This review article examines the advancements and applications of LiDAR data fusion in forest observations over the past decade. The authors conducted a structured literature review to analyze relevant studies, focusing on the motivations, applications, and methods used in LiDAR data fusion. The review highlights that LiDAR fusion with other datasets, such as multispectral, hyperspectral, and radar data, has proven useful for various applications, including tree species identification, aboveground biomass assessments, canopy height measurements, and fuel load assessments. The gains in accuracy and spatial-temporal resolution are generally positive, but the marginal improvements are often questioned from an operational perspective. The review also discusses the challenges and future directions in LiDAR data fusion. Key challenges include the high costs and computational efforts associated with processing multiple datasets, the variability in data quality and resolution, and the need for specialized expertise. The authors emphasize the importance of establishing common data formats and metadata standards to facilitate interoperability and collaboration among researchers. The article concludes by suggesting priorities for advancing LiDAR data fusion, including increasing research in the southern hemisphere, balancing the costs and benefits of data fusion, and promoting open data initiatives to enhance real-time applications and disaster response capabilities. Overall, the review provides a positive outlook for the future of LiDAR data fusion in forest observations, while also highlighting the need to address practical challenges and operational considerations.This review article examines the advancements and applications of LiDAR data fusion in forest observations over the past decade. The authors conducted a structured literature review to analyze relevant studies, focusing on the motivations, applications, and methods used in LiDAR data fusion. The review highlights that LiDAR fusion with other datasets, such as multispectral, hyperspectral, and radar data, has proven useful for various applications, including tree species identification, aboveground biomass assessments, canopy height measurements, and fuel load assessments. The gains in accuracy and spatial-temporal resolution are generally positive, but the marginal improvements are often questioned from an operational perspective. The review also discusses the challenges and future directions in LiDAR data fusion. Key challenges include the high costs and computational efforts associated with processing multiple datasets, the variability in data quality and resolution, and the need for specialized expertise. The authors emphasize the importance of establishing common data formats and metadata standards to facilitate interoperability and collaboration among researchers. The article concludes by suggesting priorities for advancing LiDAR data fusion, including increasing research in the southern hemisphere, balancing the costs and benefits of data fusion, and promoting open data initiatives to enhance real-time applications and disaster response capabilities. Overall, the review provides a positive outlook for the future of LiDAR data fusion in forest observations, while also highlighting the need to address practical challenges and operational considerations.