MyJournals Home  

RSS FeedsRemote Sensing, Vol. 11, Pages 680: CARIB18: A Stable Geodetic Reference Frame for Geological Hazard Monitoring in the Caribbean Region (Remote Sensing)

 
 

21 march 2019 17:01:13

 
Remote Sensing, Vol. 11, Pages 680: CARIB18: A Stable Geodetic Reference Frame for Geological Hazard Monitoring in the Caribbean Region (Remote Sensing)
 


We have developed a Stable Caribbean Reference Frame 2018 (CARIB18) using long-term continuous observations from 18 continuously operating Global Positioning System (GPS) stations fixed on the margins of the stable portion of the Caribbean plate. The frame stability of CARIB18 is approximately 0.7 mm/year in the horizontal direction and 0.9 mm/year in the vertical direction. A method that employs a total of seven parameters for transforming positional time series from a global reference frame (IGS14) to a regional reference frame is introduced. The major products from this study include the seven parameters for realizing CARIB18 coordinates and three-component site velocities of 250 continuous GPS stations (>3 years) with respect to CARIB18. Geological hazard monitoring using GPS has traditionally been performed using the carrier-phase differential method that requires single or multiple reference stations to be simultaneously operated in the field. CARIB18 allows for precise geological hazard monitoring using stand-alone GPS, which substantially reduces field costs and simplifies logistics for long-term geological hazard monitoring. Applications of CARIB18 in plate motion, post-seismic, and volcano monitoring and research are demonstrated in this article. The regional reference frame will be periodically updated every few years with more reference stations and longer periods of observations to mitigate the degradation of the frame over time and will be synchronized with the updates of the International GNSS Service (IGS) IGS reference frame.


 
75 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 681: A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion (Remote Sensing)
Remote Sensing, Vol. 11, Pages 691: Automatic Counting of in situ Rice Seedlings from UAV Images Based on a Deep Fully Convolutional Neural Network (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten