Influence of phenology on waveform features in deciduous and coniferous trees in airborne LiDAR

Korpela, Ilkka; Polvivaara, Antti; Hovi, Aarne; Junttila, Samuli; Holopainen, Markus

doi:10.1016/j.rse.2023.113618

Cited by 7 publications

(9 citation statements)

References 32 publications

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“…Therefore, GNSS-based crop heights were measured in the field to assess the accuracy of multi-temporal CHM [36]. Multi-temporal CHM reflect spatio-temporal changes purely from a crop height (z) perspective; failure to address many external factors that influence the LiDAR backscatter, e.g., crop phenology, may result in false observations about canopy heights [54]. (c) To understand external factors influencing the LiDAR backscatter, crop characteristic ULS waveform (WF) analysis is performed using multi-temporal simulated WFs [16,55].…”

Section: Contributionsmentioning

confidence: 99%

“…To the best of our knowledge, assessing ULS operational parameters in the context of internal and external factors influencing LiDAR backscatter over soft targets, e.g., vegetation in agricultural settings using simulated WFs is lacking in past studies [27,54]. The existing methods for WF data calibration, data processing to plant properties retrieval have gradually been available yet are far from being mature [16].…”

Section: Contributionsmentioning

confidence: 99%

“…Red-green-blue (RGB) camera was operated simultaneously to collect VHR optical imagery aiding the ULS data analysis and visualization (Figure 1b,c) [8]. Moreover, the LiDAR sensor operated at a fixed nadir-looking angle for all ULS campaigns to avoid differences associated with scan zenith angle (SZA) distributions, and spherical losses [54]. Compared with ALS flying at much higher altitudes, e.g., 750 m AGL [54], ULSs flying at 50 m-60 m AGL, SZA, and spherical losses are trivial given that the LiDAR sensor is operated at a fixed nadir-looking configuration.…”

Section: Uls Surveymentioning

confidence: 99%

“…The survey was carried out at two designated altitudes, i.e., 50 m and 60 m above ground level (AGL) with a flight speed of 5 m/s and strip overlap of 50% following a similar approach [23,46]. Furthermore, a PRR of 160 kHz and 260 kHz along with one return echo (1n), two return All UAS flights were performed within a time window (12:00-14:00 UTC) under clear sky conditions with comparable air temperature, humidity, visibility, wind speed, and solar elevation to minimize the possible offset of environmental factors on the geometric and radiometric properties of multi-temporal ULS data [54]. Consequently, the differences in LiDAR backscattered for different dates can be associated with geometric-optical properties of changing crop phenology and ULS operational practices.…”

Section: Uls Surveymentioning

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%

Section: Contributionsmentioning

confidence: 99%

Section: Uls Surveymentioning

confidence: 99%

Section: Uls Surveymentioning

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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“…On the other hand, aerial surveying has gained popularity as a fast and safe solution within outdoor missions. In recent years, drone photogrammetry has become a common solution for surveying stockpiles in a variety of industries such as mining [4,5], quarrying [6,7], construction site monitoring [8,9], and agriculture and forestry [10,11]. In fact, large volumes of aerial 2D photos are typically processed using classical photogrammetry methods or structure from motion (SfM; a more modern approach that automates much of the processes involved in classical photogrammetry) [12,13] to create 3D topographical models and orthomosaic maps.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Stockpile Volume Estimation in Open and Confined Environments: A Review

Alsayed,

Nabawy

2023

Drones

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This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces.

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Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

Campos,

Nunes,

Shcherbacheva

et al. 2024

Agricultural and Forest Meteorology

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Influence of phenology on waveform features in deciduous and coniferous trees in airborne LiDAR

Cited by 7 publications

References 32 publications

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

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

Stockpile Volume Estimation in Open and Confined Environments: A Review

Factors and effects of inter-individual variability in silver birch phenology using dense LiDAR time-series

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