Mapping Submerged Aquatic Vegetation along the Central Vietnamese Coast Using Multi-Source Remote Sensing

被引:7
作者
Tran Ngoc Khanh Ni [1 ]
Hoang Cong Tin [1 ]
Vo Trong Thach [2 ]
Jamet, Cedric [3 ,4 ]
Saizen, Izuru [5 ]
机构
[1] Hue Univ, Univ Sci, Fac Environm Sci, Hue City 530000, Vietnam
[2] Nhatrang Inst Technol Res & Applicat, Nha Trang City 650000, Vietnam
[3] Univ Sci & Technol Hanoi, LOTUS, Hanoi 100000, Vietnam
[4] Univ Littoral Cote dOpale, Univ Lille, Lab Oceanol & Geosci, LOG,CNRS,UMR 8187, F-59000 Lille, France
[5] Kyoto Univ, Grad Sch Global Environm Studies, Sakyo Ku, Yoshida Honmachi, Kyoto 6068501, Japan
关键词
Submerged aquatic vegetation; VNREDSat-1; Sentinel-2; Landsat-8; distribution map; temporal change map; SEAGRASS; BAY; REFLECTANCE; BIOMASS; WATERS; COVER;
D O I
10.3390/ijgi9060395
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Submerged aquatic vegetation (SAV) in the Khanh Hoa (Vietnam) coastal area plays an important role in coastal communities and the marine ecosystem. However, SAV distribution varies widely, in terms of depth and substrate types, making it difficult to monitor using in-situ measurement. Remote sensing can help address this issue. High spatial resolution satellites, with more bands and higher radiometric sensitivity, have been launched recently, including the Vietnamese Natural Resources, Environment, and Disaster Monitoring Satellite (VNREDSat-1) (V1) sensor from Vietnam, launched in 2013. The objective of the study described here was to establish SAV distribution maps for South-Central Vietnam, particularly in the Khanh Hoa coastal area, using Sentinel-2 (S2), Landsat-8, and V1 imagery, and then to assess any changes to SAV over the last ten years, using selected historical data. The satellite top-of-atmosphere signals were initially converted to radiance, and then corrected for atmospheric effects. This treated signal was then used to classify Khanh Hoa coastal water substrates, and these classifications were evaluated using 101 in-situ measurements, collected in 2017 and 2018. The results showed that the three satellites could provide high accuracy, with Kappa coefficients above 0.84, with V1 achieving over 0.87. Our results showed that, from 2008 to 2018, SAV acreage in Khanh Hoa was reduced by 74.2%, while gains in new areas compensated for less than half of these losses. This is the first study to show the potential for using V1 and S2 data to assess the distribution status of SAV in Vietnam, and its outcomes will contribute to the conservation of SAV beds, and to the sustainable exploitation of aquatic resources in the Khanh Hoa coastal area.
引用
收藏
页数:27
相关论文
共 50 条
[21]   Benthic habitat sediments mapping in coral reef area using amalgamation of multi-source and multi-modal remote sensing data [J].
Ji, Xue ;
Yang, Bisheng ;
Wei, Zheng ;
Wang, Mingchang ;
Tang, Qiuhua ;
Xu, Wenxue ;
Wang, Yanhong ;
Zhang, Jingyu ;
Zhang, Lin .
REMOTE SENSING OF ENVIRONMENT, 2024, 304
[22]   Research on cropping intensity mapping of the Huai River Basin (China) based on multi-source remote sensing data fusion [J].
Wang, Yihang ;
Fan, Lin ;
Tao, Ranting ;
Zhang, Letao ;
Zhao, Wei .
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (09) :12661-12679
[23]   Open-Source Analysis of Submerged Aquatic Vegetation Cover in Complex Waters Using High-Resolution Satellite Remote Sensing: An Adaptable Framework [J].
de Grandpre, Arthur ;
Kinnard, Christophe ;
Bertolo, Andrea .
REMOTE SENSING, 2022, 14 (02)
[24]   High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images [J].
Yang, Lingbo ;
Wang, Limin ;
Abubakar, Ghali Abdullahi ;
Huang, Jingfeng .
REMOTE SENSING, 2021, 13 (06)
[25]   Mapping Submerged Aquatic Vegetation Using RapidEye Satellite Data: The Example of Lake Kummerow (Germany) [J].
Fritz, Christine ;
Doernhoefer, Katja ;
Schneider, Thomas ;
Geist, Juergen ;
Oppelt, Natascha .
WATER, 2017, 9 (07)
[26]   Mapping the Forest Height by Fusion of ICESat-2 and Multi-Source Remote Sensing Imagery and Topographic Information: A Case Study in Jiangxi Province, China [J].
Luo, Yichen ;
Qi, Shuhua ;
Liao, Kaitao ;
Zhang, Shaoyu ;
Hu, Bisong ;
Tian, Ye .
FORESTS, 2023, 14 (03)
[27]   Contribution of multi-source remote sensing data to predictive mapping of plant-indicator gradients within Swiss mire habitats [J].
Ecker, Klaus ;
Waser, Lars T. ;
Kuechler, Meinrad .
BOTANICA HELVETICA, 2010, 120 (01) :29-42
[28]   Spatiotemporal fusion of multi-source remote sensing data for estimating aboveground biomass of grassland [J].
Zhou, Yajun ;
Liu, Tingxi ;
Batelaan, Okke ;
Duan, Limin ;
Wang, Yixuan ;
Li, Xia ;
Li, Mingyang .
ECOLOGICAL INDICATORS, 2023, 146
[29]   Assessing variability in post-fire forest structure along gradients of productivity in the Canadian boreal using multi-source remote sensing [J].
Bolton, Douglas K. ;
Coops, Nicholas C. ;
Hermosilla, Txomin ;
Wulder, Michael A. ;
White, Joanne C. .
JOURNAL OF BIOGEOGRAPHY, 2017, 44 (06) :1294-1305
[30]   Characterizing water body changes in Poyang lake using multi-source remote sensing data [J].
Wang, Wenyu ;
Yang, Peng ;
Xia, Jun ;
Zhang, Shengqing ;
Luo, Xiangang ;
Hu, Sheng ;
Li, Jiang ;
Chen, Nengcheng ;
Zhan, Chesheng .
ENVIRONMENTAL DEVELOPMENT, 2023, 48