Unmanned Scanning

Barbara D'hont et al.: Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning 29.01.2021

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, the authors explore if unmanned aerial vehicle (UAV)-based observations can serve as a precise and scalable tool for termite mound detection and morphological characterisation. They 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). The UAV data sets were collected with a RIEGL RiCOPTER with VUX-SYS consisting of a RIEGL VUX-1UAV Laser Scanner combined with an Applanix APX-20 IMU.

They 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. The authors 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, they 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. The 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.

The full article was published in the Remote Sensing Journal (2021, 13, 476), publishing house: MDPI, and can be found here.