Prediction of L-band signal attenuation in forests using 3D vegetation structure from airborne LiDAR

Liu, Pang‐Wei; Lee, Heezin; Judge, Jasmeet; Wright, William; Slatton, K. Clint

doi:10.1016/j.isprsjprs.2011.04.005

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Lidar point cloud data provides a 3D representation of the vertical forest structure enabling identification of individual trees (Lee et al 2010;Jakubowski et al 2013;Strimbu and Strimbu, 2015;Khosravipour et al 2015) and extracting tree canopy parameters (Chen et al 2007;Palminteri et al 2012). Lidar has also been used to find voids inside forests to estimate microwave signal attenuation (Liu et al 2011;Wright et al 2008), intercepted photosynthetically active radiation (Lee et al 2009), and within-canopy line-of-sight visibilities (Lee et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Estimation of 2-D Clutter Maps in Complex Under-Canopy Environments From Airborne Discrete-Return Lidar

Lee

Starek

Blundell

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Detection of near-ground objects occluded by above-ground vegetation from airborne lidar measurements remains challenging. Our hypothesis is that the probability of obstruction due to objects above ground at any location in the forest environment can be reasonably characterized solely from airborne lidar data. The essence of our approach is to develop a datadriven learning scheme that creates high-resolution 2D probability maps for obstruction in the under-canopy environment. These maps contain information about the probabilities of obstruction (clutter map) and lidar undersampling (uncertainty map) in the near-ground space. Airborne and terrestrial lidar data and field survey data collected within the forested, mountainous environment of Shenandoah National Park, Virginia USA are utilized to test and evaluate the proposed approach in this work. A newly developed individual tree detection algorithm is implemented to estimate undersampled stem contributions to the probability of obstruction. Results show the effectiveness of the tree detection algorithm with an accuracy index of between 61.5% and 80.7% (tested using field surveys). The estimated clutter maps are compared to the maps created from terrestrial scans (i.e., ground truth) and the results show the root-meansquare error of 0.28, 0.32, and 0.34 at three study sites. The overall framework in deriving near-ground clutter and uncertainty maps from airborne lidar data would be useful information for prediction of line-ofsight visibility, mobility and above-ground forest biomass. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents.

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Section: Introductionmentioning

confidence: 99%

Estimation of 2-D Clutter Maps in Complex Under-Canopy Environments From Airborne Discrete-Return Lidar

Lee

Starek

Blundell

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

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“…The resultant point cloud data provides a 3D representation of the vertical forest structure enabling segmentation of individual trees [1][2][3][4] and extracting tree canopy metrics [5,6]. Lidar has also been used to find voids inside forests to estimate microwave signal attenuation [7] and within canopy line-of-sight visibilities [8].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic clutter maps of forested terrain from airborne LiDAR point clouds

Lee¹,

Starek

Blundell

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Detection from airborne sensors of near-ground objects occluded by above-ground vegetation is not usually straightforward. Our hypothesis is that the probability of obstruction due to objects above ground at any location in the forest environment can be estimated with measurable uncertainty from airborne lidar data. The essence of our approach is to develop a data-driven learning scheme that creates 2D probability maps for obstructions at the study site. The result shows the effectiveness of the newly developed individual tree detection algorithm (with the accuracy index of 77.1%, tested using ground surveys) and also the usefulness of the clutter and uncertainty maps in the prediction of line-of-sight visibility, mobility and aboveground forest biomass.

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Satellite visibility analysis considering signal attenuation by trees using airborne laser scanning point cloud

Kou

Tan²,

Wang³

et al. 2023

GPS Solut

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Prediction of L-band signal attenuation in forests using 3D vegetation structure from airborne LiDAR

Cited by 6 publications

References 32 publications

Estimation of 2-D Clutter Maps in Complex Under-Canopy Environments From Airborne Discrete-Return Lidar

Estimation of 2-D Clutter Maps in Complex Under-Canopy Environments From Airborne Discrete-Return Lidar

Probabilistic clutter maps of forested terrain from airborne LiDAR point clouds

Satellite visibility analysis considering signal attenuation by trees using airborne laser scanning point cloud

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