Detection of single tree stems in forested areas from high density ALS point clouds using 3D shape descriptors

N. Amiri, P. Polewski, W. Yao, P. Krzystek, Andrew Skidmore

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

12 Citations (Scopus)
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Abstract

Airborne Laser Scanning (ALS) is a widespread method for forest mapping and management purposes. While common ALS techniques provide valuable information about the forest canopy and intermediate layers, the point density near the ground may be poor due to dense overstory conditions. The current study highlights a new method for detecting stems of single trees in 3D point clouds obtained from high density ALS with a density of 300 points/m2. Compared to standard ALS data, due to lower flight height (150-200 m) this elevated point density leads to more laser reflections from tree stems. In this work, we propose a three-tiered method which works on the point, segment and object levels. First, for each point we calculate the likelihood that it belongs to a tree stem, derived from the radiometric and geometric features of its neighboring points. In the next step, we construct short stem segments based on high-probability stem points, and classify the segments by considering the distribution of points around them as well as their spatial orientation, which encodes the prior knowledge that trees are mainly vertically aligned due to gravity. Finally, we apply hierarchical clustering on the positively classified segments to obtain point sets corresponding to single stems, and perform `1-based orthogonal distance regression to robustly fit lines through each stem point set. The `1-based method is less sensitive to outliers compared to the least square approaches. From the fitted lines, the planimetric tree positions can then be derived. Experiments were performed on two plots from the Hochficht forest in Ober¨osterreich region located in Austria.We marked a total of 196 reference stems in the point clouds of both plots by visual interpretation. The evaluation of the automatically detected stems showed a classification precision of 0.86 and 0.85, respectively for Plot 1 and 2, with recall values of 0.7 and 0.67.
Original languageEnglish
Title of host publicationProceedings ISPRS Geospatial week 2017, 18-22 September 2017, Wuhan, China. Peer reviewed Annals, Volume IV-2/W4, 2017
EditorsD. Li
PublisherInternational Society for Photogrammetry and Remote Sensing (ISPRS)
Pages35-42
DOIs
Publication statusPublished - 2017
EventISPRS Geospatial Week 2017 - Wuhan, China
Duration: 18 Sept 201722 Sept 2017
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/ (Full text Open Access proceedings)

Publication series

NamePeer reviewed Annals
PublisherISPRS
VolumeIV-2/W4
ISSN (Print)2194-9050

Conference

ConferenceISPRS Geospatial Week 2017
Country/TerritoryChina
CityWuhan
Period18/09/1722/09/17
Internet address

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