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  • 學位論文

DEM融合初探

A Preliminary Study of DEM Fusion

指導教授 : 史天元

摘要


使用數值高程模型(Digital Elevation Model, DEM)進行地形分析、模擬、與探討,是十分常見的工作。產製DEM的資料來源也十分多樣化,各種不同的觀測方式、產製DEM之方法等程序都會影響DEM之成果。不同之DEM有不同的特性,利用資料融合(Data Fusion)的概念將DEM進行融合,可以獲得比原本還更多的資訊,也可藉由分析這些資訊以得到更多的結果。 本研究中研究區域有荷蘭地區及臺灣地區,荷蘭地區使用利用空載光達產製之0.5m解析度AHN2 DEM及公開資料之SRTM DEM與ASTER GDEM。臺灣地區使用公開資料之SRTM DEM與ASTER GDEM,兩地區之SRTM DEM與ASTER GDEM解析度皆為30m。本研究主旨為探討不同DEM融合之方法並分析其高程變化情況,使用方法為透過傅立葉轉換之頻率濾波法,與空間域中之加權平均法,以及透過小波轉換之小波濾波法。根據不同之方法融合岀不同之成果,再藉由數值高程模型相減法(Difference of DEMs, DoD)分析高程變化較為明顯之區域。荷蘭地區採用0.5公尺網格以空載光達測製的AHN2為參考,臺灣地區則以ASTER GEDM 為參考。 根據差值分析之結果,在荷蘭地區的RMSE值皆低於臺灣地區的RMSE。初判應為地形之緣故,臺灣地區之多山地形高程差較大,山區部分也出現相對上差值較大之情況。至於DEM融合,三種方法均未能呈現精度提升。

關鍵字

DEM融合 SRTM DEM ASTER GDEM

並列摘要


Utilizing Digital Elevation Model (DEM) for terrain analysis, modelling, and investigation, is a type of work frequently performed. There are different kinds of data sources which could be applied for producing DEM. Each type of data source and production operation would result different characteristics of the DEM produced. With data fusion techniques, there is a possibility that more detailed information could be gathered, and also provide another way for topographic analysis. There are two study areas in this research, Netherlands and Taiwan. The SRTM DEM and ASTER GDEM, which are open source data with 30m space resolution, are used for DEM fusion in both study areas. This research applies three approaches in DEM fusion: (1) Fourier transform and frequency-domain filter, (2) weighted average, (3) wavelet transform and filter. Differences of DEMs are then computed for evaluation. In Netherlands, AHN2 with 0.5m space resolution, produced with airborne LiDAR, is used as reference. In Taiwan, the ASTER GDEM is used as reference. Based on the DEM differences, the RMSEs from Netherlands datasets are smaller than those in Taiwan. The topographic nature is likely to be the cause. Taiwan is featured with larger relief than Netherlands. And within Taiwan, the mountain area exhibits relatively larger deviations than other area. Regarding the DEM fusion schemes, none of the three studied improved the accuracy.

並列關鍵字

DEM Fusion SRTM DEM ASTER GDEM

參考文獻


卓芫陞,2015。應用DEM區塊法探討八掌溪河道變遷,碩士論文,國立交通大
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Visualizing Distributions from Multi-Return Lidar Data to Understand Forest
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