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Silva Fennica 1926-1997
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Acta Forestalia Fennica
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Articles containing the keyword 'segmentation'.

Category: Research article

article id 1414, category Research article
Rami Saad, Jörgen Wallerman, Johan Holmgren, Tomas Lämås. (2016). Local pivotal method sampling design combined with micro stands utilizing airborne laser scanning data in a long term forest management planning setting. Silva Fennica vol. 50 no. 2 article id 1414. https://doi.org/10.14214/sf.1414
Highlights: Most similar neighbor imputation was used to estimate forest variables using airborne laser scanning data as auxiliary data; For selecting field reference plots the local pivotal method (LPM) was compared to systematic sampling design; The LPM sampling design combined with a micro stand approach showed potential for improvement and has the potential to be a competitive method when considering cost efficiency.

A new sampling design, the local pivotal method (LPM), was combined with the micro stand approach and compared with the traditional systematic sampling design for estimation of forest stand variables. The LPM uses the distance between units in an auxiliary space – in this case airborne laser scanning (ALS) data – to obtain a well-spread sample. Two sets of reference plots were acquired by the two sampling designs and used for imputing data to evaluation plots. The first set of reference plots, acquired by LPM, made up four imputation alternatives (varying number of reference plots) and the second set of reference plots, acquired by systematic sampling design, made up two alternatives (varying plot radius). The forest variables in these alternatives were estimated using the nonparametric method of most similar neighbor imputation, with the ALS data used as auxiliary data. The relative root mean square error (RelRMSE), stem diameter distribution error index and suboptimal loss were calculated for each alternative, but the results showed that neither sampling design, i.e. LPM vs. systematic, offered clear advantages over the other. It is likely that the obtained results were a consequence of the small evaluation dataset used in the study (n = 30). Nevertheless, the LPM sampling design combined with the micro stand approach showed potential for improvement and might be a competitive method when considering the cost efficiency.

  • Saad, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden ORCID ID:E-mail: rami.saad@slu.se (email)
  • Wallerman, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden ORCID ID:E-mail: jorgen.wallerman@slu.se
  • Holmgren, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden ORCID ID:E-mail: johan.holmgren@slu.se
  • Lämås, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden ORCID ID:E-mail: tomas.lamas@slu.se
article id 155, category Research article
Minna Räty, Annika Kangas. (2010). Segmentation of model localization sub-areas by Getis statistics. Silva Fennica vol. 44 no. 2 article id 155. https://doi.org/10.14214/sf.155
Models for large areas (global models) are often biased in smaller sub-areas, even when the model is unbiased for the whole area. Localization of the global model removes the local bias, but the problem is to find homogenous sub-areas in which to localize the function. In this study, we used the eCognition Professional 4.0 (later versions called Definies Pro) segmentation process to segment the study area into homogeneous sub-areas with respect to residuals of the global model of the form height and/or local Getis statistics calculated for the residuals, i.e., Gi*-indices. The segmentation resulted in four different rasters: 1) residuals of the global model, 2) the local Gi*-index, and 3) residuals and the local Gi*-index weighted by the inverse of the variance, and 4) without weighting. The global model was then localized (re-fitted) for these sub-areas. The number of resulting sub-areas varied from 4 to 366. On average, the root mean squared errors (RMSEs) were 3.6% lower after localization than the global model RMSEs in sub-areas before localization. However, the localization actually increased the RMSE in some sub-areas, indicating the sub-area were not appropriate for local fitting. For 56% of the sub-areas, coordinates and distance from coastline were not statistically significant variables, in other words these areas were spatially homogenous. To compare the segmentations, we calculated an aggregate standard error of the RMSEs of the single sub-areas in the segmentation. The segmentations in which the local index was present had slightly lower standard errors than segmentations based on residuals.
  • Räty, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail: minna.s.raty@helsinki.fi (email)
  • Kangas, University of Helsinki, Department of Forest Sciences, P.O. Box 27 (Latokartanonkaari 7), FI-00014 University of Helsinki, Finland ORCID ID:E-mail:

Category: Article

article id 7675, category Article
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

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, ORCID ID:E-mail:

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