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Article

Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning

1
CAVElab-Computational & Applied Vegetation Ecology, Department of Environment, Ghent University, 9000 Gent, Belgium
2
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
3
Environmental Research Institute of the Supervising Scientist, Darwin, NT 0801, Australia
4
CSIRO Land and Water, PMB 44, Winnellie, NT 0822, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Luke Wallace
Remote Sens. 2021, 13(3), 476; https://doi.org/10.3390/rs13030476
Received: 4 December 2020 / Revised: 5 January 2021 / Accepted: 25 January 2021 / Published: 29 January 2021
(This article belongs to the Special Issue Drones for Ecology and Conservation)
Termite mounds are found over vast areas in northern Australia, delivering essential ecosystem services, such as enhancing nutrient cycling and promoting biodiversity. Currently, the detection of termite mounds over large areas requires airborne laser scanning (ALS) or high-resolution satellite data, which lack precise information on termite mound shape and size. For detailed structural measurements, we generally rely on time-consuming field assessments that can only cover a limited area. In this study, we explore if unmanned aerial vehicle (UAV)-based observations can serve as a precise and scalable tool for termite mound detection and morphological characterisation. We collected a unique data set of terrestrial laser scanning (TLS) and UAV laser scanning (UAV-LS) point clouds of a woodland savanna site in Litchfield National Park (Australia). We developed an algorithm that uses several empirical parameters for the semi-automated detection of termite mounds from UAV-LS and used the TLS data set (1 ha) for benchmarking. We detected 81% and 72% of the termite mounds in the high resolution (1800 points m2) and low resolution (680 points m2) UAV-LS data, respectively, resulting in an average detection of eight mounds per hectare. Additionally, we successfully extracted information about mound height and volume from the UAV-LS data. The high resolution data set resulted in more accurate estimates; however, there is a trade-off between area and detectability when choosing the required resolution for termite mound detection Our results indicate that UAV-LS data can be rapidly acquired and used to monitor and map termite mounds over relatively large areas with higher spatial detail compared to airborne and spaceborne remote sensing. View Full-Text
Keywords: termite mounds; LiDAR; UAV; UAV-LS; remote sensing termite mounds; LiDAR; UAV; UAV-LS; remote sensing
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MDPI and ACS Style

D’hont, B.; Calders, K.; Bartholomeus, H.; Whiteside, T.; Bartolo, R.; Levick, S.; Krishna Moorthy, S.M.; Terryn, L.; Verbeeck, H. Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning. Remote Sens. 2021, 13, 476. https://doi.org/10.3390/rs13030476

AMA Style

D’hont B, Calders K, Bartholomeus H, Whiteside T, Bartolo R, Levick S, Krishna Moorthy SM, Terryn L, Verbeeck H. Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning. Remote Sensing. 2021; 13(3):476. https://doi.org/10.3390/rs13030476

Chicago/Turabian Style

D’hont, Barbara, Kim Calders, Harm Bartholomeus, Tim Whiteside, Renee Bartolo, Shaun Levick, Sruthi M. Krishna Moorthy, Louise Terryn, and Hans Verbeeck. 2021. "Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning" Remote Sensing 13, no. 3: 476. https://doi.org/10.3390/rs13030476

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