Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning

D'hont, Barbara; Calders, Kim; Bartholomeus, Harm; Whiteside, Timothy G.; Bartolo, Renée E.; Levick, Shaun R.; Moorthy, Sruthi M. Krishna; Terryn, Louise; Verbeeck, Hans

doi:10.3390/rs13030476

Cited by 13 publications

(9 citation statements)

References 38 publications

(48 reference statements)

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“…While previous studies have evaluated the quality and content of UAV‐lidar measurements in temperate and tropical forests (Brede et al, 2017; Bruggisser et al, 2019; Schneider et al, 2019; Silva et al, 2017; Terryn et al, 2022), few have evaluated these effects in savannas (but see Armston et al, 2013; D'hont et al, 2021; Sankey et al, 2018), where grasses and shrubs, rather than trees, dominate the vegetated landscape. Savannas are mosaics of grasslands and low‐density woodlands (Sankaran & Ratnam, 2013), often with large expanses of open space and free‐standing trees that provide low canopy cover.…”

Section: Introductionmentioning

confidence: 99%

“…Savannas are mosaics of grasslands and low‐density woodlands (Sankaran & Ratnam, 2013), often with large expanses of open space and free‐standing trees that provide low canopy cover. Ecological studies in savannas have greatly benefited from assessments of their unique vegetation structure using lidar (Asner et al, 2009; Davies & Asner, 2014; Leite et al, 2022), and highly detailed UAV‐lidar data have the potential to greatly expand on these insights, particularly those focused on fine‐scale, low‐lying features, such as termite mounds (D'hont et al, 2021) and grasses (da Costa et al, 2021; Wang et al, 2017). Given the small margin of error for measurements of these detailed ecological features, the impacts of UAV‐lidar parameters on vegetation metrics are especially relevant to study.…”

Section: Introductionmentioning

confidence: 99%

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Flying high: Sampling savanna vegetation with UAV‐lidar

et al. 2023

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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 and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX − 1LR over a 300 × 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimetres or less, which shows great promise for future comparison studies. We discuss how these results apply in practice, explaining the potential trade‐offs of flying at higher altitudes and with alternate patterns. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV‐lidar sampling designs in savannas.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flying high: Sampling savanna vegetation with UAV‐lidar

et al. 2023

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“…The bags were collected and analyzed in a lab. characterizing termite mounds using ground and sUAS-based laser scanning [65]. In a recent paper, ref.…”

Section: Introductionmentioning

confidence: 99%

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Hollenbeck

Zulevic

Chen

2021

Drones

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Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods.

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“…Here, we used drone-based Light Detection and Ranging (LiDAR) to survey termite mounds across ~1300 ha of experimental burn plots in Kruger National Park, South Africa. LiDAR is an active remote sensing technology that can penetrate vegetation to measure terrain features, including termite mounds, in high resolution (Davies et al, 2014(Davies et al, , 2020D'hont et al, 2021). Because drones fly closer to the ground surface than other remote sensing platforms, we were able to measure termite mounds at 100 times finer resolution than previous studies using aeroplane-based LiDAR (Davies et al, 2014(Davies et al, , 2020, enabling us to measure termite mounds of smaller size and across a wider variety of savanna landscapes.…”

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confidence: 99%

Resistance of termite mounds to variation in long‐term fire regimes across semi‐arid African savannas

Hockridge

Singh

Boucher

et al. 2022

Journal of Animal Ecology

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Fire regimes are expected to change with climate change, resulting in a crucial need to understand the specific ways in which variable fire regimes impact important contributors to ecosystem functioning, such as mound‐building termites. Termite mounds and fire are both important agents of savanna ecosystem heterogeneity and functioning, but there is little understanding of how they interact across savanna types. We used very high‐resolution LiDAR remote sensing to measure the size and distribution of termite mounds across approximately 1300 ha of experimental burn plots in four South African savanna landscapes representing a wide range of fire treatments differing in seasonality and frequency of burning. In nutrient‐poor granitic savannas, fire had no impact on termite mound size, densities and spatial distributions. In nutrient‐rich basaltic savannas with high mammalian herbivore abundance and intermediate rainfall, very frequent fires caused a decrease in termite mound size, whereas in arid nutrient‐rich basaltic savannas, fires that occurred at intermediate frequencies and in transitional seasons (i.e. late dry season and late wet season) decreased the degree of spatial overdispersal exhibited by mounds. Overall, our results suggest that termite mounds are resistant to variation in fire seasonality and frequency, likely indicating that ecosystem services provided by mound‐building termites will be unaffected by changing fire regimes. However, consideration of changes to termite mound size and distribution could be necessary for land managers in specific savanna types, such as nutrient‐rich soils with high mammalian herbivore abundance.

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Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning

Cited by 13 publications

References 38 publications

Flying high: Sampling savanna vegetation with UAV‐lidar

Flying high: Sampling savanna vegetation with UAV‐lidar

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Resistance of termite mounds to variation in long‐term fire regimes across semi‐arid African savannas

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