Light Detection And Ranging (LiDAR) is an important branch of remote sensing (RS) technology, and its hardware and software in practical applications are getting more and more mature. Now, it is time for the community to think about its future, and a potential way of further pushing forward LiDAR RS technical progress, no doubt, is to develop its nextgeneration systems and approaches. Hyperspectral LiDAR is such a representative case, which, theoretically, is designed to synchronously collect the spectral and range information of objects. This advantage can inherently handle the errors caused when fusing those corresponding hypespectral images and point clouds in the traditional routines of 4D mapping, and hence, has attracted numerous attention on developing its prototype systems. With the performance enhancements of such prototype systems, more efforts need to be deployed onto pushing these prototypes to practical applications. In the case of the hyperspectral LiDAR prototype system developed by the Finnish Geospatial Research Institute, this study examined its applicability for investigating the intraday 3D variations of tree biophysics and biochemistry. The collected point clouds proved to be able to characterize the biophysical variation of trees in terms of laser point-represented tree geometrical centre. For the aspect of biochemical characterization, the hyperspectral LiDAR was validated through the retrievals of the 3D distributions of the fractions of photosynthetically active radiation (FAPARs), crown chlorophyll concentrations, and crown nitrogen concentrations, and the intraday biochemical variations were characterized by their day-and-night differences. The tests showed that hyperspectral LiDAR will be a kind of technology of high potentials for mapping biophysics and biochemistry and their dynamics.