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In forest ecology, a snag refers to a standing, partly or completely dead tree, often missing a top or most of the smaller branches. Theaccurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, andforest management. Therefore, an understanding of its availability and spatial distribution is required. So far, LiDAR remote sensinghas been successfully used to assess live trees and their biomass, but studies focusing on dead trees are rare. The paper develops amethodology for retrieving individual dead trees in a mixed mountain forest using features that are derived from small-footprintairborne full waveform LIDAR data. First, 3D coordinates of the laser beam reflections, the pulse intensity and width are extractedby waveform decomposition. Secondly, 3D single trees are detected by an integrated approach, which delineates both dominate treecrowns and understory small trees in the canopy height model (CHM) using the watershed algorithm followed by applyingnormalized cuts segmentation to merged watershed areas. Thus, single trees can be obtained as 3D point segments associated withwaveform-specific features per point. Furthermore, the tree segments are delivered to feature definition process to derive geometricand reflectional features at single tree level, e.g. volume and maximal diameter of crown, mean intensity, gap fraction, etc. Finally,the spanned feature space for the tree segments is forwarded to a binary classifier using support vector machine (SVM) in order todiscriminate dead trees from the living ones. The methodology is applied to datasets that have been captured with the Riegl LMSQ560laser scanner at a point density of 25 points/m<sup>2</sup> in the Bavarian Forest National Park, Germany, respectively under leaf-on andleaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification experiments lead in the bestcase to an overall accuracy of 73% in a leaf-on situation and 71% in a leaf-off situation, if we assess the classification results using5-fold cross-validation method with the help of reference data acquired by the field surveying

IDENTIFYING STANDING DEAD TREES IN FOREST AREAS BASED ON 3D SINGLE TREE DETECTION FROM FULL WAVEFORM LIDAR DATA