Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests

Nina Amiri (Corresponding Author), Przemyslaw Polewski, Marco Heurich, Peter Krzystek, Andrew K. Skidmore

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)

Abstract

The development of new approaches to individual tree crown delineation for forest inventory and management is an important area of ongoing research. The increasing availability of high density ALS (Airborne Laser Scanning) point clouds offers the opportunity to segment the individual tree crowns and deduce their geometric properties with a high level of accuracy. Top-down segmentation methods such as normalized cut are established approaches for delineation of single trees in ALS point clouds. However, overlapping crowns and branches of nearby trees frequently cause over- and under-segmentation due to the difficulty of defining a single criterion for stopping the partitioning process. In this work, we investigate an adaptive stopping criterion based on the visual appearance of trees within the point clouds. We focus on coniferous trees due to their well-defined crown shapes in comparison to deciduous trees. This approach is based on modeling the coniferous tree crowns with elliptic paraboloids to infer whether a given 3D scene contains exactly one or more than one tree. For each processed scene, candidate tree peaks are generated from local maxima found within the point cloud. Next, paraboloids are fitted at the peaks using a random sample consensus procedure and classified based on their geometric properties. The decision to stop or continue partitioning is determined by finding a set of non-overlapping paraboloids. Experiments were performed on three plots from the Bavarian Forest National Park in Germany. Based on validation data from the field inventory, results show that our approach improves the segmentation quality by up to 10% across plots with different properties, such as average tree height and density. This indicates that the new adaptive stopping criterion for normalized cut segmentation is capable of delineating tree crowns more accurately than a static stopping criterion based on a constant Ncut threshold value.
Original languageEnglish
Pages (from-to)265-274
Number of pages10
JournalISPRS journal of photogrammetry and remote sensing
Volume141
DOIs
Publication statusPublished - 1 Jul 2018

Fingerprint

airborne lasers
coniferous forest
temperate forest
stopping
segmentation
laser
Scanning
scanning
Lasers
parabolic bodies
Availability
delineation
coniferous tree
Experiments
partitioning
deciduous trees
plots
national parks
deciduous tree
forest inventory

Keywords

  • ITC-ISI-JOURNAL-ARTICLE

Cite this

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title = "Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests",
abstract = "The development of new approaches to individual tree crown delineation for forest inventory and management is an important area of ongoing research. The increasing availability of high density ALS (Airborne Laser Scanning) point clouds offers the opportunity to segment the individual tree crowns and deduce their geometric properties with a high level of accuracy. Top-down segmentation methods such as normalized cut are established approaches for delineation of single trees in ALS point clouds. However, overlapping crowns and branches of nearby trees frequently cause over- and under-segmentation due to the difficulty of defining a single criterion for stopping the partitioning process. In this work, we investigate an adaptive stopping criterion based on the visual appearance of trees within the point clouds. We focus on coniferous trees due to their well-defined crown shapes in comparison to deciduous trees. This approach is based on modeling the coniferous tree crowns with elliptic paraboloids to infer whether a given 3D scene contains exactly one or more than one tree. For each processed scene, candidate tree peaks are generated from local maxima found within the point cloud. Next, paraboloids are fitted at the peaks using a random sample consensus procedure and classified based on their geometric properties. The decision to stop or continue partitioning is determined by finding a set of non-overlapping paraboloids. Experiments were performed on three plots from the Bavarian Forest National Park in Germany. Based on validation data from the field inventory, results show that our approach improves the segmentation quality by up to 10{\%} across plots with different properties, such as average tree height and density. This indicates that the new adaptive stopping criterion for normalized cut segmentation is capable of delineating tree crowns more accurately than a static stopping criterion based on a constant Ncut threshold value.",
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Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests. / Amiri, Nina (Corresponding Author); Polewski, Przemyslaw; Heurich, Marco; Krzystek, Peter; Skidmore, Andrew K.

In: ISPRS journal of photogrammetry and remote sensing, Vol. 141, 01.07.2018, p. 265-274.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Adaptive stopping criterion for top-down segmentation of ALS point clouds in temperate coniferous forests

AU - Amiri, Nina

AU - Polewski, Przemyslaw

AU - Heurich, Marco

AU - Krzystek, Peter

AU - Skidmore, Andrew K.

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Y1 - 2018/7/1

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