Vitenskapelig artikkel

Tree species classification with hyperspectral imaging and lidar

Rudjord, Øystein; Trier, Øivind Due


Tidsskrift: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, p. 1–4, 2017

Utgivere: IEEE

Internasjonale standardnumre:
Trykt: 2158-6276


This paper presents a new method to discriminate between spruce, pine and birch, which are the dominating tree species in Norwegian forests. For this purpose, simultaneously acquired airborne laser scanning (ALS) and hyperspectral data are used. The laser scanning data was used to mask pixels with low or no vegetation in the hyperspectral data. From the species-specific spectra, three wavelengths were identified for species discrimination: 544 nm (green), 674 nm (red) and 710 nm (red edge). A decision tree-based pixel classification method obtained 83-86% correct classification. We plan a field revisit to include misclassified trees in an extended in situ data set, and then to re-calibrate and re-run the classifier. There is also potential for improvement by using individual tree crown delineation. Further, the vegetation height could potentially be used to improve classification.