Multi-modal surveying refers to the integration of multiple data collected with different methods and technologies to acquire spatial and environmental information. Multi-modal data can enhances accuracy, coverage, and efficiency by leveraging different complementary sensors, platforms, and processing techniques. In this PhD project, the candidate will focus on the investigation of optimal methods for collecting, and processing and fusing spatial data in forest environments. The investigation will include, but not be limited to, photogrammetry, laser scanning (mobile/static, aerial, drone, terrestrial), optical multi/hyperspectral and radar remote sensing. Forest environments pose unique challenges due to their complex geometric structures and accessibility conditions, notwithstanding heterogeneous structures, dynamic objects and occlusions. The aim is to prove that processing and fusing solutions – including AI methods – based on multi-modal data improve current state of the art mapping methods for the identification of tree species and distribution as well as determination of forest structures and ecological variables in time and space domains. The ideal candidate should have an attitude towards problem-solving using programming environments, a strong background on spatial data management and analysis as well as natural, environmental and/or forestry sciences.