Wei Du
ABSTRACT
With the increasing importance of forest resources management, accurate classification of forest tree species has become a key task. Based on airborne LiDAR (Light Detection and Ranging) and hyperspectral technology, a comprehensive application system is proposed to improve the accuracy of automatic classification of forest tree species. In this study, the airborne LiDAR data, and AISA Eagle Ⅱ hyperspectral data of a natural forest in mountainous area of a forest farm were taken as the objects, and a combined system of airborne LIDAR and hyperspectral data was established to study the classification of main forest species in mountainous areas. In this experiment, the image obtained by PCA (Principal Component Analysis) transformation and DCHM (Digital canopy height model) data are combined into a fused image by band fusion. In the experiment, RF (Random Forest) method, one of the supervised classification methods, was selected to classify forest tree species from the extracted single-point feature vectors. The findings of the study indicate that, in this experiment, the classification accuracy achieved through fusion surpasses that achieved solely through hyperspectral data and airborne LiDAR. The overall accuracy sees an improvement of 28.8% and 15.6% compared to the individual modalities, accompanied by respective increases in the Kappa coefficient by 0.407 and 0.184. Consequently, the classification performance is deemed optimal, and the level of recognition is notably elevated. When only hyperspectral data is used as the classification basis, there are more misclassification points, but the characteristics of point cloud are improved to some extent. The system of integrated application of airborne LiDAR and hyperspectral technology provides an efficient and accurate solution for forest tree species classification. This method can play an important role in forest resources management, ecological monitoring and other fields, and provide strong support for the protection and sustainable utilization of forest resources.
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Index Terms
- Research on Airborne LiDAR and Hyperspectral Combined System for Forest Tree Species Classification
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