Abstract
Abstract. In forest ecology, a snag refers to a standing, partly or completely dead tree, often missing a top or most of the smaller branches. The accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, and forest management. Therefore, an understanding of its availability and spatial distribution is required. So far, LiDAR remote sensing has been successfully used to assess live trees and their biomass, but studies focusing on dead trees are rare. The paper develops a methodology for retrieving individual dead trees in a mixed mountain forest using features that are derived from small-footprint airborne full waveform LIDAR data. First, 3D coordinates of the laser beam reflections, the pulse intensity and width are extracted by waveform decomposition. Secondly, 3D single trees are detected by an integrated approach, which delineates both dominate tree crowns and understory small trees in the canopy height model (CHM) using the watershed algorithm followed by applying normalized cuts segmentation to merged watershed areas. Thus, single trees can be obtained as 3D point segments associated with waveform-specific features per point. Furthermore, the tree segments are delivered to feature definition process to derive geometric and 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 to discriminate dead trees from the living ones. The methodology is applied to datasets that have been captured with the Riegl LMSQ560 laser scanner at a point density of 25 points/m2 in the Bavarian Forest National Park, Germany, respectively under leaf-on and leaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification experiments lead in the best case to an overall accuracy of 73% in a leaf-on situation and 71% in a leaf-off situation, if we assess the classification results using 5-fold cross-validation method with the help of reference data acquired by the field surveying.
Highlights
Laser scanning or LiDAR has been widely used in mapping the Earth’s surface and especially in forest application
The objective of this paper is (i) to highlight a method that detects single trees with a novel 3D segmentation method in combination with the watershed algorithm, (ii) to introduce a new approach to detect dead trees that utilizes the geometric, reflectional and transmittance features which are derived at single tree level, (iii) to show how the detection and location of single dead and living trees across datasets of different foliage conditions are achieved by using a binary support vector machine (SVM) classifier
Under leaf-on condition the crown of deciduous trees exhibits a more abundant type, which leads to a stronger evidence for differing from coniferous dead trees making the distinction of tree types based on crown geometry and transmittance easier
Summary
Laser scanning or LiDAR has been widely used in mapping the Earth’s surface and especially in forest application. Techniques for tree extraction from LIDAR data have been investigated for mapping forests at both plot and tree levels to identify important structural parameters (Korpela et al, 2010; Yu et al, 2011). Recent advances in LIDAR technology have generated new full waveform scanners that provide a higher spatial point density and additional information about the reflectional characteristics and vertical structure of trees (Stilla et al, 2007; Reitberger et al, 2008; Yao et al, 2010). In combination with full waveform data Reitberger et al (2009) successfully demonstrated that the detection rate of single trees could be significantly improved in overall terms, especially in heterogeneous forest types where groups of trees grow closely to each other. The fusion of 3D techniques with full waveform data seems to push the single tree approach to a new level of accuracy. The analysis of the internal tree reflectional characteristics gains more insight into tree structure which are significant for instance for tree species classification
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