Abstract
Effective strategies for forest characterization and monitoring are important to support sustainable management. Recent advances in remote sensing, like hyperspectral and LiDAR sensors, provide valuable information to describe forests at stand, plot, and tree level. Hyperspectral imaging contains meaningful reflectance attributes of plants or spectral traits, while LiDAR data offer alternatives for analyzing structural properties of canopy. The fusion of these two data sources can improve forest characterization. The method to use for the data fusion should be chosen according to the variables to predict. This work presents a literature review on the integration of hyperspectral imaging and LiDAR data by considering applications related to forest monitoring. Although different authors propose a variety of taxonomies for data fusion, we classified our reviewed methods according to three levels of fusion: low level or observation level, medium level or feature level, and high level or decision level. This review examines the relationship between the three levels of fusion and the methods used in each considered approach.