Unveiling the Canopy: Insights, Definitions, and Outcomes from a LiDAR Data Fusion Review for Forest Observation

Many LiDAR remote sensing studies over the past decades declared data fusion as a potential avenue to increase accuracy, spatial and temporal resolution of the final datasets. LiDAR data fused with other datasets such as multispectral, hyperspectral and radar has proven beneficial for various applications, including the segmentation processes, the above ground biomass (AGB) assessments, the tree height estimation and the tree species identification. Despite progress in data fusion techniques and opportunities, the proliferation of scientific papers has given rise to questions within the scientific community. What is ‘data fusion’ and how should this term be used in our community? What opportunities does it provide and are these approaches as good as promised? What are the main challenges in LiDAR data fusion for forest observations? In this paper, we performed a structured literature review to analyse relevant studies on these topics published in the last decade (2014-2023). We used a specific query in the Web of Science database, selecting only papers published in English language with a publication status of “article” or “review article”. These limitations in the query resulted in 407 papers. The abstract were screened by two independent reviewers, following these criteria: (1) The paper must assess some aspect of trees/forests relevant to forestry applications, with the exclusion of those solely focusing on crops or human-made structures (such as infrastructure or buildings). (2) The fusion process must include LiDAR data. A significant portion of excluded papers did not actively engage in data fusion; instead, they merely discussed it as a potential solution to identified limitations in their analyses. Alternatively, these papers did not use data from a LiDAR sensor in their application. The screening process resulted in 153 papers. From our findings, there is a slight general upward publication trend over the last 10 years, with an increasing trend in the use of spaceborne LiDAR sensors. The predominant form of fusion observed in this study was airborne LiDAR with other airborne data types, accounting for 45.4% of the total papers. Following closely was the fusion of airborne LiDAR data and spaceborne devices, constituting 29.8%. Equally represented, each with 11.3%, were spaceborne LiDAR-data with other spaceborne sensors and airborne-terrestrial fusion. The least commonly encountered method was the fusion of terrestrial LiDAR with other data from terrestrial platforms, representing only 2.1%. 27.2% of the papers are dealing with an individual tree detection approach, 49.7% with an area-based approach and 17.2% with both. 6% are reviews with no defined study area/application. Our review indicated that, generally, all common applications are improved using data fusion. The benefits include improved accuracy, particularly noticeable in tree species composition classifications, and advancements in spatial or temporal resolution, especially for canopy height assessments. However, a critical consideration arises regarding whether the incremental improvements, at times marginal, justify the additional economical and computational investment.

This abstract is based upon work from COST Action 3DForEcoTech, CA20118, supported by COST (European Cooperation in Science and Technology).