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

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...

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Main Authors: Flavio Furukawa, Lauretta Andrew Laneng, Hiroaki Ando, Nobuhiko Yoshimura, Masami Kaneko, Junko Morimoto
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Drones
Subjects:
Online Access:https://www.mdpi.com/2504-446X/5/3/97
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author Flavio Furukawa
Lauretta Andrew Laneng
Hiroaki Ando
Nobuhiko Yoshimura
Masami Kaneko
Junko Morimoto
author_facet Flavio Furukawa
Lauretta Andrew Laneng
Hiroaki Ando
Nobuhiko Yoshimura
Masami Kaneko
Junko Morimoto
author_sort Flavio Furukawa
collection DOAJ
description 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.
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spelling doaj.art-356c22df974f4ffdbe8f4a1f0e800e9f2023-11-22T12:43:21ZengMDPI AGDrones2504-446X2021-09-01539710.3390/drones5030097Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide AreaFlavio Furukawa0Lauretta Andrew Laneng1Hiroaki Ando2Nobuhiko Yoshimura3Masami Kaneko4Junko Morimoto5Laboratory of Ecosystem Management, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8587, JapanLaboratory of Ecosystem Management, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8587, JapanLaboratory of Ecosystem Management, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8587, JapanDepartment of Environmental and Symbiotic Science, Rakuno Gakuen University, Ebetsu 069-8501, JapanDepartment of Environmental and Symbiotic Science, Rakuno Gakuen University, Ebetsu 069-8501, JapanLaboratory of Ecosystem Management, Graduate School of Agriculture, Hokkaido University, Sapporo 060-8587, JapanThe 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.https://www.mdpi.com/2504-446X/5/3/97landslidesunmanned aerial vehicle (UAV)multispectralRGBvegetation monitoring
spellingShingle Flavio Furukawa
Lauretta Andrew Laneng
Hiroaki Ando
Nobuhiko Yoshimura
Masami Kaneko
Junko Morimoto
Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide Area
Drones
landslides
unmanned aerial vehicle (UAV)
multispectral
RGB
vegetation monitoring
title Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide Area
title_full Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide Area
title_fullStr Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide Area
title_full_unstemmed Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide Area
title_short Comparison of RGB and Multispectral Unmanned Aerial Vehicle for Monitoring Vegetation Coverage Changes on a Landslide Area
title_sort comparison of rgb and multispectral unmanned aerial vehicle for monitoring vegetation coverage changes on a landslide area
topic landslides
unmanned aerial vehicle (UAV)
multispectral
RGB
vegetation monitoring
url https://www.mdpi.com/2504-446X/5/3/97
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