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Identifying standing dead trees in forest areas based on 3D single tree detection from full waveform LiDAR data

  • 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.

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Metadaten
Author:Wei Yao, Peter Krzystek, Marco Heurich
URN:urn:nbn:de:bvb:m347-opus-2315
DOI:https://doi.org/10.5194/isprsannals-I-7-359-2012
Parent Title (English):ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Document Type:Article
Language:English
Date of Publication (online):2022/01/05
Year of first Publication:2012
Publishing Institution:Hochschule für angewandte Wissenschaften München
Contributing Corporation:22. ISPRS Congress, 25. August bis 01. September 2012, Melbourne, Australien
Release Date:2022/01/05
GND Keyword:ForstwirtschaftGND; LidarGND
Volume:I-7, 2012
Page Number:7
First Page:359
Last Page:365
Note:
Sonderdruck aus: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012
Departments and Institutes:Fakultäten der Hochschule München / FK 08 Fakultät für Geoinformation
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 60 Technik
Illustrations:Illustration/en
Illustrations:grafische Darstellung/en
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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