The Effectiveness of a UAV-Based LiDAR Survey to Develop Digital Terrain Models and Topographic Texture Analyses

Bartmiński, Piotr; Siłuch, Marcin; Kociuba, Waldemar

doi:10.3390/s23146415

Cited by 12 publications

(6 citation statements)

References 61 publications

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“…Ground points are then used for derivative products, e.g., DEM/DTM, DSM, and CHM. Inaccurate ground point classification highly likely contributes towards the under-/over-estimation of CHM heights when some of the crop points are classified as ground points or ground points are classified as crop points [25,47,49,93]. Furthermore, a wide variety of ground point classification algorithms have been developed; therefore, the choice and algorithm parameter optimization is a challenging task for ULS high-density point cloud classification [25].…”

Section: Discussionmentioning

confidence: 99%

“…Unlike forestry and coastal environments, ULS operational parameter optimization over crop environments has been lacking in past studies [23,45]. Fewer sensor-specific studies, e.g., DJI Zenmuse L1, dedicated in this regard use absolute and relative vertical accuracy assessments of acquired point clouds and their derived products, e.g., DEM, DSM, and CHM have been investigated in the past [32,[46][47][48]. Most of the scientific investigations depend on the sensor or UAS's default/recommended operational practices to acquire point clouds over the agricultural environment.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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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“…LiDAR-collected point cloud data has high precision and good anti-interference ability, is unaffected by changes in lighting conditions, and provides accurate spatial information. We selected the CHC Navigation AlphaAir 450 pocket LiDAR [27] based on its lightweight and highly integrated design concept. With a built-in camera, the entire payload weighs only 950 g, enabling high-precision, high-density, and efficient real-time data acquisition.…”

Section: Sensorsmentioning

confidence: 99%

Multi-Level Hazard Detection Using a UAV-Mounted Multi-Sensor for Levee Inspection

Su,

Yan,

Xie

et al. 2024

Drones

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This paper introduces a developed multi-sensor integrated system comprising a thermal infrared camera, an RGB camera, and a LiDAR sensor, mounted on a lightweight unmanned aerial vehicle (UAV). This system is applied to the inspection tasks of levee engineering, enabling the real-time, rapid, all-day, all-round, and non-contact acquisition of multi-source data for levee structures and their surrounding environments. Our aim is to address the inefficiencies, high costs, limited data diversity, and potential safety hazards associated with traditional methods, particularly concerning the structural safety of dam bodies. In the preprocessing stage of multi-source data, techniques such as thermal infrared data enhancement and multi-source data alignment are employed to enhance data quality and consistency. Subsequently, a multi-level approach to detecting and screening suspected risk areas is implemented, facilitating the rapid localization of potential hazard zones and assisting in assessing the urgency of addressing these concerns. The reliability of the developed multi-sensor equipment and the multi-level suspected hazard detection algorithm is validated through on-site levee engineering inspections conducted during flood disasters. The application reliably detects and locates suspected hazards, significantly reducing the time and resource costs associated with levee inspections. Moreover, it mitigates safety risks for personnel engaged in levee inspections. Therefore, this method provides reliable data support and technical services for levee inspection, hazard identification, flood control, and disaster reduction.

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“…Their findings revealed that terrain roughness significantly impacts the vertical accuracy of ALS DSMs. In another context, Bartmiński, Siłuch and Kociuba (2023) presented a comparison of data acquired from three LiDAR sensors from different manufacturers, namely, Yellow Scan Mapper (YSM), AlphaAir 450 Airborne LiDAR System CHC Navigation (CHC), and DJI Zenmuse L1 (L1) in the north-western part of the Lublin Province in eastern Poland. Their findings highlighted significant variations in terrain models calculated from point clouds, ranging from the CHC sensor with differences exceeding 2.5 m to the L1 sensor with RMSE at 0.31 m. This underscores the importance of accurate data acquisition and processing, especially when utilising UAVs for very high-resolution data in challenging environments.…”

Section: Introductionmentioning

confidence: 99%

The assessment of elevation data consistency. A case study using the ALS and georeference database in the City of Kraków

Piech,

Policht-Latawiec,

Lackóová

et al. 2024

Journal of Water and Land Development

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The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych -BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes.

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The Effectiveness of a UAV-Based LiDAR Survey to Develop Digital Terrain Models and Topographic Texture Analyses

Cited by 12 publications

References 61 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

Multi-Level Hazard Detection Using a UAV-Mounted Multi-Sensor for Levee Inspection

The assessment of elevation data consistency. A case study using the ALS and georeference database in the City of Kraków

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