Poster   2016

Trier, Øivind Due; Kermit, Martin Andreas; Rudjord, Øystein; Gobakken, Terje; Næsset, Erik; Aarsten, Dagrun

Publikasjonsdetaljer

Arrangør:

European Association of Remote Sensing Laboratories

Lenker:

SAMMENDRAG: www.conftool.net/EARSeL-WS-Forest-2016-Krakow/index.php?page=browseSessions&form_session=17#paperID23

This paper presents a research collaboration to develop more automated methods for forest inventory in Norway and Scandinavia.

The current situation in the forest industry in Norway is difficult due to reduced timber prices and high labor cost. Currently forest inventory methods combine airborne laser scanning (ALS) data and manual photointerpretation using multispectral imagery (broad visual and near-infrared channels), but extensive field work is needed in addition. This makes forest inventory very expensive for large areas.

In this perspective, more automated methods for forest inventory are needed. Specifically, we will focus on methods which combine data from simultaneously acquired airborne laser scanning and imaging spectrometer. The ALS data already provides information on vegetation height. The hyperspectral data may provide information on biophysical and biochemical parameters, and species composition. Combined, the two types of data have the potential of more accurate forest inventory with less fieldwork.

Hyperspectral and ALS data were acquired simultaneously for a forest area in Våler municipality, Østfold County, Norway. Field work was conducted to identify examples of individual trees and small clusters of trees of one single species. Approximately 169 tree polygons were delineated using ALS data.
A set of sample spectra based on in situ data was prepared for each of the three species pine, spruce and birch. The mean spectra were found for each tree species, by averaging over all the samples. From these data we identified three regions where the tree species may be differentiated: 544 nm (green), 674 nm (red) and 710 nm (red edge). We used the three bands from these regions to create two indices in order to separate the different tree species.

Firstly, ALS data was used to create a mask, removing areas where the vegetation was lower than 1 meter. Secondly, thresholds on the green 544 nm band and on broadband NDVI were applied to remove shadows and areas without live vegetation. Finally, the two proposed indices were used to differentiate the tree species.
The resulting tree species classification map has clusters of homogeneously classified pixels, corresponding to individual trees or groups of trees of the same species. Close inspection reveals some occasional misclassified pixels and/or pixels of a different class than the majority within a cluster. The pixel-based accuracies for each species are in the range 83-86%.

In photointerpretation of broadband multispectral images, it is a well-known problem that young spruce may be confused spectrally with birch, and old spruce with young pine. This indicates a potential to reduce the confusion between birch and spruce and between spruce and pine, by also considering the tree height in the classification method. The tree height, which is possible to derive from the ALS data, is a good proxy for tree age.