were used for model fitting, and third for validation; Using partial instead of ordinary least squares had no major influence on the results; Forty well placed plots produced fairly reliable volume estimates.

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

Field measurements conducted on sample plots are a major cost component in airborne laser scanning (ALS) based forest inventories, as field data is needed to obtain reference variables for the statistical models. The ALS data also provides an excellent source of prior information that may be used in the design phase of the field survey to reduce the size of the field data set. In the current study, we acquired two independent modeling data sets: one with ALS-assisted and another with random plot selection. A third data set was used for validation. One canopy height and one canopy density variable were used as a basis for the ALS-assisted selection. Ordinary and partial least squares regressions for stem volume were fitted for four different strata using the two data sets separately. The results show that the ALS-assisted plot selection helped to decrease the root mean square error (RMSE) of the predicted volume. Although the differences in RMSE were relatively small, models based on random plot selection showed larger mean differences from the reference in the independent validation data. Furthermore, a sub-sampling experiment showed that 40 well placed plots should be enough for fairly reliable predictions.

Gobakken, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, Ås, Norway E-mail: terje.gobakken@umb.no
Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
Næsset, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, Ås, Norway E-mail: erik.naesset@umb.no

article id 952, category Research article

Lauri Korhonen, Inka Pippuri, Petteri Packalén, Ville Heikkinen, Matti Maltamo, Juho Heikkilä. (2013). Detection of the need for seedling stand tending using high-resolution remote sensing data. Silva Fennica vol. 47 no. 2 article id 952. https://doi.org/10.14214/sf.952

Keywords: forest management; airborne laser scanning; logistic regression; seedling stand; tending; support vector machine

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

Seedling stands are problematic in airborne laser scanning (ALS) based stand level forest management inventories, as the stem density and species proportions are difficult to estimate accurately using only remotely sensed data. Thus the seedling stands must still be checked in the field, which results in an increase in costs. In this study we tested an approach where ALS data and aerial images are used to directly classify the seedling stands into two categories: those that involve tending within the next five years and those which involve no tending. Standard ALS-based height and density features, together with texture and spectral features calculated from aerial images, were used as inputs to two classifiers: logistic regression and the support vector machine (SVM). The classifiers were trained using 208 seedling plots whose tending need was estimated by a local forestry expert. The classification was validated on 68 separate seedling stands. In the training data, the logistic model’s kappa coefficient was 0.55 and overall accuracy (OA) 77%. The SVM did slightly better with a kappa = 0.71 and an OA = 86%. In the stand level validation data, the performance decreased for both the logistic model (kappa = 0.38, OA = 71%) and the SVM (kappa = 0.37, OA = 72%). Thus our approach cannot totally replace the field checks. However, in considering the stands where the logistic model predictions had high reliability, the number of misclassifications reduced drastically. The SVM however, was not as good at recognizing reliable cases.

Korhonen, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
Pippuri, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: inka.pippuri@uef.fi
Packalén, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
Heikkinen, School of Computing, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: ville.heikkinen@uef.fi
Maltamo, School of Forest Sciences, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
Heikkilä, Finnish Forest Centre, Public Services, Maistraatinportti 4 A, FI-00240 Helsinki, Finland E-mail: juho.heikkila@metsakeskus.fi

article id 902, category Research article

Sakari Tuominen, Reija Haapanen. (2013). Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features. Silva Fennica vol. 47 no. 1 article id 902. https://doi.org/10.14214/sf.902

Keywords: forest inventory; airborne laser scanning; biomass modelling

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

Information on forest biomass is required for several purposes, including estimation of forest bioenergy resources and forest carbon stocks. Airborne laser scanning is today considered the most accurate remote sensing method for forest inventory. The three-dimensional nature of laser scanning data enables estimation of the volumes of the tree canopies. The dimensions of the tree canopies show high correlation with the amount of forest biomass. Optical aerial photographs are often used to complement laser data, for improved distinction between tree species. The paper reports on a study testing the estimation of forest biomass variables in two study areas in Southern Finland. The biomass variables were derived on the basis of tree-level field measurements, with biomass models used for pine, spruce, and birch. The sample-plot-level biomass components were derived on the basis of tree-level data and used as reference data for airborne-laser- and aerial‑photograph-based estimation. Results were slightly better for total biomass (RMSE 22.5% and 23.6% for the two study areas) than total volume (RMSE: 23.4% and 26.1%). Species-specific estimation errors were large in general but varied between the study areas, because of differences in their forest structures.

