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Articles by Petteri Packalen

Category : Research article

article id 10360, category Research article
Mikko Kukkonen, Eetu Kotivuori, Matti Maltamo, Lauri Korhonen, Petteri Packalen. (2021). Volumes by tree species can be predicted using photogrammetric UAS data, Sentinel-2 images and prior field measurements. Silva Fennica vol. 55 no. 1 article id 10360. https://doi.org/10.14214/sf.10360
Keywords: forest inventory; satellite image; open data; drone; stereo matching; unmanned aircraft system
Highlights: A UAS-based species-specific forest inventory approach that avoids new field measurements is presented; Models were constructed using previously measured training plots and remotely sensed data; Bi-seasonal Sentinel-2 data were beneficial in the prediction of species-specific volumes; RMSE values associated with the prediction of volumes by tree species and total volume at the validation plot level were 33.4–62.6% and 9.0%, respectively.
Abstract | Full text in HTML | Full text in PDF | Author Info

Photogrammetric point clouds obtained with unmanned aircraft systems (UAS) have emerged as an alternative source of remotely sensed data for small area forest management inventories (FMI). Nonetheless, it is often overlooked that small area FMI require considerable field data in addition to UAS data, to support the modelling of forest attributes. In this study, we propose a method whereby tree volumes by species are predicted with photogrammetric UAS data and Sentinel-2 images, using models fitted with airborne laser scanning data. The study area is in a managed boreal forest area in Eastern Finland. First, we predicted total volume with UAS point cloud metrics using a prior regression model fitted in another area with ALS data. Tree species proportions were then predicted by k nearest neighbor (k-NN) imputation based on bi-seasonal Sentinel-2 images without measuring new field plot data. Species-specific volumes were then obtained by multiplying the total volume by species proportions. The relative root mean square error (RMSE) values for total and species-specific volume predictions at the validation plot level (30 m × 30 m) were 9.0%, and 33.4–62.6%, respectively. Our approach appears promising for species-specific small area FMI in Finland and in comparable forest conditions in which suitable field plots are available.

  • Kukkonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mikko.kukkonen@uef.fi (email)
  • Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi
  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
  • Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
article id 10183, category Research article
Tomi Karjalainen, Petteri Packalen, Janne Räty, Matti Maltamo. (2019). Predicting factual sawlog volumes in Scots pine dominated forests using airborne laser scanning data. Silva Fennica vol. 53 no. 4 article id 10183. https://doi.org/10.14214/sf.10183
Keywords: Pinus sylvestris; area based approach; k-NN; sawlog
Highlights: We predicted visually bucked factual sawlog volumes at the 30 × 30 m plot-level with several alternatives; The lowest root mean squared error value of approximately 21% was obtained with a linear mixed-effects model that employed factual sawlog volume as a response variable and airborne laser scanning metrics as predictors; The sawlog reduction model commonly used in Finland performed poorly.
Abstract | Full text in HTML | Full text in PDF | Author Info

The aim in the study was to compare alternatives for the prediction of factual sawlog volumes using airborne laser scanning (ALS) data in Scots pine (Pinus sylvestris L.) dominated forests in eastern Finland. Accurate estimates of factual sawlog volume are desirable to ease the planning of harvesting operations. The factual sawlog volume of pines was derived from visual bucking, i.e. a procedure where the defects were located on each stem during sample plot measurements. For other species, the theoretical sawlog volume was considered also as the factual sawlog volume due to data restrictions. We predicted factual sawlog volume with eight alternatives that were based on either linear mixed-effects models or k-nearest neighbour imputations. An existing sawlog reduction model, commonly used in Finland, was also tested individually and combined with a number of the alternatives, and site type information was also utilised. Model fitting and prediction was implemented at the 15 × 15 m level, but accuracy was assessed at the 30 × 30 m level. The relative root mean squared error (RMSE%) values for the factual sawlog volume predictions varied between 20.9% and 33.5%, and the best accuracy was obtained with a linear mixed-effects model. These results indicate that factual sawlog volumes in Scots pine dominated forests can be predicted with reasonable accuracy with ALS data.

  • Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tomikar@uef.fi (email)
  • Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
  • Räty, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: janne.raty@uef.fi
  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
article id 10179, category Research article
Lauri Korhonen, Jaakko Repola, Tomi Karjalainen, Petteri Packalen, Matti Maltamo. (2019). Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines. Silva Fennica vol. 53 no. 3 article id 10179. https://doi.org/10.14214/sf.10179
Keywords: Pinus sylvestris; LIDAR; crown base height; hierarchical data; individual tree detection; sawlog quality
Highlights: Attributes of individual sawlog-sized pines estimated by transferring ALS-based models between sites; Mixed effects models were more accurate than k-NN imputation tested earlier; Calibration with a small number of field measured trees improved the accuracy.
Abstract | Full text in HTML | Full text in PDF | Author Info

Airborne laser scanning (ALS) data is nowadays often available for forest inventory purposes, but adequate field data for constructing new forest attribute models for each area may be lacking. Thus there is a need to study the transferability of existing ALS-based models among different inventory areas. The objective of our study was to apply ALS-based mixed models to estimate the diameter, height and crown base height of individual sawlog sized Scots pines (Pinus sylvestris L.) at three different inventory sites in eastern Finland. Different ALS sensors and acquisition parameters were used at each site. Multivariate mixed-effects models were fitted at one site and the models were validated at two independent test sites. Validation was carried out by applying the fixed parts of the mixed models as such, and by calibrating them using 1–3 sample trees per plot. The results showed that the relative RMSEs of the predictions were 1.2–6.5 percent points larger at the test sites compared to the training site. Systematic errors of 2.4–6.2 percent points also emerged at the test sites. However, both the RMSEs and the systematic errors decreased with calibration. The results showed that mixed-effects models of individual tree attributes can be successfully transferred and calibrated to other ALS inventory areas in a level of accuracy that appears suitable for practical applications.

  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID http://orcid.org/0000-0002-9352-0114 E-mail: lauri.korhonen@uef.fi (email)
  • Repola, Natural Resources Institute of Finland (Luke), Natural resources, Eteläranta 55, FI-96300 Rovaniemi, Finland E-mail: jaakko.repola@luke.fi
  • Karjalainen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: tomikar@uef.fi
  • Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
  • Maltamo, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: matti.maltamo@uef.fi
article id 1567, category Research article
Eetu Kotivuori, Lauri Korhonen, Petteri Packalen. (2016). Nationwide airborne laser scanning based models for volume, biomass and dominant height in Finland. Silva Fennica vol. 50 no. 4 article id 1567. https://doi.org/10.14214/sf.1567
Keywords: forest inventory; LIDAR; regression analysis; remote sensing; calibration; area-based approach; mixed-effect models
Highlights: Pooled data from nine inventory projects in Finland were used to create nationwide laser-based regression models for dominant height, volume and biomass; Volume and biomass models provided regionally different means than real means, but for dominant height the mean difference was small; The accuracy of general volume predictions was nevertheless comparable to relascope-based field inventory by compartments.
Abstract | Full text in HTML | Full text in PDF | Author Info

The aim of this study was to examine how well stem volume, above-ground biomass and dominant height can be predicted using nationwide airborne laser scanning (ALS) based regression models. The study material consisted of nine practical ALS inventory projects taken from different parts of Finland. We used field sample plots and airborne laser scanning data to create nationwide and regional models for each response variable. The final models had one or two ALS predictors, which were chosen based on the root mean square error (RMSE), and cross-validated. Finally, we tested how much predictions would improve if the nationwide models were calibrated with a small number of regional sample plots. Although forest structures differ among different parts of Finland, the nationwide volume and biomass models performed quite well (leave-inventory-area-out RMSE 22.3% to 33.8%, mean difference [MD] –13.8% to 18.7%) compared with regional models (leave-plot-out RMSE 20.2% to 26.8%). However, the nationwide dominant height model (RMSE 5.4% to 7.7%, MD –2.0% to 2.8%, with the exception of the Tornio region – RMSE 11.4%, MD –9.1%) performed nearly as well as the regional models (RMSE 5.2% to 6.7%). The results show that the nationwide volume and biomass models provided different means than real means at regional level, because forest structure and ALS device have a considerable effect on the predictions. Large MDs appeared especially in northern Finland. Local calibration decreased the MD and RMSE of volume and biomass models. However, the nationwide dominant height model did not benefit much from calibration.

