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Articles by Ilkka Korpela

Category : Research article

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 1087, category Research article

Ilkka Korpela, Lauri Mehtätalo, Lauri Markelin, Anne Seppänen, Annika Kangas. (2014). Tree species identification in aerial image data using directional reflectance signatures. Silva Fennica vol. 48 no. 3 article id 1087. https://doi.org/10.14214/sf.1087

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

Highlights: Multispectral reflectance data showed a strong and spectrally correlated tree effect; There was no gain in species classification from using species-specific differences of directional reflectance in real data and only a marginal improvement in simulated data; The directional signatures extracted in multiple images are obscured by the intrinsic within-species variation, correlated observations and inherent reflectance calibration errors.

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

Tree species identification using optical remote sensing is challenging. Modern digital photogrammetric cameras enable radiometrically quantitative remote sensing and the estimation of reflectance images, in which the observations depend largely on the reflectance properties of targets. Previous research has shown that there are species-specific differences in how the brightness observed changes when the viewing direction in an aerial image is altered. We investigated if accounting for such directional signatures enhances species classification, using atmospherically corrected, real and simulated multispectral Leica ADS40 line-camera data. Canopy in direct and diffuse illumination were differentiated and species-specific variance-covariance structures were analyzed in real reflectance data, using mixed-effects modeling. Species classification simulations aimed at elucidating the level of accuracy that can be achieved by using images of different quality, number and view-illumination geometry. In real data, a substantial variance component was explained by tree effect, which demonstrates that observations from a tree correlate between observation geometries as well as spectrally. Near-infrared band showed the strongest tree effect, while the directionality was weak in that band. The gain from directional signatures was insignificant in real data, while simulations showed a potential gain of 1–3 percentage points in species classification accuracy. The quality of reflectance calibration was found to be important as well as the image acquisition geometry. We conclude that increasing the number of image observations cancels out random observation noise and reflectance calibration errors, but fails to eliminate the tree effect and systematic calibration inaccuracy. Directional reflectance constitutes a marginal improvement in tree species classification.

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

article id 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

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

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 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

Keywords: silviculture; remote sensing; forest regeneration; classification; species

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

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 E-mail: ilkka.korpela@helsinki.fi
Tuomola, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland E-mail: tt@nn.fi
Tokola, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland E-mail: tt@nn.fi
Dahlin, University of Helsinki, Dept of Forest Management, P.O. Box 27, FI-00014 University of Finland E-mail: bd@nn.fi

article id 283, category Research article

Ilkka Korpela, Tuukka Tuomola, Esko Välimäki. (2007). Mapping forest plots: an efficient method combining photogrammetry and field triangulation. Silva Fennica vol. 41 no. 3 article id 283. https://doi.org/10.14214/sf.283

Keywords: remote sensing; least squares adjustment; intertree positioning; spatial resection; trilateration

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

Intra stand spatial information is often collected in ecological investigations, when functioning or interactions in the ecosystem are studied. Local relative accuracy is often given priority in such cases. Forest maps with accurate absolute positions in a global coordinate system are needed in remote-sensing applications for validation and calibration purposes. Establishing the absolute position is particularly difficult under a canopy as is creating undistorted coordinate systems for large plots in the forest. We present a method that can be used for the absolute mapping of point features under a canopy that is efficient for large forest plots. In this method, an undistorted network of control points is established in the forest using photogrammetric observations of treetops. These points are used for the positioning of other points, using redundant observations of interpoint distances and azimuths and a least squares adjustment. The method provides decimetre-level accuracy and only one person is required to conduct the work. An estimate of the positioning accuracy of each point is readily available in the field. We present the method, a simulation study that explores the potential of the method and results from an experiment in a mixed boreal stand in southern Finland.

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

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

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

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