Mapping and quantifying seagrass distribution and coverage using multi-satellite images

Title
Mapping and quantifying seagrass distribution and coverage using multi-satellite images
Author(s)
김근용; 김범준; 최종국; 유주형
KIOST Author(s)
Kim, Keunyong(김근용)Choi, Jong Kuk(최종국)Ryu, Joo Hyung(유주형)
Publication Year
2018-09-13
Abstract
Seagrass beds plays important roles, not only providing food and habitat for various animals but also leading nutrients and carbon cycle in coastal ecosystem. Despite the importance of seagrass beds, studies of seagrass distribution in Korea, have been conducted by direct sampling method in limited depth and area. Recently, remote sensing is considered to be very effective tool to detecting wide-scales habitat. In this study, multi-satellite data were used for detecting seagrass beds in turbid waters and describing temporal changes over the past two decades. All seagrass spectra from different sensors (Landsat TM/ETM+, Aster, Spot-4, and Kompsat-2) have low reflectance in green and high in NIR band, whereas bare seawater showed opposite spectrum features. A training area in the green band and Mahalanobis Distance Classification (MDC) algorithm was adopted for classification and mapping of seagrass beds. Based on the underwater acoustic referencedata, overall accuracy of error matrix for Kompsat-2 image classification was 73% accurate. Despite the high turbidity seawater, this accuracy is considered relatively high precision. The average area of seagrass beds was 4.6 km2 from 1990to 2012, and it was consistently >4.0 km2. Seagrass beds are often inaccurately detected when using different spatial resolutions and turbid water, but the MDC method will be appropriate for turbid water of Korean coast.a, have been conducted by direct sampling method in limited depth and area. Recently, remote sensing is considered to be very effective tool to detecting wide-scales habitat. In this study, multi-satellite data were used for detecting seagrass beds in turbid waters and describing temporal changes over the past two decades. All seagrass spectra from different sensors (Landsat TM/ETM+, Aster, Spot-4, and Kompsat-2) have low reflectance in green and high in NIR band, whereas bare seawater showed opposite spectrum features. A training area in the green band and Mahalanobis Distance Classification (MDC) algorithm was adopted for classification and mapping of seagrass beds. Based on the underwater acoustic referencedata, overall accuracy of error matrix for Kompsat-2 image classification was 73% accurate. Despite the high turbidity seawater, this accuracy is considered relatively high precision. The average area of seagrass beds was 4.6 km2 from 1990to 2012, and it was consistently >4.0 km2. Seagrass beds are often inaccurately detected when using different spatial resolutions and turbid water, but the MDC method will be appropriate for turbid water of Korean coast.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/20441
Bibliographic Citation
3rd China-Korea Workshop on Marine Environment and Disaster monitoring using Remote sensing in the Yellow Sea, pp.21, 2018
Publisher
China-Korea
Type
Conference
Language
English
Publisher
China-Korea
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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