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系統識別號 U0026-2408201115425100
論文名稱(中文) 高密度空載光達資料應用於河川流態分類之評估
論文名稱(英文) Evaluation of High Density Airborne LiDAR Data for Instream Flow Type Classification
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 99
學期 2
出版年 100
研究生(中文) 林育立
研究生(英文) Yu-Li Lin
電子信箱 P66984045@mail.ncku.edu.tw
學號 p66984045
學位類別 碩士
語文別 英文
論文頁數 64頁
口試委員 指導教授-王驥魁
口試委員-吳富春
口試委員-莊明德
中文關鍵字 空載光達  流態 
英文關鍵字 LiDAR  Instream Flow Type 
學科別分類
中文摘要 河川棲地之分布對於河川管理上是非常重要的資訊,河川棲地的分類通常是根據水面流態、水深、底質材料及河川底部地形來決定。河川上游的生態棲地較為多樣化,具有值得詳細調查的價值,但由於河川上游通常位於山谷中,地形較為狹窄且險峻,會在水面留下因兩岸山壁所產生的陰影,造成影像分類的困擾。本研究使用空載光達進行水面流態的分類。空載光達在此應用的優勢為,不需受到日夜的限制、不受陰影的影響及可以避免水面波浪的反光。
本研究方法提出一個基於各流態水面的空間變異性的方法以及八個基於水面時間變異性的方法。前者是利用光達點雲在水表面高程資訊的標準差,評估各個流態水面的粗糙度以進行流態的分類。並且達到69.0%到88.2%的分類精度;後者是利用水表面光達點雲在不同航線間的變化,評估其時間變異性以進行流態的分類。此方法礙於本資料的掃描航線數量不足,其分類精度略差於前者。然而新一代的光達系統具備更高的定位精度及掃描頻率,使得本方法在河川流態分類上具有更大的潛力,提供快速、準確並且大範圍的河川棲地測繪。
英文摘要 Instream habitat mapping is an important task for river management. The conventional method for habitat mapping, which is ground-based survey to map instream features carry out by ecology surveyor, is time consuming and difficult to be applied to a large spatial extend. Remote sensing techniques like high resolution hyperspectral (HSRH) image, terrestrial laser scanning (TLS) and airborne laser scanning (ALS) have been successfully applied to replace the traditional method of habitat mapping. In this research, we propose using airborne LiDAR (Light Detection And Ranging), also known as ALS, for instream flow type classification. Standard deviation was applied to characterize instream habitats, and provides the classification accuracies ranging from 69.0% to 88.2%. Furthermore, methods which investigate the temporal variation of water surface to discriminate flow types are proposed. Among methods that adopt temporal variation, standard deviation of average surface elevation provides the classification accuracies ranging from 58.5% to 83.5%. As the LiDAR technique improved, this proposed method has a great potential to be a useful tool for flow type classification in the future.
論文目次 中文摘要 I
Abstract II
Acknowledgement III
Table of contents IV
List of Figures VI
List of Tables X
Chapter 1: Introduction 1
Chapter 2: Using High Density Airborne LiDAR Data for Instream Flow Type Classification 3
2.1 Introduction 3
2.2. Material and method 7
2.2.1. The study areas 7
2.2.2. LiDAR data 10
2.2.3. Water surface roughness for various flow types 11
2.2.4. Minimum requirement of point density 16
2.3. Results and Discussions 22
2.3.1 The training site 22
2.3.2 The test site 29
2.4. Conclusions 32
Chapter 3: Potential of Temporal Information of High Density Airborne LiDAR Data for Instream Flow Type Classification 34
3.1. Introduction 34
3.2. Material and Method 36
3.2.1 LiDAR data 36
3.2.2 Ground reference data 39
3.2.3 Flow type classification 39
3.3. Result and Discussions 46
3.3.1 Classification results 46
3.3.2 Effect of wind 52
3.3.3 Effect of exposed rocks 54
3.3.4 Effect of vehicle motion 56
3.4. Conclusions 57
Chapter 4: Conclusions 59
References 61
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