Analysis of UAV lidar information loss and its influence on the estimation accuracy of structural and functional traits in a meadow steppe

Zhao, Xiaoxia; Su, Yanjun; Hu, Tianyu; Cao, Mengmeng; Liu, Xiaoqiang; Yang, Qiuli; Guan, H. R.; Liu, Lingli

doi:10.1016/j.ecolind.2021.108515

Cited by 27 publications

(32 citation statements)

References 56 publications

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“…For the large size of sample plot, point cloud derived by unmanned aerial vehicle (UAV) or UAV LiDAR may perform better, because the problem of occlusion can be effectively avoided by acquiring the data from the top of the shrub canopy. Zhao et al (2022) found that the information of the crown tip of meadow derived by UAV‐LiDAR is easy to be lost, which has an impact on the height measurement. However, this problem has not been known in the shrub observation.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, SS mode is suitable for shrub observation with small size of sample plot (i.e., from 5 to 20 m), and the accuracy of quantity detection and attribute information extraction of this size can meet the requirements of shrub field investigation. For the large size of sample plot, point cloud derived by unmanned aerial vehicle (UAV) or UAV LiDAR may perform better, because the problem of occlusion can be effectively avoided by acquiring the data from the top of the shrub canopy Zhao et al (2022). found that the information of the crown tip of meadow derived by UAV-LiDAR is easy to be lost, which has an impact on the height measurement.…”

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confidence: 99%

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Quality assessment of shrub observation data based on TLS: A case of revegetated shrubland, Southern Qinghai‐Tibetan Plateau

Tian

Sun

et al. 2022

Land Degrad Dev

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Vegetation restoration is one of the effective measures to deal with land degradation, and shrubs, as the pioneer plants, can improve soil conditions and accelerate the recovery of desertification. In recent decades, single‐scan (SS) and multiple‐scan (MS) modes have been widely used for forest inventories by terrestrial laser scanner (TLS) but TLS‐based shrub observation, and data quality uncertainties are less studied, especially in eco‐fragile regions with high survey costs and harsh environmental conditions. In this study, two shrub sample plots with different topographic characteristics were selected as the study area in the southern Qinghai‐Tibetan Plateau. We compared the basic phenotypic parameters (BPPs) under SS and MS mode, including number (N), height (H) and crown width (CW), to evaluate the potential of SS mode in extracting vegetation attributes. Through the distribution of shrub information loss rate (L) with distance and shrub height, the limitations of SS mode in obtaining data were discussed. By combining the topographic features of scanning spots, topographic relief (T) and L, we provided reasonable opinions on TLS collection mode, setting of scanning spots, and other operational aspects. The results indicated that: (1) SS mode has good applicability in shrub observation under different terrain characteristics. (2) The height distribution of the shrub is the main factor affecting the quality of data collection, and the information loss bias caused by distance should be considered only in the shrub plots with uniform height distribution. (3) T has an impact on shrub growth but has no effect on the quality of data acquisition (no significant correlation between T and L). These findings will provide technical support for TLS data collection on ecological restoration of degraded grassland dunes.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Quality assessment of shrub observation data based on TLS: A case of revegetated shrubland, Southern Qinghai‐Tibetan Plateau

Tian

Sun

et al. 2022

Land Degrad Dev

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“…Several studies, which explored the potential of UAV LiDAR to estimate canopy height in grasslands, reported height underestimation ( Miura et al., 2019 ; Zhao et al., 2022 ). The underestimation would affect the estimation of grassland ecosystem functions related to canopy height and its heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…High-density canopy was considered to be a main cause in forests, which hamper the penetration of laser pulses to reach the ground ( Hu et al., 2020 ). Although grasslands may have a higher canopy density than forests, the gaps between grass individuals combined with high point cloud density allow UAV LiDAR to obtain reliable ground elevation information ( Getzin et al., 2021 ; Zhao et al., 2022 ). The height loss at canopy was proved to be the main cause which greatly reduced the accuracy of grassland height-related structural traits ( Miura et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

Zhao

Zheng

et al. 2023

Front. Plant Sci.

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Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25–65cm) and segmented (25–40cm, 40–50cm and 50–65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.

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“…In practice, the observed error magnitude and pattern is related to the target application as well. For example, errors have been assessed for forestry [45], meadow steppe [46], mountainous areas [47], flood plains [48], and different vegetation levels [49]. The focus of this work is on the vertical error on bulk measurements, such as piles or excavation.…”

Section: Introductionmentioning

confidence: 99%

An Error Prediction Model for Construction Bulk Measurements Using a Customized Low-Cost UAS-LIDAR System

Guan

Huang²,

Wang³

et al. 2022

Drones

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Small unmanned aerial systems (UAS) have been increasingly popular in surveying and mapping tasks. While photogrammetry has been the primary UAS sensing technology in other industries, construction activities can also benefit from accurate surveying measurements from airborne LIDAR. This paper discusses a custom-designed low-cost UAS-based LIDAR system that can effectively measure construction excavation and bulk piles. The system is designed with open interfaces that can be easily upgraded and expanded. An error model was developed to predict the horizontal and vertical errors of single point geo-registration for a generic UAS-LIDAR. This model was validated for the proposed UAS-LIDAR system using calibration targets and real-world measurements from different scenarios. The results indicated random errors from LIDAR at approximately 0.1 m and systematic errors at or below centimeter level. Additional pre-processing of the raw point cloud can further reduce the random errors in LIDAR measurements of bulk piles.

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Analysis of UAV lidar information loss and its influence on the estimation accuracy of structural and functional traits in a meadow steppe

Cited by 27 publications

References 56 publications

Quality assessment of shrub observation data based on TLS: A case of revegetated shrubland, Southern Qinghai‐Tibetan Plateau

Quality assessment of shrub observation data based on TLS: A case of revegetated shrubland, Southern Qinghai‐Tibetan Plateau

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

An Error Prediction Model for Construction Bulk Measurements Using a Customized Low-Cost UAS-LIDAR System

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