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Characterizing termite mounds in a tropical savanna with UAV laser scanning

Published on
March 9, 2021

In 2018, a team of researchers from Ghent University, Wageningen University, the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and the Australian government collected UAS LiDAR data at Litchfield National Park using a RIEGL RiCOPTER, equipped with a RIEGL VUX-1UAV scanner. The RiCOPTER system is part of the Shared Facilities of Wageningen University and Research. Harm Bartholomeus (see person with head on photo) of the WUR was the pilot for the UAV system and coordinated the UAV activities in this challenging environment.

Main objective of the research campaign was the 3D mapping of the tropical forest of several locations and comparing different experimental set-ups. But the acquired LiDAR point cloud, the research team coordinated by Barbara D’hont of Ghent University uncovered hundreds of termite mounds spread out underneath the canopy. The termites that inhabit those mounds are true ecosystem engineers, for example, they increase water infiltration due to their extensive network and can create small fertility islands by concentrating nutrients close by the mound. In addition, mounds are hypothesized to increase resilience to climate change and are considered to play a meaningful role in savanna restoration.

They respectively detected 82% and 72% of the mounds (higher than 50 cm) in the high and low resolution point cloud. In addition, they were able to estimate mound height and volume. This new work opens the ability to monitor mound size in order to see whether the mound is continuously growing, or if it is abandoned and eroding. This is the first time termite mounds are detected over relatively large areas with this level of detail, using remote sensing techniques. Currently, large area data sets are based on sparse aerial laser scanning (ALS) data or high resolution satellite data. Both lack the detailed 3D information that UAS LiDAR can provide, which is needed to reliably detect termite mounds in savannas with a dense understory, especially if they are small. This methodology opens a range of possibilities for termite mound analysis and demonstrates the possibilities of UAV-LS in savanna woodland, in which not only mere detection of termite mounds is possible, but also 3D information is available.

The results of this study are published in the journal Remote Sensing in a paper entitled ‘Characterising termite mounds in a tropical savanna with UAV laser scanning’. The paper is open-source and can be accessed through the following link: https://doi.org/10.3390/rs13030476

The research was funded by the Belgian Federal Science Policy Office (3D-TERRAIN and 3D-FOREST).

Abstract
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 m−2) and low resolution (680 points m−2) 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.