# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 5 Discussion  
Conducting landslide assessments along transportation corridors is essential for the sustainability of critical infrastructure. Remote sensing provides a more automated approach compared to traditional in situ monitoring and quantification methods, with improved spatial coverage and data accuracies. In particular, InSAR has been shown to have potential applications across the geophysical and civil engineering domains, including the identification and monitoring of landslide movements [3; 4; 5; 6; 7; 8; 9; 10]. The resultant landslide inventories may inform early-warning prediction models, road maintenance and hazard mitigation procedures. Figure 17: (**a**) SLC SAR image from Flint Hall Farm, with annotated flight path and circled targets; the latter includes the corner reflectors (red), and other fenced areas for in situ instrumentation (yellow, blue and pink); (**b**) Google Street View imagery of Flint Hall Farm, with annotated flight path, and corresponding circled targets, as indicated by the arrows connecting (**a,b**). Downscaling InSAR from satellite to drone platforms will provide improved spatial-temporal data resolutions and operational flexibility. Several authors have demonstrated drone radar systems with SAR imaging capabilities [11; 12; 13; 14; 15]; however, only one group of authors has demonstrated multi-pass drone InSAR [21; 22], and their investigations are short-term with a limited temporal range and separation of input data. Long-term, multi-temporal, multi-pass drone InSAR is yet to be demonstrated in the literature and is the motivation for the development of our drone radar system. Regarding the hardware development, like the other drone radar systems that utilise this model [15; 16; 17; 18; 19; 21; 22], the DJI Matrice 600 Pro demonstrates good flight durations and stability whilst carrying the 3.5 kg radar payload. The Raspberry Pi SBC provides a novel method for controlling the non-standard radar payload, with power supplied via the drone and a voltage converter. The SAR image is the first collected using the CCL antennas presented in [24], which are themselves a novel invention, thus validating this custom hardware production. The drone SAR image presented in Figure 17 is the output of the custom GRC FMCW flowgraph and SAR RMA. Corner reflectors were installed at Flint Hall Farm to provide strong and stable radar reflectors in this rural location and support the investigation of landslide deformation using satellite InSAR. Following the examples of other drone radar systems in the literature [14; 15; 22], the corner reflectors were subsequently utilised as targets for the drone SAR imaging. The fenced area for the corner reflectors is approximately 4 \(\times\) 4 m; the blue and pink targets in Figure 17 are both approximately 3 \(\times\) 3 m; and the yellow target is approximately 10 \(\times\) 3.5 m. The drone SAR image provides an improved centimetric spatial resolution compared to satellite SAR, which is useful for the identification of these features and micro-scale analysis. The size and positioning of these targets were used for a ground-truth comparison to assess the accuracy of the SAR data, with results as expected. Furthermore, drone flight instabilities may introduce phase errors which impact the SAR image accuracy. To account for this potential source of error, an accuracy assessment of the automated GPS flight path was conducted using the fixed GNSS data with millimetre accuracy. This yielded a maximum horizontal and vertical flight path deviation of 5 and 10 mm, respectively, which is deemed to have a negligible effect on the SAR image accuracy and quality beyond some minor distortions, which are somewhat inevitable. Figure 18 presents the average SAR amplitude for Flint Hall Farm from Sentinel-1, from the date of the corner reflector installation in September 2021 until September 2023. Strong and stable radar reflectors such as buildings and motorway overbridges appear as bright pixels.