LiDAR provides precise height information which could extract vertical and horizontal information of the forest135 and is less sensitive to saturation when compared with other sensors. Hyperspectral data can provide detailed spectral information which can be used to effectively classify forest species. The findings on forest biomass estimation in the last decades are encouraging, and there is a growing interest for hyperspectral, LiDAR, and fusion of LiDAR and hyperspectral data for forest biomass estimation. However, there are still many limitations that should taken into account: (1) because a forest is a complex ecosystem, many factors may impact the estimation of forest biomass when using LiDAR and hyperspectral imagery, such as spectral and spatial resolution, co-registration of data from different sources (data from different platforms, different dates or with different resolutions), LiDAR point density, fusion framework, classification algorithm, allometric equations, plot-size, study area, stem density, canopy volume, and height; (2) although the Global Climate Observing System has suggested some accuracy levels, there is still no clear standard for forest biomass estimation. In addition, it is still unclear as to how forest biophysical and biochemical attributes derived from hyperspectral data relate to structural attributes from LiDAR data. The current methods normally add LiDAR elevation information as an extra band of hyperspectral data on the pixel level, or extract metrics, respectively, and classify fused images using different algorithms. In some other studies, hyperspectral and LiDAR data were processed separately. For example, hyperspectral data were used to classify forest species, and LiDAR data were used to identify individual trees, then specific allometric equations were used for forest biomass; (3) it is difficult to estimate forest biomass in forest with complex structures, especially tropical forests. In addition, high-quality ground truth data for validation is still lacking. For example, it is unrealistic to classify all tree species with hyperspectral data101 and classify all species-specific allometric equations; (4) for tree-level biomass estimation, although hyperspectral and LiDAR data have been successfully used, it is still problematic, because it is difficult to allocate individual trees to the proper species. Using a spatial resolution coarser than that of individual tree crown size, it is difficult to classify and attribute individual trees. In this case, tone can assume that there are several species within a pixel area, especially in heterogeneous forests. Using a spatial resolution finer than that of individual tree crown size, it is also difficult to classify and attribute individual trees. As in this case, an individual crown may contain several pixels with different illumination.