# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
###### Abstract
With technological advancements in Unmanned Aerial Vehicles (UAV) and Software Defined Radar (SDR), a new field of research has emerged with several authors developing drone radar and SAR systems [11; 12; 13; 14; 15; 16; 17; 18], and one group of authors developing a drone InSAR system [19; 20; 21; 22]; where drones are classified as small and lightweight UAVs. Drone radar deployment enables a lower observation altitude compared to satellites for an improved point density and spatial resolution; this is particularly useful for small or complex sites where multiple radar scatterers are located in close proximity. The drone may be deployed on a daily or sub-daily basis through rapid and customisable deployment for an improved temporal resolution and operational flexibility; this enables the creation of a detailed time-series to understand the impact of specific engineering or weather events on deformation in the InSAR data. Drone radar systems offer the potential to utilise higher frequency radar bands, such as within the K-band, since the atmospheric range and thus attenuation effect are both reduced [23]; these shorter wavelength signals are more sensitive to deformation for an improved detection capability. The use of SDR in drone radar allows the operator to change parameters such as the centre frequency, bandwidth and sampling rate on-the-fly, to optimise the system for penetration into different target materials. Lastly, drone radar may obtain a line-of-sight (LOS) vector from multiple directions, thus overcoming layover and shadowing effects found in satellite radar data for complex locations [13; 14]. Drone InSAR should therefore provide a complimentary data source to satellite and ground-based methods, whilst providing improved spatial-temporal resolutions. [PERSON] and [PERSON] [11; 12] developed an inexpensive pulsed SAR system for the DJI Phantom 2 drone. The total payload, which includes an ultra-wideband (UWB) P410 radar, Raspberry Pi, two 5-turn helix antennas and ancillaries weighs less than 0.3 kg, making it suitable for deployment on almost any size of drone. The range profiles of generated SAR images show good validation results against trihedral corner reflectors and other targets, despite the authors highlighting potential issues of turbulence sensitivity, drone flight instability, and nearfield antenna effects. [PERSON] et al. [13; 14; 15] developed a Ku-band Frequency Modulated Continuous Wave (FMCW) SAR system for the DJI Matrice 600 Pro drone for slope stability and aging infrastructure management. Their low-weight and small form factor payload design includes two 48 \(\times\) 68 mm aperture horn antennas with a 15 dBi gain. Verification of the SAR processing using corner reflectors shows good azimuth compression, with ongoing work to further improve this and develop drone differential InSAR (DInSAR). [PERSON] et al. developed a multi-band (P-, L- and C- bands) [19] and a P-band [20] single-pass drone InSAR system. Whilst multi-pass InSAR provides surface deformation data over time, single-pass InSAR utilises the physical separation of two antennas to compare two or more echoes of the target displaced in the along-track direction; this is useful for determining any distortions due to topography, and thus inform the creation of Digital Elevation Models (DEM). Finally, [PERSON] et al. [21] and [PERSON] et al. [22] utilise the same hardware as [PERSON] et al. [19; 20] to demonstrate short-term, multi-pass drone InSAR; the former for crop growth monitoring and the latter for ground surface deformation monitoring. These investigations, however, have a limited temporal range and separation of input data, which reduces potential spatial-temporal decorrelation effects, such as the atmospheric contribution, that are commonly associated with long-term InSAR processing. Long-term, multi-temporal, multi-pass drone InSAR is still yet to be demonstrated in the literature and is the motivation for ongoing research at Imperial College London.