The paper describes a methodology for tree species classification using features that are derived from small‐footprint full waveform Light Detection and Ranging (LIDAR) data. First, 3‐dimensional coordinates of the laser beam reflections, the intensity, and the pulse width are extracted by a waveform decomposition, which fits a series of Gaussian pulses to the waveform. Since multiple reflections are detected, and even overlapping pulse reflections are distinguished, a much higher point density is achieved compared to the conventional first/last‐pulse technique. Secondly, tree crowns are delineated from the canopy height model (CHM) using the watershed algorithm. The CHM posts are equally spaced and robustly interpolated from the highest reflections in the canopy. Thirdly, tree features computed from the 3‐dimensional coordinates of the reflections, the intensity and the pulse width are used to detect coniferous and deciduous trees by an unsupervised classification. The methodology is applied to datasets that have been captured with the TopEye MK II scanner and the Riegl LMS‐Q560 scanner in the Bavarian Forest National Park in leaf‐on and leaf‐off conditions for Norway spruces, European beeches and Sycamore maples. The classification, which groups the data into two clusters (coniferous, deciduous), leads in the best case to an overall accuracy of 85% in a leaf‐on situation and 96% in a leaf‐off situation.

Recent progress in remote sensing provides much-needed, large-scale spatio-temporal information on habitat structures important for biodiversity conservation. Here we examine the potential of a newly launched satellite-borne radar system (Sentinel-1) to map the biodiversity of twelve taxa across five temperate forest regions in central Europe. We show that the sensitivity of radar to habitat structure is similar to that of airborne laser scanning (ALS), the current gold standard in the measurement of forest structure. Our models of different facets of biodiversity reveal that radar performs as well as ALS; medianR²over twelve taxa by ALS and radar are 0.51 and 0.57 respectively for the first non-metric multidimensional scaling axes representing assemblage composition. We further demonstrate the promising predictive ability of radar-derived data with external validation based on the species composition of birds and saproxylic beetles. Establishing new area-wide biodiversity monitoring by remote sensing will require the coupling of radar data to stratified and standardized collected local species data.