An instructional workflow for using terrestrial laser scanning (TLS) to quantify vegetation structure for wildlife studies

Penman, Sandra; Lentini, Pia; Law, Bradley; York, Alan

doi:10.1016/j.foreco.2023.121405

Cited by 2 publications

(3 citation statements)

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“…We aimed to consider all internal, e.g., flight altitude, PRR, scanning, and return echo modes, and external factors, e.g., crop type, structural complexity, and phonological stages. (d) Finally, regarding the vertical information loss for ULS WFs over agricultural environments, a comparative assessment is made with benchmark TLS WFs that provide concurrent detailed vegetation structural information [56][57][58].…”

Section: Contributionsmentioning

confidence: 99%

Section: Terrestial Laser Scannermentioning

confidence: 99%

“…Though geometric occlusions are more pronounced for optical sensors, a comparison with a standard benchmark, i.e., TLS, is necessary to quantify the information loss of ULSs [62]. TLSs have been witnessed as great tools to measure fine-scale vegetation attributes [56,63]. TLSs generally operate in PRS space (≈5-10 m), apart from terrestrial objects, and translates most structural details compared with sensors onboard UASs, thus have been used as a reference benchmark to assess ULS information loss [57,64,65].…”

Section: Terrestial Laser Scannermentioning

confidence: 99%

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UAS Quality Control and Crop Three-Dimensional Characterization Framework Using Multi-Temporal LiDAR Data

Fareed,

Das,

Flores

et al. 2024

Remote Sensing

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Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R2 (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R2 (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets.

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Section: Contributionsmentioning

confidence: 99%