  • Kotivuori, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: eetu.kotivuori@uef.fi (email)
  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@uef.fi
  • Packalen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: petteri.packalen@uef.fi
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 (email)
  • 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 68, category Research article
Maria Villikka, Petteri Packalén, Matti Maltamo. (2012). The suitability of leaf-off airborne laser scanning data in an area-based forest inventory of coniferous and deciduous trees. Silva Fennica vol. 46 no. 1 article id 68. https://doi.org/10.14214/sf.68
Keywords: forest inventory; group-specific estimates; leaf-off data; LIDAR
Abstract | View details | Full text in PDF | Author Info
This study examined the suitability of airborne laser scanner (ALS) data collected under leaf-off conditions in a forest inventory, in which deciduous and coniferous trees need to be separated. All analyses were carried out with leaf-on and leaf-off ALS data collected from the same study area. Additionally, aerial photographs were utilized in the Nearest Neighbor (NN) imputations. An area-based approach was used in this study. Regression estimates of plot volume were more accurate in the case of leaf-off than leaf-on data. In addition, regression models were more accurate in coniferous plots than in deciduous plots. The results of applying leaf-on models with leaf-off data, and vice versa, indicate that leaf-on and leaf-off data should not be combined since this causes serious bias. The total volume and volume by coniferous and deciduous trees was estimated by the NN imputation. In terms of total volume, leaf-off data provided more accurate estimates than leaf-on data. In addition, leaf-off data discriminated between coniferous and deciduous trees, even without the use of aerial photographs. Accurate results were also obtained when leaf-off ALS data were used to classify sample plots into deciduous and coniferous dominated plots. The results indicate that the area-based method and ALS data collected under leaf-off conditions are suitable for forest inventory in which deciduous and coniferous trees need to be distinguished.
  • Villikka, University of Easten Finland, Department of Forest Sciences, Joensuu, Finland E-mail: mv@nn.fi
  • Packalén, University of Easten Finland, Department of Forest Sciences, Joensuu, Finland E-mail: petteri.packalen@uef.fi (email)
  • Maltamo, University of Easten Finland, Department of Forest Sciences, Joensuu, Finland E-mail: mm@nn.fi
article id 203, category Research article
Matti Maltamo, Jussi Peuhkurinen, Jukka Malinen, Jari Vauhkonen, Petteri Packalén, Timo Tokola. (2009). Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data. Silva Fennica vol. 43 no. 3 article id 203. https://doi.org/10.14214/sf.203
Keywords: LIDAR; alpha shape; crown height; height metrics; k-MSN; timber quality
Abstract | View details | Full text in PDF | Author Info
The development of airborne laser scanning (ALS) during last ten years has provided new possibilities for accurate description of the living tree stock. The forest inventory applications of ALS data include both tree and area-based plot level approaches. The main goal of such applications has usually been to estimate accurate information on timber quantities. Prediction of timber quality has not been focused to the same extent. Thus, in this study we consider here the prediction of both basic tree attributes (tree diameter, height and volume) and characteristics describing tree quality more closely (crown height, height of the lowest dead branch and sawlog proportion of tree volume) by means of high resolution ALS data. The tree species considered is Scots pine (Pinus sylvestris), and the field data originate from 14 sample plots located in the Koli National Park in North Karelia, eastern Finland. The material comprises 133 trees, and size and quality variables of these trees were modeled using a large number of potential independent variables calculated from the ALS data. These variables included both individual tree recognition and area-based characteristics. Models for the dependent tree characteristics to be considered were then constructed using either the non-parametric k-MSN method or a parametric set of models constructed simultaneously by the Seemingly Unrelated Regression (SUR) approach. The results indicate that the k-MSN method can provide more accurate tree-level estimates than SUR models. The k-MSN estimates were in fact highly accurate in general, the RMSE being less than 10% except in the case of tree volume and height of the lowest dead branch.
  • Maltamo, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: matti.maltamo@joensuu.fi (email)
  • Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: jp@nn.fi
  • Malinen, Finnish Forest Research Institute, Joensuu Research Unit, FI-80101 Joensuu, Finland E-mail: jm@nn.fi
  • Vauhkonen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: jv@nn.fi
  • Packalén, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: pp@nn.fi
  • Tokola, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland E-mail: tt@nn.fi

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