Using a wide range of sensors and platforms, remote sensing allows examining and gathering information about an object or a place from a distance. A key development in remote sensing has been the increased availability of data with very high-temporal, spatial and spectral resolution. In the last decades, several types of remote sensing data, including optical, radar, LiDAR from terrestrial, UAV, aerial and satellite platform, have been used to detect, classify, evaluate and measure the Earth surface, including different vegetation covers and forest structure. For the forest sector, such information allow the efficient monitoring of changes over time and space, in support of sustainable forest management, forest, and carbon inventory or for monitoring forest health and their disturbances. Remote Sensing data can provide both qualitatively and quantitatively information about forest ecosystems. In a qualitative analysis forest cover types and species composition can be classified, whereas the quantitative analysis can measure and estimate different forest structure parameters related to single trees (e.g., DBH, height, basal area, timber volume, etc.) and to the whole stand (e.g. number of trees per unit area, distribution, etc.). However, to meet the various information requirements, different data sources should be adopted according to the application, the level of detail required and the extension of the area under study. The integration of in-situ measurements with satellite/airborne/UAV imagery, Structure from Motion, LiDAR and geo-information systems offer new possibilities, especially for interpretation, mapping and measuring of forest parameters and will be a challenge for future research and application. This session explores the potentials and limitations of several types of remote sensing applications in forestry, with the focus on the identification and integration of different methodologies and techniques from different sensors and in-situ data for providing qualitative and quantities forest information.