Tuominen, Finnish Forest Research Institute, P.O.Box 18, FI-01301 Vantaa, Finland E-mail: sakari.tuominen@metla.fi
Haapanen, Finnish Forest Research Institute, P.O.Box 18, FI-01301 Vantaa, Finland E-mail: reija.haapanen@gmail.com

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 156, category Research article

Ilkka Korpela, Hans Ole Ørka, Matti Maltamo, Timo Tokola, Juha Hyyppä. (2010). Tree species classification using airborne LiDAR – effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fennica vol. 44 no. 2 article id 156. https://doi.org/10.14214/sf.156

Keywords: airborne laser scanning; ALS; laser; Optech ALTM3100; Leica ALS50-II; canopy; crown modeling; monoplotting; backscatter amplitude; intensity; discriminant analysis

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Tree species identification constitutes a bottleneck in remote sensing-based forest inventory. In passive images the differentiating features overlap and bidirectional reflectance hampers analysis. Airborne LiDAR provides radiometric and geometric information. We examined the single-trees-level response of two LiDAR sensors in over 13 000 forest trees in southern Finland. We focused on the commercially important species. Our aims were to 1) explore the relevant LiDAR features and study their dependencies on stand and tree variables, 2) examine two sensors and their fusion, 3) quantify the gain from intensity normalizations, 4) examine the importance of the size of the training set, and 5) determine the effects of stand age and site fertility. A set of 570 semiurban broad-leaved trees and exotic conifers was analyzed to 6) examine the LiDAR signal in the economically less important species. An accuracy of 88 90% was achieved in the classification of Scots pine, Norway spruce, and birch, using intensity variables. Spruce and birch showed the highest levels of confusion. Downsizing the training set from 30% to 2.5% of all trees had only a marginal effect on the performance of classifiers. The intensity features were dependent on the absolute and relative sizes of trees, especially for birch. The results suggest that leaf size, orientation, and foliage density affect the intensity, which is thus not affected by reflectance only. Some of the ecologically important species in Finland may be separable, since they gave rise to high intensity values. Comparison of the sensors implies that performance of the intensity data for species classification varies between sensors for reasons that remained uncertain. Both range and gain receiver normalization improved species classification. Weighting of the intensity values improved the fusion of two LiDAR datasets.

Korpela, University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ilkka.korpela@helsinki.fi
Ørka, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O.Box 5003, NO-1432 Ås, Norway E-mail: hoo@nn.no
Maltamo, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
Tokola, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tt@nn.fi
Hyyppä, Finnish Geodetic Institute, Department of Photogrammetry and Remote Sensing, P.O.Box 15, FI-02431 Masala, Finland E-mail: jh@nn.fi

article id 164, category Research article

Aki Suvanto, Matti Maltamo. (2010). Using mixed estimation for combining airborne laser scanning data in two different forest areas. Silva Fennica vol. 44 no. 1 article id 164. https://doi.org/10.14214/sf.164

Keywords: airborne laser scanning; area-based method; mixed estimation; regression models

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Airborne laser scanning (ALS) data have become the most accurate remote sensing technology for forest inventories. When planning new inventories the costs of fieldwork could be reduced if datasets of old inventory areas are effectively reused in the new area. The aim of this study was to apply mixed estimation using a combination of existing and new field datasets in area-based approach. Additionally, combining datasets with mixed estimation was compared with constructing new local models with smaller datasets. The two forest study areas were in Juuka and Matalansalo, which are located about 120 km apart in eastern Finland. ALS-based regression models were constructed using datasets of Matalansalo (472 reference plots) and Juuka (10–212 reference plots). Models were developed for the basal area median tree diameter and height, mean tree height, stem number, basal area and volume. The work was based on a simulation approach which involved five methods for approximating the regression coefficients. The first method merged the datasets using ordinary least squares (OLS) regression models, whereas the second and third methods combined datasets using mixed estimation on different weighting principles, and the final two estimated local models with predetermined and new independent variables. The results indicate that mixed estimation can improve the accuracy of derived stand variables compared with basic OLS models. Additionally, a sample of 40–50 plots was enough to build local models for basal area and volume and produce at least the equal accuracy of results than any other methods in this study.

