Abstract
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.
Highlights
The evolution of remote sensing technology allows a feasible method for gathering detailed information for mapping land-cover changes [1], drought monitoring [2], and analyzing complex attributes [3,4] over space and time
15 though mosaics with more details compared to the Multispectral unmanned aerial vehicles (UAVs) ortho-mosaic, even ducer’s accuracy (PA), and user’s accuracy (UA)
The present study reveals that Multispectral UAVs are more applicable for characterizing vegetation, bare soil, and dead matter in areas affected by landslides, highlighting that cloudy weather and brownish soil are recommended to create a more reliable dataset
Summary
The evolution of remote sensing technology allows a feasible method for gathering detailed information for mapping land-cover changes [1], drought monitoring [2], and analyzing complex attributes [3,4] over space and time. This technology uses different types of sensor onboard satellites, airborne or unmanned aerial vehicles (UAVs), and provides different methods of vegetation classification at large and small scales. Playing an important role in forest disaster management, satellite-based remote sensing has some limitations in terms of spatial and temporal resolution of the data. A one-day temporal resolution satellite dataset is available [16], but cloud cover can still be a hindrance to acquiring the desired dataset
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