Tree species classification using airborne hyperspectral data in subtropical mountainous forest

Hyperspectral remote sensing data have great potential to identify ground objects and classify tree species. However, the tree species classification based on hyperspectral data in the subtropical region of hilly landscape have always been challenged by rugged topography. We conducted our research in subtropical mountainous forests in Pu'er of Yunnan province in southwestern China. This research investigated the capability of airborne AISA Eagle II hyperspectral data in tree species classification, especially in mountainous areas. Integrated Radiometric Correction (IRC) is applied as atmospheric and topographic correction technique; a classification algorithm was developed based on the topographic-corrected data. Principal Component Analysis (PCA) method was used to reduce the dimension of hyperspectral data before classifying tree species. The first three components indices combined with texture features were used for Support Vector Machine (SVM) classification. Study results demonstrated that this developed method obtained a good performance in detecting the target tree species for the overall classification accuracy is 95.14% and kappa coefficient is 0.93.