以全波形光達次足跡技術進行河床粗糙度之分析

Sub-Footprint Roughness Analysis for Riverbeds using Full-Waveform LiDAR Data

徐若堯(Jo-Yao Hsu)

指導教授 ： 韓仁毓

國立臺灣大學/工學院/土木工程學研究所/碩士(2018年)

https://doi.org/10.6342/NTU201801413

摘要

河床粗糙度是了解河流特性的一項重要的指標，常用於水利工程建設等項目之規劃；藉由河床上的粒徑大小、分布型態計算河床的粗糙度，進而可以分析河流流速與河床沖積等特性。本研究主要使用全波形光達之波形資訊輔助得到小於一個足跡範圍內的粒徑尺寸，作為河床之次足跡表面變化程度；本研究首先根據感測器的參數設定、地表面的幾何建立和河床粗糙度與粒徑大小相關等特性，進行參考波形的重建，用以作為特定航高和特定粒徑尺寸的標準波形。另一方面，將全波形光達之回波進行強度值的改正，消除在發射和接收過程中會造成能量耗損的影響因子，將回波回復至只有受到表面起伏影響的狀態。接著，將標準參考波形與改正後的回波進行波形匹配，殘差平方和最小之類別，即為該足跡的河床粗糙度值，並以正射影像、河床特性、自然灘地規則性和河床質調查報告書等資訊進行分類成果檢核與分析。
研究成果顯示，分類成果與河川之水理特性具有高度相關，粒徑的分布符合邊灘、沙洲的礫石堆積特性，且波形匹配之平均相似率達到85%~90%、加權計算後之平均粒徑值與現地調查量測結果僅有約3cm之差異，表示本研究所提出的方法可以作為獲得河床粒徑大小與粗糙度的另一種有效率之可行方案，並且將新式空間資訊技術與環境分析應用具體結合，解決現地河床質調查之人力與時間成本問題，可以用於輔助歷年來河床變化與沖積特性之分析。

關鍵字

粗糙度分析 ； 全波形光達 ； 遙感探測 ； 波形重建 ； 次足跡法 ； 河流型態

並列摘要

Surface roughness is an important indicator to understand river morphology. It is possible to estimate the roughness of riverbeds by surveying their grain size, distribution and type, so that the flow rate and the alluvial characteristics of the riverbeds can be analyzed. This study aims to use the waveform information of a full-waveform LiDAR data to assist in obtaining the grain size within a footprint. First, the reconstruction of the reference waveform is performed as a standard waveform based on the parameters of the sensor and the geometry of the ground surface. On the other hand, an intensity correction of observed full-waveform LiDAR data is applied. This study uses Lambertian reflectance model and DEM information to eliminate the effects of energy loss during transmission. Next, the reference waveforms are used to compare with the observed waveform data and the waveform matching is performed. Finally, the grain size category can be determined by the above approach and the actual riverbed roughness value is thus identified. The experimental results show that the classification results are highly correlated with the river's hydraulic characteristics. The average similarity rate of waveform matching is 85%~90%, and the weighted average grain size value is only about 3cm difference from the on-site riverbed sampling report. It gives an evidence that the proposed approach can become an efficient alternative for evaluating the surface roughness in a river morphology analysis. It solves the problem of laborious and time-consuming of the on-site riverbed material sampling tasks, and can be used to assist the analysis of riverbed changes and alluvial characteristics over the years.

並列關鍵字

Roughness analysis ； Full-waveform LiDAR ； Remote sensing ； Waveform reconstruction ； Sub-footprint method ； River morphology

參考文獻

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