Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide Area

• Published in: Drones (Basel) Vol. 5; no. 3; p. 97
• Main Authors: Furukawa, Flavio, Laneng, Lauretta Andrew, Ando, Hiroaki, Yoshimura, Nobuhiko, Kaneko, Masami, Morimoto, Junko
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2021
• Subjects: Datasets; Disaster management; Image quality; Landslides; Landslides & mudslides; Management decisions; Monitoring; multispectral; Natural disasters; RGB; Sensors; Soils; unmanned aerial vehicle (UAV); Unmanned aerial vehicles; Vegetation; vegetation monitoring; Japan
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones5030097

The development of UAV technologies offers practical methods to create landcover maps for monitoring and management of areas affected by natural disasters such as landslides. The present study aims at comparing the capability of two different types of UAV to deliver precise information, in order to characterize vegetation at landslide areas over a period of months. For the comparison, an RGB UAV and a Multispectral UAV were used to identify three different classes: vegetation, bare soil, and dead matter, from April to July 2021. The results showed high overall accuracy values (>95%) for the Multispectral UAV, as compared to the RGB UAV, which had lower overall accuracies. Although having lower overall accuracies, the vegetation class of the RGB UAV presented high producer’s and user’s accuracy over time, comparable to the Multispectral UAV results. Image quality played an important role in this study, where higher accuracy values were found on cloudy days. Both RGB and Multispectral UAVs presented similar patterns of vegetation, bare soil, and dead matter classes, where the increase in vegetation class was consistent with the decrease in bare soil and dead matter class. The present study suggests that the Multispectral UAV is more suitable in characterizing vegetation, bare soil, and dead matter classes on landslide areas while the RGB UAV can deliver reliable information for vegetation monitoring.