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系統識別號 U0026-2808201122274300
論文名稱(中文) 利用基因演算法合併淺水區半解析模式處理水下高光譜反射率以反算底棲珊瑚礁分布與水質
論文名稱(英文) Retrieving benthic coral reef distribution and water quality using the genetic analytical and shallow water semi-analytical model to process the underwater hyperspectral reflectanc
校院名稱 成功大學
系所名稱(中) 地球科學系碩博士班
系所名稱(英) Department of Earth Sciences
學年度 99
學期 2
出版年 100
研究生(中文) 鍾曉緯
研究生(英文) Hsiao-Wei Chung
學號 l96981040
學位類別 碩士
語文別 中文
論文頁數 93頁
口試委員 指導教授-劉正千
口試委員-楊文昌
口試委員-陳震遠
口試委員-張智華
中文關鍵字 水色  遙測  珊瑚礁  海床  水質  高光譜影像  監督式分類 
英文關鍵字 remote sensing  coral reefs  coastal water quality  retrieving the properties of benthic coral reefs  seabed 
學科別分類
中文摘要 珊瑚礁通常生長在溫暖、乾淨、清澈與高氧含量的海水中。任何環境的些微變化會對珊瑚礁的生存造成致命的威脅。因此珊瑚礁群聚的變化可以作為一種重要的環境指標。在台灣地區的墾丁國家公園擁有豐富的珊瑚礁資源,不論是在觀光發展或是生態保育方面都佔有非常重要的地位。
然而,近年來的重大天災事件,如2009年的莫拉克颱風,已造成墾丁地區約50%的珊瑚礁被破壞,有些珊瑚礁甚至已經是無法回復的狀態了,所以當務之急,我們應該要建立一個可以快速評估珊瑚礁生長狀況的方法做為往後無論是天然災害或是人為破壞情況的緊急應變措施。
傳統的潛水監測方法雖然可以準確的評估珊瑚礁的生長狀態,但是需要耗費相當的人力以及時間再加上整合的資料時間間隔太長,在珊瑚礁生態系發生劇變時(颱風或颶風)無法做出即時的應變,在考量到時間與空間兩個重要的因素之下,我們應該要選擇最可以提供快速且大範圍監測的方法:水下高光譜遙測。
然而,遙測平台種類繁多,本研究中所使用的遙測平台為水下拖曳式平台,搭載高光譜儀進行水底珊瑚礁生態系底棲分布物質的遙測反射光譜訊號收集,此法雖然會犧牲時間解析度,但是卻可以提升空間解析度至公分等級(水深10m時,空間解析度為5cm)以及成功除去大氣粒子對於水底遙測反射訊號之干擾,可以獲得較高的水底底棲物質反算精確度。
水色遙測技術可以在兩個高空間和高時間分辨率下評估珊瑚礁的分布狀態。要直接從水的顏色信號檢索水質和底棲珊瑚礁的分布與屬性,需要一個可靠的演算模型。在本研究中,反算水質所需要的水層訊號之外還必須考慮來自於海床的各種反射光譜(珊瑚礁、藻類或沙地),有鑑於此,本研究當中利用GA- SA(基因演算法和半解析法)模型合併淺水區域的半解析模式所產生之新模型:GA-SSA(基因演算法和淺水區域半解析法),並進行分類底部為五種不同的類型,此六種珊瑚礁生態系常見的水底物質包括珊瑚、海草、藻類(綠藻,紅藻和褐藻)和沙子。利用HydroLight輻射傳輸模型,模擬各種不同的水下光學環境因子(葉綠素濃度、水深),建立我們所需要的反射光譜。新的模型能從輸入底質反射光譜反算水質狀況和底棲珊瑚礁分布狀況。反算結果,新模型能達到滿意的精度高達 80%。
除此之外,本研究也在墾丁海域利用高光譜儀進行珊瑚生態系的光譜資料庫收集,建立之光譜資料庫可應用在未來各項相關研究。
最後,本研究中也在墾丁海域利用拖曳式水下載具V-Fin進行水下高光譜影像之收取。目前所量測的高光譜影像資訊進行四階導數運算找出特徵波段後,利用監督式分類的方法對水下高光譜掃描儀收集的數據進行分類。再將不同時間所量測的水下高光譜影像進行分類,將兩張影像的結果進行交叉比對。
本研究之成果在反算模型發展與現地資料量測工作都有所突破,是後續發展珊瑚礁遙測技術之重要方法。未來將繼續改良水下高光譜影像之收取技術,達成利用GA-SSA直接反算水質與水底底質分類之工作,以評估在墾丁國家公園中珊瑚礁的狀況。
英文摘要 Coral reefs prefer to reside in warm, clean, clear waters with high oxygen content. Any deterioration of their environment would affect the life of coral reefs. Therefore, coral reefs serve as an important indicator of the environmental condition. Kenting National Park enjoys the most abundant coral reefs around Taiwan. However, recent extreme weather events, such as Typhoon Morakot in 2009, destroyed 50% of coral reefs in this area. The technique of water color remote sensing is promising in assessing the status of coral reefs at both high spatial and high temporal resolutions. However, to retrieve the water quality and the properties of benthic coral reefs directly from the water color