Generation of a Large-Scale Surface Sediment Classification Map Using Unmanned Aerial Vehicle (UAV) Data: A Case Study at the Hwang-do Tidal Flat, Korea SCIE SCOPUS

Cited 10 time in WEB OF SCIENCE Cited 10 time in Scopus
Title
Generation of a Large-Scale Surface Sediment Classification Map Using Unmanned Aerial Vehicle (UAV) Data: A Case Study at the Hwang-do Tidal Flat, Korea
Author(s)
Kim, Kye-Lim; Kim, Bum-Jun; Lee, Yoon-Kyung; Ryu, Joo-Hyung
KIOST Author(s)
Kim, Kye Lim(김계림)Ryu, Joo Hyung(유주형)
Alternative Author(s)
김계림; 김범준; 유주형
Publication Year
2019-02-01
Abstract
Tidal flats are associated with complicated depositional and ecological environments, and have changed considerably as a result of the erosion and sedimentation caused by tidal energy; consequently, the surface sediment distribution in tidal flats must be constantly monitored and mapped. Although several studies have been conducted with the aim of classifying intertidal surface sediments using various remote sensing methods combined with field survey, most of these studies were unable to consider various sediment types, due to the low spatial resolution of remotely sensed data. Therefore, previous studies were unable to efficiently describe precise surface sediment distribution maps. In the present study, unmanned aerial vehicle (UAV) red, green, blue (RGB) orthoimagery was used in combination with a field survey (232 samples) to produce a large-scale classification map for surface sediment distribution, in accordance with sedimentology standards, using an object-based method. The object-based method is an effective technique that can classify surface sediment distribution by analyzing its correlations with spectral reflectance, grain size, and tidal channels. Therefore, we distinguished six sediment types based on their spectral reflectance and sediment properties, such as grain composition and statistical parameters. The accuracy assessment of the surface sediment classification based on these six types indicated an overall accuracy of 72.8%, with a kappa coefficient of 0.62 and 5-m error range related to the Global Positioning System (GPS) device. We found that 11 samples were misclassified due to the effects of sun glint and cloud caused by the UAV system and shellfish beds, while 14 misclassified samples were influenced by surface water related to the elevation, tidal channels, and sediment properties. These results indicate that large-scale classification of surface sediment with high accuracy is possible using UAV RGB orthoimagery.
ISSN
2072-4292
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/685
DOI
10.3390/rs11030229
Bibliographic Citation
REMOTE SENSING, v.11, no.3, 2019
Publisher
MDPI
Subject
INTERTIDAL SEDIMENTS; GRAIN-SIZE; WADDEN SEA; MOISTURE; FACIES
Keywords
tidal flat; surface sediments classification; UAVs; large-scale map
Type
Article
Language
English
Document Type
Article
Publisher
MDPI
Related Researcher
Research Interests

Coastal Remote Sensing,RS based Marine Surveillance System,GOCI Series Operation,연안 원격탐사,원격탐사기반 해양감시,천리안해양관측위성 운영

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