Erkki Tomppo. (1992). Satellite image aided forest site fertility estimation for forest income taxation. Acta Forestalia Fennica no. 229 article id 7675. https://doi.org/10.14214/aff.7675

Keywords: site quality; discriminant analysis; forest taxation; satellite images; segmentation; logistic regression analysis; Markov random field

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

Two operative forest site class estimation methods utilizing satellite images have been developed for forest income taxation purposes. For this, two pixelwise classification methods and two post-processing methods for estimating forest site fertility are compared using different input data. The pixelwise methods are discriminant analysis, based on generalized squared distances, and logistic regression analysis. The results of pixelwise classifications are improved either with mode filtering within forest stands or assuming a Markov random field type dependence between pixels. The stand delineation is obtained by using ordinary segmentation techniques. Optionally, known stand boundaries given by the interpreter can be applied. The spectral values of images are corrected using a digital elevation model of the terrain. Some textural features are preliminary tested in classification. All methods are justified by using independent test data.

A test of the practical methods was carried out and a cost-benefit analysis computed. The estimated cost saving in site quality classification varies from 14% to 35% depending on the distribution of the site classes of the area. This means a saving of about 2.0–4.5 million FMK per year in site fertility classification for income taxation purposes. The cost savings would rise even to 60% if that version of the method were chosen where field checking is totally omitted. The classification accuracy at the forest holding level would still be similar to that of traditional method.

The PDF includes a summary in Finnish.

Tomppo, E-mail: et@mm.unknown

Category : Research article

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 345, category Research article

Pekka Hyvönen, Perttu Anttila. (2006). Change detection in boreal forests using bi-temporal aerial photographs. Silva Fennica vol. 40 no. 2 article id 345. https://doi.org/10.14214/sf.345

Keywords: forest inventory; discriminant analysis; change detection; aerial photography; continuous updating

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

Increased need for timely forest information is leading to continuous updating of stand databases. In continuous updating, stand attributes are estimated in the field after an operation and stored in databases. To find the changes caused by operations and forest damage, a semi-automatic method based on bi-temporal aerial photographs was developed. The test data were classified into three classes: No-change (952 stands), Moderate-change (163 stands) and Considerable-change (44 stands). The aerial photographs were acquired in years 2001 and 2004 with almost the same image specifications. Altogether 110 features at stand level were extracted and used in change detection analysis. The test data were classified with stepwise discriminant analysis. The overall accuracy of classification varied between 75.3 and 84.7%. The considerable changes were found without error, whereas the Moderate-change and No-change classes were often confused. However, 84.2% of thinned stands were classified correctly. The best accuracy in classification was obtained by using the histogram and textural features extracted from the original, uncorrected images. Radiometric correction did not improve the accuracy of classification. Soil type, characteristics of the growing stock and the location of a stand in an image were found to affect the change detection. Before the method can be applied operationally, issues related to, e.g., confusion between No-change and Moderate-change must be solved.

Hyvönen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: pekka.hyvonen@metla.fi
Anttila, University of Joensuu, Faculty of Forestry, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: pa@nn.fi

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