signal requires a robust algorithm that has been validated against comprehensive in situ data and model simulations. In this research, we improve upon a genetic algorithm/semi-analytical model by taking into account the properties of benthic coral reefs, classifying the bottom into six different types; coral reefs, sand, sea grass, and green, red, and brown algae. A spectral library of bottom reflectance is established from in situ data measured in Kenting National Park and data simulated by the HydroLight radiative transfer model. Our new model, Genetic Algorithm and Shallow water Semi-Analytical model (GA-SSA), is able to iterate for an optimized solution of water quality and the properties of the benthic coral reef from the input of bottom reflectance spectrum data. These solutions are then compared to the conditions of water quality and benthic coral reef properties, under which the bottom reflectance spectra are measured in situ or simulated by the Hydrolight algorithm. Our results demonstrate that our new model is able to achieve accuracy as high as 80%. In addition, we also used a hyperspectral imager to collect a coral ecosystem spectral database in the Kenting area. The establishment of a database can be applied to future research. This study is also the first time in the Kenting area where a towed underwater vehicle (V-Fin) was used to collect underwater hyperspectral imaging. We use a supervised classification method to classify underwater hyperspectral data and assess the coral reef condition in Kenting National Park. In the future, we will continue to improve the technology of underwater image collection and use GA-SSA to retrieve the water quality and bottom type classification. Once established, this system will enable us to monitor and address changes in our valuable coral reef ecosystem.
論文目次 摘要 I
Abstract III
致謝 V
目錄 VI
圖目錄 IX
表目錄 XII
第 1 章 研究緣起與目的 1
1.1 研究背景 1
1.2 研究目的 9
1.3 論文架構 10
第 2 章 文獻回顧 12
2.1 相關研究 12
2.2 建立反射光譜資料庫 15
2.3 水體光學性質 16
2.4 基因演算法GA(GENETIC ALGORITHM) 20
2.5 輻射傳輸模式 20
2.6 水層修正 21
第 3 章 研究方法 25
3.1 研究區域 25
3.2 模擬資料 27
3.2.1 合成固有光學性質IOPs 27
3.2.2 以輻射傳輸模式獲取AOPs 31
3.2.3 Hydrolight參數設定 31
3.2.4 合成遙測反射光譜rrs 35
3.2.5 資料處理流程 38
3.3 現地光譜資料庫建立 39
3.3.1 儀器介紹 39
3.3.2 現地光譜資料建立作業流程 40
3.3.3 現地光譜資料處理方法 43
3.4 利用水下高光譜儀測繪珊瑚礁海域 44
3.4.1 儀器介紹 45
3.4.2 水下高光譜影像收取作業流程 45
3.4.3 水下高光譜影像分類資料處理方法 49
第 4 章 結果與討論 51
4.1 模擬資料處理結果 51
4.1.1 底質類型反算與葉綠素濃度之關係 51
4.1.2 底質類型反算與水深之關係 54
4.1.3 水體固有光學性質之反算結果 55
4.2 現地光譜資料庫建立結果 58
4.3 水下高光譜影像分類結果 61
第五章 結論與建議 70
參考文獻 73
附錄一 合成資料結果 76
附錄二 墾丁海域光譜資料庫 81
附錄三 水下高光譜影像 92
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