Résumé
Effective strategies for forest characterization and monitoring, are important to support sustainable management.Recent advances in remote sensing, like LiDAR and hyperspectral sensors, provide valuable information to char-acterize forests at stand, plot and tree level. LiDAR data offers potentialities for analyzing structural properties ofcanopy, while hyperspectral imaging contains meaningful reflectance attributes of plants or spectral traits. The fu-sion of these two modalities provide better and more robust estimation of the forestry variables. This work presentsa review of methods for the integration of LiDAR data and hyperspectral imagery by taking into account appli-cations related to forestry. Although different authors propose different approaches of data fusion, our review isdivided according to three levels of fusion based on data processing: observation level, feature level, and decisionlevel. Fusion at observation level preserves most of the original information from both modalities by integrating3D point cloud with the hyperspectral information. One alternative for this task is the generation of the CanopyHeight Model (CHM), which is the most used two-dimensional representation of LiDAR data. Fusion at featurelevel seeks to complement information by exploiting the original data. The most relevant features extracted fromLiDAR or hyperspectral data are statistical, structural, topographic, vegetation indices, textural and dimension re-duction. Some of these feature descriptors are stacked to be fused at higher level, or these are normalized to beintegrated through methods of dimension reduction or feature selection. Fusion at decision level is directly associ-ated to the forestry application and implies tasks of segmentation, classification, data association and prediction -estimation. This review describes relationship between the three levels of fusion and the methods used in each con-sidered approach. The most important applications are oriented to species mapping, functional and physiologicalattributes, structural attributes, above ground biomass and carbon density and landcover maps.