This review summarizes the use of LiDAR data fusion in forest observations over the past decade. LiDAR data fusion combines data from multiple sources, such as multispectral, hyperspectral, and radar, to improve the accuracy and resolution of forest attribute estimates. The review highlights the main motivations for data fusion, including enhancing spatial and temporal resolution, improving data density, and overcoming occlusion issues. It also discusses the various applications of LiDAR data fusion, such as tree species classification, biomass estimation, forest structure analysis, tree height measurement, and fuel load assessment. The review identifies two main types of data fusion: data-level fusion, which combines raw datasets from different sources, and feature-level fusion, which merges features extracted from different datasets. The study found that data fusion can significantly improve the accuracy of forest attribute estimates, but it also raises questions about the trade-off between the benefits and the time and effort required for data collection and processing. The review also discusses the challenges of implementing data fusion in operational applications, including the high costs of acquiring and processing multiple datasets, the need for specialized expertise, and the time required for data processing. The authors suggest that future research should focus on improving data fusion methods, establishing common data formats and metadata standards, and promoting open data initiatives to facilitate collaboration among researchers and practitioners. Overall, the review concludes that LiDAR data fusion has the potential to enhance forest observations in a wide range of applications, but careful consideration of the costs and benefits is necessary.This review summarizes the use of LiDAR data fusion in forest observations over the past decade. LiDAR data fusion combines data from multiple sources, such as multispectral, hyperspectral, and radar, to improve the accuracy and resolution of forest attribute estimates. The review highlights the main motivations for data fusion, including enhancing spatial and temporal resolution, improving data density, and overcoming occlusion issues. It also discusses the various applications of LiDAR data fusion, such as tree species classification, biomass estimation, forest structure analysis, tree height measurement, and fuel load assessment. The review identifies two main types of data fusion: data-level fusion, which combines raw datasets from different sources, and feature-level fusion, which merges features extracted from different datasets. The study found that data fusion can significantly improve the accuracy of forest attribute estimates, but it also raises questions about the trade-off between the benefits and the time and effort required for data collection and processing. The review also discusses the challenges of implementing data fusion in operational applications, including the high costs of acquiring and processing multiple datasets, the need for specialized expertise, and the time required for data processing. The authors suggest that future research should focus on improving data fusion methods, establishing common data formats and metadata standards, and promoting open data initiatives to facilitate collaboration among researchers and practitioners. Overall, the review concludes that LiDAR data fusion has the potential to enhance forest observations in a wide range of applications, but careful consideration of the costs and benefits is necessary.