The Study Of Using Ground-Based LiDAR And Robotic Total Station To Measure Sea Level Variations / 應用地面光達及全站儀測量海水位之研究

碩士 / 國防大學理工學院 / 空間科學碩士班 / 100 / This study used Ground Based LiDAR and Robotic Total Station (RTS) as the main instruments to measure sea level variation. Testing area locates at the Nanliao tide gauge station in Hsinchu. The Ground Based LiDAR scanned through the boundary of water and the shore, then analysis the change of point cloud intensity. RTS was used to observe a reflector fixed on a bouy, and recorded the coordinate variation of the reflector. Two methods were used to obtain the data of sea level variation in this study.
After developed the appropriate operation procedures for both fieldwork and data post-processing, fieldwork were executed in 22 of November 2011, 17 of January 2012 and 28 of March 2012. The duration of every observation is 5.5 hours. The first stage of data post-processing, Multiresolution Analysis of wavelet was used to detect the saltation of the point cloud intensity in the edge. The data of RTS only chooses height component to use. All the observed data processed then compared with the Central Weather Bureau tide gauge data of Nanliao. The results show that the RMSE(Root Mean Square Error) of LiDAR data in three experiments are 3.44cm, 4.88cm and 4.34cm. And the Correlation Coefficient are 0.9997, 0.9994 and 0.9976. The RMSE of RTS data are 9.72cm, 4.81cm and 9.31cm. The Correlation Coefficient are 0.999, 0.9991 and 0.9975.
This study also used curve fitting with Boltzman function to detect the saltation edge of the point cloud intensity. The results show that the maximum difference between LiDAR data and tide gauge data is 8.92cm. And the RMSE of LiDAR observations in three experiments are 2.81cm, 4.1cm and 1.98cm. The Correlation Coefficient are 0.9996, 0.9997 and 0.9994.

Identiferoai:union.ndltd.org:TW/100CCIT0367007
Date January 2012
CreatorsChien, Chih Feng, 簡志峰
ContributorsChen, Sung An, 陳松安
Source SetsNational Digital Library of Theses and Dissertations in Taiwan
Languagezh-TW
Detected LanguageEnglish
Type學位論文 ; thesis
Format69

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