Characterizing old-growth forests from multisource remote sensing

Old-growth forest is the ultimate forest stand development stage with the most complex structure. The complexity allows different ecological niches to be assembled, which benefits biodiversity. Some attributes are well known for defining old-growth forests' complexity, such as multi-age story, tree size (height and DBH), basal area, stand density, and deadwood presence. Their appearance and diversity characterize the degree of old-growth forests' structural complexity. However, only a few studies incorporated remote sensing data to identify those attributes for measuring the structural complexity of the old-growth forest stage. Through this study, we aimed to use the integration of multisource remote sensing data -mainly LiDAR and hyperspectral-, and ancillary GIS data to derive information the complexity of old-growth forests as well as to incorporate ground measured structural attributes as a proxy for enhancing the characterization. LiDAR has been proven to derive 3D forest structural information, which is suitable for delivering the complexity information of structural attributes both in horizontal and vertical dimensions. The hyperspectral imagery is useful for further old-growth forests complexity characterization, especially to define how the complex ecosystem structure will affect the dynamics of the ecosystem function such as productivity and disturbances in old-growth forest ecosystems. The expected results are a correlation model of the impact of the structural complexity on the ecosystem function of old-growth and earlier stages and a further explanation of the impact on the biodiversity in the old-growth forest ecosystem.