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Articles by Timo Tokola

Category: Research article

article id 1405, category Research article
Lauri Korhonen, Daniela Ali-Sisto, Timo Tokola. (2015). Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data. Silva Fennica vol. 49 no. 5 article id 1405. https://doi.org/10.14214/sf.1405
Highlights: The fusion of airborne lidar data and satellite images enables accurate canopy cover mapping; The zero-and-one inflated beta regression is demonstrated in large area estimation; Forest/non-forest classification should be done directly, for example by using logistic regression.

The fusion of optical satellite imagery, strips of lidar data and field plots is a promising approach for the inventory of tropical forests. Airborne lidars also enable an accurate direct estimation of the forest canopy cover (CC), and thus a sample of lidar strips can be used as reference data for creating CC maps which are based on satellite images. In this study, our objective was to validate CC maps obtained from an ALOS AVNIR-2 satellite image wall-to-wall, against a lidar-based CC map of a tropical forest area located in Laos. The reference CC values which were needed for model training were obtained from a sample of four lidar strips. Zero-and-one inflated beta regression (ZOINBR) models were applied to link the spectral vegetation indices derived from the ALOS image with the lidar-based CC estimates. In addition, we compared ZOINBR and logistic regression models in the forest area estimation by using >20% CC as a forest definition. Using a total of 409 217 30 × 30 m population units as validation, our model showed a strong correlation between lidar-based CC and spectral satellite features (root mean square error = 12.8%, R2 = 0.82). In the forest area estimation, a direct classification using logistic regression provided better accuracy than the estimation of CC values as an intermediate step (kappa = 0.61 vs. 0.53). It is important to obtain sufficient training data from both ends of the CC range. The forest area estimation should be done before the CC estimation, rather than vice versa.

  • Korhonen, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland; (current) University of Helsinki, Department of Forest Sciences, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID: http://orcid.org/0000-0002-9352-0114 E-mail: lauri.z.korhonen@helsinki.fi (email)
  • Ali-Sisto, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail: dheikkil@student.uef.fi
  • Tokola, University of Eastern Finland, School of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland. ORCID ID:E-mail: timo.tokola@uef.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
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 ORCID ID:E-mail: ilkka.korpela@helsinki.fi (email)
  • Ørka, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O.Box 5003, NO-1432 Ås, Norway ORCID ID:E-mail:
  • Maltamo, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Tokola, University of Eastern Finland, School of Forest Science, P.O. Box 111, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Hyyppä, Finnish Geodetic Institute, Department of Photogrammetry and Remote Sensing, P.O.Box 15, FI-02431 Masala, Finland ORCID ID:E-mail:
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
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 ORCID ID:E-mail: matti.maltamo@joensuu.fi (email)
  • Peuhkurinen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Malinen, Finnish Forest Research Institute, Joensuu Research Unit, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Vauhkonen, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Packalén, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
  • Tokola, University of Joensuu, Faculty of Forest Sciences, FI-80101 Joensuu, Finland ORCID ID:E-mail:
article id 466, category Research article
Ilkka Korpela, Tuukka Tuomola, Timo Tokola, Bo Dahlin. (2008). Appraisal of seedling stand vegetation with airborne imagery and discrete-return LiDAR – an exploratory analysis. Silva Fennica vol. 42 no. 5 article id 466. https://doi.org/10.14214/sf.466
The potential for combined use of airborne discrete-return LiDAR and digital imagery in the classification and measurement of common seedling stand vegetation was examined in southern Finland (61°50’N, 24°20’E). Classification was based on spectral and textural image features in addition to geometric and radiometric features of the LiDAR. The accuracy of leaf-on, LiDAR-based terrain elevation models was tested as well as the accuracy of LiDAR in the measurement of vegetation heights. LiDAR-based canopy height and the range-normalized intensity of the LiDAR were strong explanatory variables in vegetation classification. Interspecies variation was observed in the height measurement accuracy of LiDAR for different tree, shrub and low vegetation canopies. Elevation models derived with 1–15 pulses per m2 showed an inherent noise of app. 15–25 cm, which restricts the use of LiDAR in regeneration assessment of very young stands. The spatial pattern of the competing vegetation was reproduced in classification-based raster surfaces, which could be useful in deriving meaningful treatment proposals.
  • Korpela, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland ORCID ID:E-mail: ilkka.korpela@helsinki.fi (email)
  • Tuomola, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland ORCID ID:E-mail:
  • Tokola, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland ORCID ID:E-mail:
  • Dahlin, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland ORCID ID:E-mail:
article id 386, category Research article
Jouni Kalliovirta, Timo Tokola. (2005). Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information. Silva Fennica vol. 39 no. 2 article id 386. https://doi.org/10.14214/sf.386
The aim was to investigate the relations between diameter at breast height and maximum crown diameter, tree height and other possible independent variables available in stand databases. Altogether 76 models for estimating stem diameter at breast height and 60 models for tree age were formulated using height and maximum crown diameter as independent variables. These types of models can be utilized in modern remote sensing applications where tree crown dimensions and tree height are measured automatically. Data from Finnish national forest inventory sample plots located throughout the country were used to develop the models, and a separate test site was used to evaluate them. The RMSEs of the diameter models for the entire country varied between 7.3% and 14.9% from the mean diameter depending on the combination of independent variables and species. The RMSEs of the age models for entire country ranged from 9.2% to 12.8% from the mean age. The regional models were formulated from a data set in which the country was divided into four geographical areas. These regional models reduced local error and gave better results than the general models. The standard deviation of the dbh estimate for the separate test site was almost 5 cm when maximum crown width alone was the independent variable. The deviation was smallest for birch. When tree height was the only independent variable, the standard deviation was about 3 cm, and when both height and maximum crown width were included it was under 3 cm. In the latter case, the deviation was equally small (11%) for birch and Norway spruce and greatest (13%) for Scots pine.
  • Kalliovirta, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail:
  • Tokola, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland ORCID ID:E-mail: timo.tokola@helsinki.fi

Category: Article

article id 5610, category Article
Timo Tokola, Juho Heikkilä. (1997). Improving satellite image based forest inventory by using a priori site quality information. Silva Fennica vol. 31 no. 1 article id 5610. https://doi.org/10.14214/sf.a8511

The purpose of this study was to test the benefits of a forest site quality map, when applying satellite image-based forest inventory. By combining field sample plot data from national forest inventories with satellite imagery and forest site quality data, it is possible to estimate forest stand characteristics with higher accuracy for smaller areas. The reliability of the estimates was evaluated using the data from a stand-wise survey for area sizes ranging from 0.06 ha to 300 ha. When the mean volume was estimated, a relative error of 14 per cent was obtained for areas of 50 ha; for areas of 30 ha the corresponding figure was below 20 per cent. The relative gain in interpretation accuracy, when including the forest site quality information, ranged between 1 and 6 per cent. The advantage increased according to the size of the target area. The forest site quality map had the effect of decreasing the relative error in Norway spruce (Picea abies) volume estimations, but it did not contribute to Scots pine (Pinus sylvestris) volume estimation procedure.

  • Tokola, ORCID ID:E-mail:
  • Heikkilä, ORCID ID:E-mail:

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