2023
DOI: 10.1111/2041-210x.14081
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Flying high: Sampling savanna vegetation with UAV‐lidar

Abstract: The flexibility of UAV‐lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. To better understand the impacts that UAV flight patterns a… Show more

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Cited by 11 publications
(9 citation statements)
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“…First, we ignored interactions between pulse density degradation and other sources of uncertainty (e.g., an operator flying at higher altitudes can compensate lower point densities by increasing laser power) and focussed on sites with subtle changes in topography (except for Robson Creek; Fig S9). Furthermore, some sources of point cloud variation (rotational patterns of scanners, varying footprint sizes or waveform-to-discrete-point conversion) were beyond the scope of our analysis and would need ray tracing simulations or careful experiments with different instrumentations (Boucher et al, 2023; Brede et al, 2022; Næsset, 2009). Footprint size, for example, has been found to introduce moderate, but highly variable biases into height estimates (0.1-0.5 m), including both positive (Næsset, 2009), negative (Morsdorf et al, 2008; Roussel et al, 2017) and site-dependent (Goodwin et al, 2006) differences between larger and smaller footprints.…”
Section: Discussionmentioning
confidence: 99%
“…First, we ignored interactions between pulse density degradation and other sources of uncertainty (e.g., an operator flying at higher altitudes can compensate lower point densities by increasing laser power) and focussed on sites with subtle changes in topography (except for Robson Creek; Fig S9). Furthermore, some sources of point cloud variation (rotational patterns of scanners, varying footprint sizes or waveform-to-discrete-point conversion) were beyond the scope of our analysis and would need ray tracing simulations or careful experiments with different instrumentations (Boucher et al, 2023; Brede et al, 2022; Næsset, 2009). Footprint size, for example, has been found to introduce moderate, but highly variable biases into height estimates (0.1-0.5 m), including both positive (Næsset, 2009), negative (Morsdorf et al, 2008; Roussel et al, 2017) and site-dependent (Goodwin et al, 2006) differences between larger and smaller footprints.…”
Section: Discussionmentioning
confidence: 99%
“…Although many methods have been proposed to create 3D tree models, modelling realistic 3D forest scenes is still challenging due to the high complexity of tree architectures in reality (Hu et al., 2017). Recently, the development of LiDAR technology makes it a powerful tool in forest inventory, and researchers can use different LiDAR systems to collect information of 3D architectures of forests (Boucher et al., 2023; Liang et al., 2016; Zhao et al., 2011). Therefore, LiDAR is increasingly used to extract 3D structural attributes of forests, for example tree position, height, crown radius, diameter at breast height (DBH), leaf area index (LAI), leaf angle distribution and detailed architectures of individual trees, which can then be used to create 3D forest models (Allen et al., 2022; Burt et al., 2019; Hardenbol et al., 2022; Zhao et al., 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Ecologists, foresters and conservation practitioners need ‘biodiversity scanners’—timely, cost‐effective landscape observation technology to inventory biodiversity, audit conservation progress and track changes in ecosystem function (Bush et al., 2017; Jetz et al., 2019; Ji et al., 2022). One promising candidate is the use of light detection and ranging (LiDAR) scanners to provide high‐resolution scans of terrain and vegetation structure across vast, difficult‐to‐access landscapes (Boucher et al., 2023; Kellner et al., 2019). LiDAR scanners emit pulses of light into the survey environment and use temporal differences in received light reflection to reconstruct surrounding objects as a collection of point clouds in three‐dimensional space (Gatziolis & Andersen, 2008).…”
Section: Introductionmentioning
confidence: 99%