# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 6 Conclusions  
We have demonstrated the first SAR image from our drone radar system at Flint Hall Farm. The site is situated adjacent to the M25 and contains several landslide systems that pose a significant geohazard risk to this critical infrastructure. Conducting landslide assessments along transportation corridors is essential to the sustainability of critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000. InSAR has been widely used to identify potential landslides and monitor millimetric rates of surface deformation. Downscaling InSAR from satellites to drones will provide improved spatial-temporal data resolutions and operational flexibility. This transition is made possible by recent technological advancements in drones and SDR processing power.  
Long-term, multi-temporal and multi-pass drone InSAR is still yet to be demonstrated in the literature and is the motivation for the development of our drone radar system. The goal of this research is the downscaling of InSAR from satellites to drones, and the first step in achieving this is demonstrating drone SAR imaging. This case study demonstrates the functionality of the developed drone radar system in acquiring SAR imagery that will be used in downstream InSAR processing for deformation monitoring of the landslide at Flint Hall Farm and other geotechnical hazards and infrastructure. The drone SAR image from Flint Hall Farm is a novel achievement and provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope are visible as bright pixels (Figure 17). Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure. The main contributions of this work include:  
* The first SAR image from our drone radar system. This is a newly developing field of remote sensing research, with few authors demonstrating similar systems. The image validates our custom and novel hardware production of CCL radar horn antennas.
* A comparison of the drone SAR image and Sentinel-1 SAR shows that the former provides an improved centimetric spatial resolution compared to the meter-level resolution of the latter. This is useful for the identification and imaging of small and closely spaced targets, such as the corner reflectors and in situ instrumentation at Flint Hall Farm.
* A complete overview of the hardware and software development for the drone radar system; other demonstrations of drone radar systems in the literature do not provide such a comprehensive account.  
Conceptualization, A.C., J.A.L., P.J.M., R.G. and S.A.; methodology, A.C., J.A.L. and S.A.; software, A.C.; validation, A.C.; personal analysis, A.C.; investigation, A.C. and S.A.; resources, A.C.; data curation, A.C.; writing--original draft preparation, A.C.; writing--review and editing, A.C., J.A.L., P.J.M., R.G. and S.A.; visualization, A.C.; supervision, J.A.L., P.J.M. and R.G.; project administration, A.C. and J.A.L.; funding acquisition, A.C., J.A.L. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Nuclear Liabilities Fund (NLF) via EDF Energy.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors acknowledge the landowners at Flint Hall Farm for providing us with access to install the corner reflectors and conduct the drone flights.  
The authors declare no conflicts of interest.