Suvanto, Blom Kartta Oy, Teollisuuskatu 18, FI-80100 Joensuu, Finland E-mail: aki.suvanto@blomasa.com
Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box, FI-80101, Joensuu, Finland E-mail: mm@nn.fi

article id 335, category Research article

Markus Holopainen, Mervi Talvitie. (2006). Effect of data acquisition accuracy on timing of stand harvests and expected net present value. Silva Fennica vol. 40 no. 3 article id 335. https://doi.org/10.14214/sf.335

Keywords: forest inventory; laser scanning; digital aerial photographs; digital photogrammetry; net present value; expected net present value loss

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Modern remote sensing provides cost-efficient spatial digital data that are more accurate than before. However, the influence of increased accuracy and cost-efficiency on simulations of forest management planning has not been evaluated. The aim of the present study was to analyse the effect of data acquisition accuracy on standwise forest inventory by comparing the accuracy and cost of traditional compartmentwise inventory methods with 2D and 3D measurements of digital aerial photographs and airborne laser scanning. Comparison was based on the expected net present value (NPV), i.e. economic losses that consisted of the inventory costs and incorrect timings of treatments. The reference data, totalling 700 ha, were measured from Central Park in the city of Helsinki, Finland. The data were simulated to final cut with a MOTTI simulator, which is a stand-level analysis tool that can be used to assess the effects of alternative forest management practices on growth and timber yield. The results showed that when inventory costs were not considered there were no significant differences between the expected NPV losses in 3D measurements of digital aerial photographs, laser scanning and the compartmentwise method. When inventory costs were taken into account, the compartmentwise method was still the most efficient inventory method in the study area. Forest inventories, however, are usually directed to larger areas when the costs per hectare of remote-sensing methods decrease. As a result of better accuracies, 3D and compartmentwise methods always produce better results than the 2D method when NPV losses are accounted. Simulations of this type are based on the accuracies and costs of the 3D data available today, assuming that the data can be used in tree-level measurements.

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
Talvitie, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: mt@nn.fi

article id 355, category Research article

Ilkka Korpela. (2006). Geometrically accurate time series of archived aerial images and airborne lidar data in a forest environment. Silva Fennica vol. 40 no. 1 article id 355. https://doi.org/10.14214/sf.355

Keywords: vegetation; canopy; monitoring; laser scanning; change detection; photogrammetry; 3D; aerial triangulation; direct georeferencing

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Reconstructing three-dimensional structural changes in the forest over time is possible using archived aerial photographs and photogrammetric techniques, which have recently been introduced to a larger audience with the advent of digital photogrammetry. This paper explores the feasibility of constructing an accurate time-series of archived aerial photographs spanning 42 years using different types of geometric data and estimation methods for image orientation. A recent airborne laser scanning (lidar) data set was combined with the image block and assessed for geometric match. The results suggest that it is possible to establish the multitemporal geometry of an image block to an accuracy that is better than 0.5 m in 3D and constant over time. Even geodetic ground control points can be omitted from the estimation if the most recent images have accurate direct sensor orientation, which is becoming a standard technique in aerial photography. This greatly reduces the costs and facilitates the work. An accurate multitemporal image block combined with recent lidar scanning for the estimation of topography allows accurate monitoring and retrospective analysis of forest vegetation and management operations.

Korpela, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ilkka.korpela@helsinki.fi

Category : Research note

article id 10557, category Research note

Mikko T. Niemi. (2021). Improvements to stream extraction and soil wetness mapping within a forested catchment by increasing airborne LiDAR data density – a case study in Parkano, western Finland. Silva Fennica vol. 55 no. 5 article id 10557. https://doi.org/10.14214/sf.10557

Keywords: remote sensing; interpolation; laser scanning; digital elevation model conditioning; overland flow routing; soil drainage; wetness index

Highlights: Overland flow routing can be improved with high-density airborne LiDAR data; Kriging and inverse-distance weighting outperformed triangulated irregular networks in DEM interpolation; A hybrid breaching-filling workflow performed well for DEM conditioning in the Finnish landscape; Enhanced stream extraction and soil wetness mapping contribute to multi-purpose precision forestry.

