論文

2021年12月

Analysis of long-term (2002-2020) trends and peak events in total suspended solids concentrations in the Chesapeake Bay using MODIS imagery

JOURNAL OF ENVIRONMENTAL MANAGEMENT
  • Ali P. Yunus
  • ,
  • Yoshifumi Masago
  • ,
  • Yasuaki Hijioka

299
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1016/j.jenvman.2021.113550
出版者・発行元
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD

Water quality monitoring programs have been widely implemented worldwide to monitor and assess water quality and to understand its trends. However, water quality analysis based on point-source field observations is difficult to perform at large spatial and temporal scales. In this paper, a fully automated Google Earth Engine (GEE) application algorithm was developed to estimate the total suspended solids (TSS) concentration in the Chesapeake Bay based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery. Combining long-term archived satellite data (2002-2020) with field observations, the concentrations and spatiotemporal patterns of TSS in the bay water were evaluated. Time series analysis showed a statistically significant decreasing trend in TSS concentration between 2002 and 2020, suggesting that the sediment concentration in the bay has gradually been decreasing over the last two decades. The decreasing trend was observed in 49 out of 60 segments of the bay, implying that substantial progress has been made toward attaining the Chesapeake Bay water quality standards. Based on the monthly TSS analysis, 12 major peak events of TSS were identified in the Chesapeake Bay, which coincided with extreme winter blizzards and summer hurricane events. The GEE application and the results presented herein complement the existing monitoring program in attaining the water quality standards of the bay.

リンク情報
DOI
https://doi.org/10.1016/j.jenvman.2021.113550
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000704810300004&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.jenvman.2021.113550
  • ISSN : 0301-4797
  • eISSN : 1095-8630
  • ORCIDのPut Code : 98854124
  • Web of Science ID : WOS:000704810300004

エクスポート
BibTeX RIS