Potential use of hyperspectral data to classify forest tree species

• Published in: New Zealand journal of forestry science Vol. 48; no. 1; pp. 1 - 13
• Main Authors: Hycza, Tomasz, Stereńczak, Krzysztof, Bałazy, Radomir
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2018; SCION; Scion
• Subjects: AISA; Beech; Biomedical and Life Sciences; Change detection; Classification; Data processing; Forest management; Forestry; Forests; Hyperspectral classification; Hyperspectral imaging; Image classification; Life Sciences; Minimum noise fraction; Pine trees; Rainforests; Remote sensing; Research Article; Species; Species classification; Trees; Trees; AISA; Hyperspectral classification; Minimum noise fraction
• ISSN: 1179-5395; 0048-0134; 1179-5395
• DOI: 10.1186/s40490-018-0123-9

Background Remote sensing techniques and data are becoming increasingly popular in forest management, e.g. for change detection and health condition analysis. Tree species recognition is a fundamental issue in taking forest inventories, especially in carbon budget modelling. Hyperspectral imagery provides an accurate classification results for large areas based on a relatively small amount of training data. Results A hyperspectral image of a forest stand in north-eastern Poland taken using an AISA (Airborne Imaging Spectrometer for Application) Eagle camera was transformed to extract the most valuable spectral differences and was classified into seven tree types (birch, European beech, oak, hornbeam, European larch, Scots pine, and Norway spruce) using nine classification algorithms. The highest overall accuracy and kappa coefficient were 90.3% and 0.9 respectively using three minimum noise fraction bands and maximum likelihood classifier. Conclusions Hyperspectral imaging of forests can be used to classify major forest tree species with a good degree of accuracy. It is time-efficient and user-friendly; however, the data and software required means that this approach is still expensive at present.