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Monitoring the Spatio-Temporal Distribution of Ulva prolifera in the Yellow Sea (2020–2022) Based on Satellite Remote Sensing.
- Source :
-
Remote Sensing . Jan2023, Vol. 15 Issue 1, p157. 22p. - Publication Year :
- 2023
-
Abstract
- The green tide caused by Ulva prolifera (U. prolifera) is becoming more severe as climate change and human activity accelerate, endangering tourism, aquaculture, and urban landscapes in coastal cities. In order to understand the spatio-temporal distribution of U. prolifera in response to the green tide disaster, this study used the Haiyang-1C (HY-1C) satellite accompanied by the Sentinel-2 and GaoFen-1 (GF-1) satellites to systematically monitor U. prolifera between 2020 and 2022. The consistency of U. prolifera distribution between the HY-1C and Sentinel-2 satellites, as well as the HY-1C and GF-1 satellites, was first investigated and the determination coefficients (R2) were 0.966 and 0.991, respectively, which supports the feasibility of China's first ocean water color operational satellite, HY-1C, for U. prolifera monitoring. Therefore, the spatio-temporal distribution of U. prolifera is studied herein, along with the influence range, influence area, and drift paths. From 2020 to 2022, U. prolifera appeared in late May and lasted for 61, 88, and 73 days. Additionally, the in influence area continuously decreased in 2020 and 2022, while it generally increased and then decreased in 2021. It is an interesting phenomenon that when the maximum influence area occurred at the early stage of U. prolifera in both 2020 and 2022, the drift paths tended to move southward after traveling northward. The overall trend of the drift path in 2021 was to head northward. Thus, the study of the dynamic evolution, influence range, influence area, and drift paths of U. prolifera is helpful to promote the systematic development of emergency response mechanisms for U. prolifera. [ABSTRACT FROM AUTHOR]
- Subjects :
- *REMOTE sensing
*ULVA
*OCEAN color
*SEAWATER
*CLIMATE change
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 161182974
- Full Text :
- https://doi.org/10.3390/rs15010157