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

The pulse density of airborne Light Detection and Ranging (LiDAR) is increasing due to technical developments. The trade-offs between pulse density, inventory costs, and forest attribute measurement accuracy are extensively studied, but the possibilities of high-density airborne LiDAR in stream extraction and soil wetness mapping are unknown. This study aimed to refine the best practices for generating a hydrologically conditioned digital elevation model (DEM) from an airborne LiDAR -derived 3D point cloud. Depressionless DEMs were processed using a stepwise breaching-filling method, and the performance of overland flow routing was studied in relation to a pulse density, an interpolation method, and a raster cell size. The study area was situated on a densely ditched forestry site in Parkano municipality, for which LiDAR data with a pulse density of 5 m^–2 were available. Stream networks and a topographic wetness index (TWI) were derived from altogether 12 DEM versions. The topological database of Finland was used as a ground reference in comparison, in addition to 40 selected main flow routes within the catchment. The results show improved performance of overland flow modeling due to increased data density. In addition, commonly used triangulated irregular networks were clearly outperformed by universal kriging and inverse-distance weighting in DEM interpolation. However, the TWI proved to be more sensitive to pulse density than an interpolation method. Improved overland flow routing contributes to enhanced forest resource planning at detailed spatial scales.

Niemi, Department of Forest Sciences, University of Helsinki, FI-00014 University of Helsinki, Finland E-mail: mikko.t.niemi@helsinki.fi

article id 10533, category Research note

Daniel Schraik, Aarne Hovi, Miina Rautiainen. (2021). Estimating cover fraction from TLS return intensity in coniferous and broadleaved tree shoots. Silva Fennica vol. 55 no. 4 article id 10533. https://doi.org/10.14214/sf.10533

Keywords: terrestrial laser scanning; leaf area; lidar intensity; physically-based; voxel

Highlights: We developed a method to obtain the fraction of TLS pulses’ footprint area covered by a target’s projection area; We tested our method with shoots of Norway spruce, Scots pine and silver birch; We provide a physically-based framework related to unmeasured variables, and provide a robust statistical framework to deal with uncertainty.

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

Terrestrial laser scanning (TLS) provides a unique opportunity to study forest canopy structure and its spatial patterns such as foliage quantity and dispersal. Using TLS point clouds for estimating leaf area density with voxel-based methods is biased by the physical dimensions of laser beams, which violates the common assumption of beams being infinitely thin. Real laser beams have a footprint size larger than several millimeters. This leads to difficulties in estimating leaf area density from light detection and ranging (LiDAR) in vegetation, where the target objects can be of similar or even smaller size than the beam footprint. To compensate for this bias, we propose a method to estimate the per-pulse cover fraction, defined as the fraction of laser beams’ footprint area that is covered by vegetation targets, using the LiDAR return intensity and an experimental calibration measurement. We applied this method to a Leica P40 single-return instrument, and report our experimental results. We found that conifer foliage had a lower average per-pulse cover fraction than broadleaved foliage, indicating an increased number of partial hits in conifer foliage. We further discuss limitations of our method that stem from unknown target properties that influence the LiDAR return intensity and highlight potential ways to overcome the limitations and manage the remaining uncertainty. Our method’s output, the per-beam cover fraction, may be useful in a weight function for methods that estimate leaf area density from LiDAR point clouds.

Schraik, Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00076 Aalto, Finland; https://orcid.org/0000-0002-7794-3918 E-mail: daniel.schraik@aalto.fi
Hovi, Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00076 Aalto, Finland; https://orcid.org/0000-0002-4384-5279 E-mail: aarne.hovi@aalto.fi
Rautiainen, Aalto University, School of Engineering, Department of Built Environment, P.O. Box 14100, FI-00076 Aalto, Finland; Aalto University, School of Electrical Engineering, Department of Electronics and Nanoengineering, P.O. Box 14100, FI-00076 Aalto, Finland https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi

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