The fusion of hyperspectral and LiDAR data provides a synergistic ability, information from each data source to acquire more robust diversity monitoring results. Previous studies have integrated structural features extracted by LiDAR data and spectral characteristics from hyperspectral images for directly discriminating tree species by using classification techniques, including linear discrimination analysis (Alonzo et al., 2014), support vector machine (Dalponte et al., 2012), random forest (Liu et al., 2017) and spectral angle mapper.
Mayra et al. (2021) compared the performance of different classification methods for identifying the major tree species in a boreal forest based on airborne hyperspectral and LiDAR data. Assessing forest species diversity using remote sensing classification methods has the advantage of providing spatially explicit species Spain phone number list distribution information for each ITC or pixel. However, it remains challenging to directly discriminate the species of all individuals accurately in complex subtropical or tropical forests due to the potential spectral or structural similarity among different species or differences existing for the same species.
The confusion in classification usually increases with increasing biodiversity levels and more training data for species-rich forests is usually needed to improve the classification accuracy. Moreover, collecting sufficient training and validation data for each tree species in species-rich and topographically complex forests can be a challenging task. Although some methods are relatively capable of classifying trees with limited training samples (Christian et al., 2013; Awad, 2018), the classification results are achieved using specific images and algorithms with relatively lower transferability.