Michael Vohland, Johannes Stoffels, Christina Hau, Gebhard Schüler. (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

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

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

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).

Vohland, University of Trier, Faculty of Geography and Geosciences, Remote Sensing Department, Trier, Germany E-mail: mv@nn.de
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

Category : Research note

article id 10600, category Research note

Nea Kuusinen, Aarne Hovi, Miina Rautiainen. (2021). Contribution of woody elements to tree level reflectance in boreal forests. Silva Fennica vol. 55 no. 5 article id 10600. https://doi.org/10.14214/sf.10600

Keywords: reflectance model; bark; hyperspectral; spectral mixture analysis

Highlights: Contribution of woody elements to reflectance of boreal tree species was estimated using spectral mixture analysis and airborne hyperspectral data; Mean woody element contribution varied between 0.14–0.19 (Scots pine), 0.12–0.20 (birches) and 0.09–0.10 (Norway spruce).

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

Spectral mixture analysis was used to estimate the contribution of woody elements to tree level reflectance from airborne hyperspectral data in boreal forest stands in Finland. Knowledge of the contribution of woody elements to tree or forest reflectance is important in the context of lea area index (LAI) estimation and, e.g., in the estimation of defoliation due to insect outbreaks, from remote sensing data. Field measurements from four Scots pine (Pinus sylvestris L.), five Norway spruce (Picea abies (L.) Karst.) and four birch (Betula pendula Roth and Betula pubescens Ehrh.) dominated plots, spectral measurements of needles, leaves, bark, and forest floor, airborne hyperspectral as well as airborne laser scanning data were used together with a physically-based forest reflectance model. We compared the results based on simple linear combinations of measured bark and needle/leaf spectra to those obtained by accounting for multiple scattering of radiation within the canopy using a physically-based forest reflectance model. The contribution of forest floor to reflectance was additionally considered. The resulted mean woody element contribution estimates varied from 0.140 to 0.186 for Scots pine, from 0.116 to 0.196 for birches and from 0.090 to 0.095 for Norway spruce, depending on the model used. The contribution of woody elements to tree reflectance had a weak connection to plot level forest variables.

Kuusinen, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland E-mail: nea.kuusinen@aalto.fi
Hovi, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland E-mail: aarne.hovi@aalto.fi
Rautiainen, Department of Built Environment, School of Engineering, Aalto University, P.O. Box 14100, FI-00076 Aalto, Finland; Department of Electronics and Nanoengineering, School of Electrical Engineering, Aalto University, P.O. Box 15500, FI-00076 Aalto, Finland https://orcid.org/0000-0002-6568-3258 E-mail: miina.a.rautiainen@aalto.fi

Click this link to register to Silva Fennica.

If you are a registered user, log in to save your selected articles for later access.

Contents alert

Your selected articles

Follow @SilvaFennica