Full text of this article is only available in PDF format.

Michael Vohland (email), Johannes Stoffels, Christina Hau, Gebhard Schüler

Remote sensing techniques for forest parameter assessment: multispectral classification and linear spectral mixture analysis

Vohland M., Stoffels J., Hau C., Schüler G. (2007). Remote sensing techniques for forest parameter assessment: multispectral classification and linear spectral mixture analysis. Silva Fennica vol. 41 no. 3 article id 471. https://doi.org/10.14214/sf.471

Abstract

One of the most common applications of remote sensing in forestry is the production of thematic maps, depicting e.g. tree species or stand age, by means of image classification. Nevertheless, the absolute quantification of stand variables is even more essential for forest inventories. For both issues, satellite data are attractive for their large-area and up-to-date mapping capacities. This study followed two steps, and at first a supervised parametric classification was performed for a German test site based on a radiometrically corrected Landsat-5 TM scene. There, eight forest classes were identified with an overall accuracy of 87.5%. In the following, the study focused on the estimation of one key stand variable, the stem number per hectare (SN), which was carried out for a number of Norway spruce stands that had been clearly identified in the multispectral classification. For the estimation of SN, the approach of Linear Spectral Mixture Analysis (LSMA) was found to be clearly more effective than spectral indices. LSMA is based on the premise that measured reflectances can be linearly modelled from a set of so-called endmember spectra. In this study, the endmember sets were held variable to decompose pixel values to abundances of a vegetation, a background (soil, litter, bark) and a shade fraction. Forest structure determines the visible portions of these fractions, and therefore, a multiple regression using them as predictor variables provided the best SN estimates. LSMA allows a pixel-by-pixel quantification of SN for complete satellite images. This opens the view to exploit these data for an improved calibration of large-scale multi-parameter assessment strategies (e.g. statistical modelling or the kNN method for satellite data interpretation).

Keywords
Picea abies; remote sensing; stand variables; stem number; multispectral classification; Linear Spectral Mixture Analysis

Author Info
  • Vohland, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail mv@nn.de (email)
  • Stoffels, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail js@nn.de
  • Hau, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail ch@nn.de
  • Schüler, Research Institution for Forest Ecology and Forestry (FAWF), Department of Forest Growth and Silviculture, Trippstadt, Germany E-mail gs@nn.de

Received 28 February 2007 Accepted 13 July 2007 Published 31 December 2007

Views 6774

Available at https://doi.org/10.14214/sf.471 | Download PDF

Creative Commons License CC BY-SA 4.0

Register
Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content

Your selected articles
Send to email
Mola-Yudego B., Picchi G. et al. (2015) Assessing chipper productivity and operator effe.. Silva Fennica vol. 49 no. 5 article id 1342 (remove) | Edit comment
Kuusela K., Kilkki P. (1963) Multiple regression of increment percentage on o.. Acta Forestalia Fennica vol. 75 no. 4 article id 7138 (remove) | Edit comment
Putkisto K., (1959) Effect of the mechanization of timber preparatio.. Silva Fennica vol. 0 no. 101 article id 4686 (remove) | Edit comment
Wallenius T., (2002) Forest age distribution and traces of past fires.. Silva Fennica vol. 36 no. 1 article id 558 (remove) | Edit comment
Lähteenmäki-Uutela A., Rantala S. et al. (2023) Increasing access to forest data for enhancing f.. Silva Fennica vol. 57 no. 3 article id 23034 (remove) | Edit comment
Kangas A., Mehtätalo L. et al. (2007) Modelling percentile based basal area weighted d.. Silva Fennica vol. 41 no. 3 article id 282 (remove) | Edit comment
Omwami R. K., (1986) A theory of stumpage appraisal. Silva Fennica vol. 20 no. 3 article id 5273 (remove) | Edit comment
Meurman O., (1963) Notes on ornamental trees and shrubs at the Depa.. Acta Forestalia Fennica vol. 76 no. 3 article id 7143 (remove) | Edit comment
Cajander A. K., (1923) On the division of fertile soils in Finland and .. Acta Forestalia Fennica vol. 25 no. 3 article id 7078 (remove) | Edit comment
Riihinen P., (1971) A contribution to discussion on the application .. Silva Fennica vol. 5 no. 3 article id 4851 (remove) | Edit comment
Heikinheimo L., Ervasti S. et al. (1959) Development of forest economic research in Finland Acta Forestalia Fennica vol. 70 no. 10 article id 7500 (remove) | Edit comment
Vohland M., Stoffels J. et al. (2007) Remote sensing techniques for forest parameter a.. Silva Fennica vol. 41 no. 3 article id 471 (remove) | Edit comment
Your search results