Application of UAS laser scanning for precision crop monitoring in Hungary

Airborne Laser Scanning (ALS) is a widely used method in Earth science, Agriculture or Forestry. This method could provide high resolution and accurate spatial data for the better understanding of surface structures, moreover, based on the laser pulses, it can even show important features of the ground below dense vegetation. However, these ALS surveys requires specially designed aircrafts, pilots and operators, detailed flight planning, which leads to an expensive way of data analysis. The application of laser scanners for Unmanned Aerial Systems (UAS) has started in the last few years. These sensor payloads provide less weight and size and decreased accuracy compared to the traditional ALS surveys but still serve as more reliable mapping technology contrary to the photogrammetric methods in many cases. However, several new UAS laser scanners are being developed but their accuracy conditions and applicability for agricultural monitoring must be studied in many ways.

In our study we applied the novel Zenmuse L1 LiDAR sensor mounted on a DJI Matrice M300 RTK UAS. We surveyed a ~50 ha area of corn field near Berettyóújfalu, Hungary in the summer of 2021. Our aim was to reveal the applicability of UAS laser scanning for the precise ground surface reconstruction. In this period, the corn was under irrigated condition, therefore, extensive weed patches were observed between the paths. The laser scanner ground filtering data was compared to a photogrammetry-based aerial survey that we have carried out at the beginning of the vegetation cycle at the same parcel. Our results showed both the potentials and limitations of this sensor for precision agriculture. The laser beams produced significant amount of noise between the paths that had to be cleaned to extract the ground surface below the corn canopy. Based on our data processing methods we were able to delineate similar drainage networks within the parcel that was also processed from the initial aerial survey. However, the UAS LiDAR gained the most accurate surface reconstruction at the more clear grassland patches around the parcel. 

L. Bertalan was supported by the INKP2022-13 grant of the Hungarian Academy of Sciences. This research was funded by the Thematic Excellence Programme (TKP2020-NKA-04) of the Ministry for Innovation and Technology in Hungary. This research was also influenced by the COST Action CA16219 “HARMONIOUS - Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”.