Extracting and Visualizing Structural Features in Environmental Point Cloud LiDaR Data Sets

Keller, Patric; Kreylos, Oliver; Vanco, Marek; Hering-Bertram, Martin; Cowgill, Eric; Kellogg, Louise H.; Hamann, Bernd; Hagen, Hans

doi:10.1007/978-3-642-15014-2_15

Cited by 10 publications

(2 citation statements)

References 22 publications

(24 reference statements)

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“…For example, in the case of UAV, some developments can be expected with infra-red (Lelong et al, 2008;Lobo, 2009), real-time generation of 3-D maps systems (Stefanik et al, 2011) and species scale classification (Dunford et al, 2009;Laliberte and Rango, 2011;Fernandes et al, 2013). Regarding LiDAR data, landscape features could be identified, extracted and mapped more satisfactorily from LiDAR 3D point clouds than from the 2D height and intensity images; however, 3D objects extraction based on object-oriented approaches is still at an experimental stage, while early studies are currently focusing on the segmentation of voxel (3D pixel) for the extraction of trees (Reitberger et al, 2009) or buildings (Keller et al, 2011). Moreover, most studies focus on LiDAR data analysis for topography, vegetation or built-up purposes, exclusively using the cloud points classified as "ground" and "above-ground" as well as intensity images.…”

Section: Discussionmentioning

confidence: 99%

Monitoring restored riparian vegetation: how can recent developments in remote sensing sciences help?

Dufour

Bernez

Betbeder

et al. 2013

Knowl. Managt. Aquatic Ecosyst.

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Key-words:remote sensing, riparian vegetation, UAV, LiDAR, radar Riparian vegetation restoration projects require appropriate tools to monitor actions efficiency. On a large scale remote sensing approaches can provide continuous and detailed data to describe riparian vegetation. In this paper, we illustrated recent developments and perspectives for riparian vegetation monitoring purposes through three examples of image sources: Light Detection And Ranging (LiDAR), radar and Unmanned Aerial Vehicule (UAV) images. We notably focused on the potential of such images to provide 3D information for narrow strips of riparian vegetation with high temporal resolution to allow fine monitoring following restoration program. LiDAR data allows canopy structure identification with a high accuracy level and automatic classifications for heterogeneous riparian corridors. Radar images allow a good identification of riparian vegetation but also of the structure and phenology of vegetation through time with an analysis of the Shannon entropy of the signal. The UAV system used here is a very flexible approach that can easily provide RGB mosaic but also a local digital surface model with very high spatial resolution. Lastly, we discuss the advantages and limitations of each approach from an applied perspective, in terms of flexibility, resolution and technicality. RÉSUMÉSuivre la restauration de la végétation riveraine : comment les développements récents de la télédétection peuvent-ils aider ? Mots-clés : télédétection, végétation riveraine, UAV, LiDAR, radarLe suivi des projets de restauration de la ripisylve nécessite des outils spécifiques. Dans cet article, nous illustrons et discutons comment les développements ré-cents dans le domaine de la télédétection permettent une description détaillée, continue et à large échelle des ripisylves restaurées à partir de trois exemples d'images : laser (LiDAR), radar et drone. Nous analysons notamment la capacité et le potentiel de ces images à fournir une information volumétrique de ripisylves étroites avec une forte résolution temporelle afin de permettre un suivi fin des actions de restauration. Les données LiDAR permettent une description de la structure de la canopée avec une très bonne précision ainsi qu'une classification automatique des ripisylves hétérogènes. Les images radar permettent une bonne identification non seulement de la végétation riveraine mais aussi de sa structure et de sa phénologie par analyse de l'entropie du signal. La technologie drone dé-ployée ici est très flexible et facile à mettre en oeuvre ; elle donne accès à des mosaïques de photographie à très haute résolution spatiale et à faible résolution

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Section: Discussionmentioning

confidence: 99%

Monitoring restored riparian vegetation: how can recent developments in remote sensing sciences help?

Dufour

Bernez

Betbeder

et al. 2013

Knowl. Managt. Aquatic Ecosyst.

View full text Add to dashboard Cite

show abstract

“…Support for this feature is integrated into the preprocessing stage: for each point, we compute its normal direction to be the same as the one of the best-fi t plane to the neighboring points within a userdefi ned radius. By enabling such interactive analysis of LiDAR data, LiDAR Viewer contrasts with approaches emphasizing automated algorithmic feature extraction (e.g., Filin, 2004;Keller et al, 2011aKeller et al, , 2011b.…”

Section: Softwarementioning

confidence: 99%