本文選題：污水監(jiān)測　切入點：遙感　出處：《大連海事大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：海洋是地球上生命的搖籃,蘊藏著巨大的能源,世界上各個國家的發(fā)展都離不開海洋。隨著我國日新月異地發(fā)展,我國沿海和內(nèi)河區(qū)域船舶溢油污染、壓載水和船舶生活垃圾任意排放量與日俱增,陸源污染物的海洋排放量持續(xù)增加,我國沿海水域海水污染問題變得日益嚴(yán)峻,不僅影響著人們的日常生活和身體健康,也制約了我國航運業(yè)的發(fā)展。為了規(guī)范船舶操作、保護海洋環(huán)境,IMO制定了一些船舶防污染公約,比如《1954年油污公約》(OIL54)、《MARPOL73/78公約》、《(2001年控制船舶防污底系統(tǒng)國際公約》(AFS Convention 2004)、《國際安全與環(huán)保拆船公約》等。我國擁有漫長的海岸線,保護海洋環(huán)境防治海水污染必須引起我們的重視。IMO制定的防治海洋污染公約的實施將對我國的航運業(yè)產(chǎn)生巨大的影響。傳統(tǒng)的海水水質(zhì)監(jiān)測方法效率較低,無法獲得大范圍海水的水質(zhì)狀況,尋找一種更加精確、簡便的沿海海域污水監(jiān)測模型,對海水水質(zhì)進行監(jiān)測是一項十分重要的工作。隨著全球遙感理論不斷革新,海洋污水遙感監(jiān)測技術(shù)逐漸由定性遙感向定量遙感轉(zhuǎn)變。將遙感技術(shù)應(yīng)用于海洋水質(zhì)監(jiān)測領(lǐng)域,不僅可以對某一區(qū)域進行長期監(jiān)測,從而對海水污染趨勢進行預(yù)測,而且節(jié)約了污水監(jiān)測成本。對海水水質(zhì)進行監(jiān)測同樣符合IMO船舶防污染相關(guān)公約的精神。本文對我國沿海水域污水遙感監(jiān)測方法進行了深入的理論研究,污水遙感監(jiān)測分為定性監(jiān)測點和定量監(jiān)測。論文利用HJ1A/1B遙感衛(wèi)星數(shù)據(jù)對我國香港海域海水水質(zhì)進行了定量反演,同時利用NOAA系列衛(wèi)星數(shù)據(jù)對2010年8月2日河北秦皇島老龍頭海域船舶排放的污水團的擴散及漂移狀況進行了解譯分析。在反演香港海域海水水質(zhì)時,以反演葉綠素濃度指標(biāo)為例,首先從我國資源衛(wèi)星應(yīng)用中心獲取2012年12月26日香港海域的HJ1A/1B影像資料,其次從香港環(huán)保署獲取2012年12月香港海域76個水質(zhì)監(jiān)測點的葉綠素濃度現(xiàn)場數(shù)據(jù)。本文利用三種方法對香港海域葉綠素濃度進行了分析：①用Pearson相關(guān)性分析法分析了100多種波段組合,并選取Pearson相關(guān)性系數(shù)最大的波段組合對葉綠素濃度進行多元線性回歸分析；②將HJ1A/1B遙感數(shù)據(jù)四個波段數(shù)據(jù)作為輸入,建立BP神經(jīng)網(wǎng)絡(luò)模型對香港海域葉綠素濃度進行反演；③建立RBF神經(jīng)網(wǎng)絡(luò)模型對葉綠素濃度進行反演。研究結(jié)果表明：多元線性回歸模型和RBF神經(jīng)網(wǎng)絡(luò)模型反演結(jié)果相對誤差較大,約為0.67；BP神經(jīng)網(wǎng)絡(luò)反演結(jié)果誤差較小,相對誤差約為0.38。2010年8月2日秦皇島老龍頭海域發(fā)生海水污染事故,給當(dāng)?shù)仞B(yǎng)殖戶造成了巨大損失,本文利用遙感圖像增強的相關(guān)方法,對NOAA系列遙感影像進行人工目視解譯,最終發(fā)現(xiàn)海水污染源頭及污水漂移擴散規(guī)律。
[Abstract]:The ocean is the cradle of life on the earth and contains huge energy resources. The development of every country in the world can not be separated from the ocean. With the rapid development of our country, the oil spill pollution from ships in the coastal and inland areas of our country, The amount of ballast water and domestic garbage discharged from ships is increasing with each passing day, and the marine discharge of land-based pollutants is increasing continuously. The problem of sea water pollution in coastal waters of our country becomes more and more serious, which not only affects the daily life and health of people, but also affects the health of people. It also restricts the development of the shipping industry in China. In order to regulate the operation of ships, IMO has formulated a number of anti-pollution conventions on ships for the protection of the marine environment. For example, the 1954 Oil pollution Convention, the MARPOL73/78 Convention, the 2001 International Convention for the Control of Anti-fouling bottom Systems on ships, the International Convention on the Safety and Environmental Protection of ships, etc. China has a long coastline. To protect the marine environment and prevent sea water pollution, we must pay attention to the implementation of the convention on the prevention and control of marine pollution made by IMO, which will have a great impact on the shipping industry of our country. The traditional methods for monitoring the water quality of sea water are less efficient. It is a very important task to find a more accurate and simple model for monitoring the sewerage water quality in coastal waters, which is unable to obtain the water quality of a wide range of seawater, and it is a very important task to monitor the water quality of the sea water. With the innovation of the theory of global remote sensing, The remote sensing monitoring technology of marine sewage is gradually changing from qualitative remote sensing to quantitative remote sensing. Applying remote sensing technology to the field of ocean water quality monitoring can not only monitor a certain area for a long time, so as to predict the trend of sea water pollution. Moreover, the cost of sewage monitoring is saved. The monitoring of sea water quality is also in line with the spirit of IMO ship pollution Prevention Convention. This paper makes a deep theoretical study on the remote sensing monitoring method of sewage in coastal waters of our country. The remote sensing monitoring of sewage is divided into qualitative monitoring points and quantitative monitoring points. The paper uses HJ1A/1B remote sensing satellite data to carry out quantitative inversion of seawater quality in Hong Kong sea area of China. At the same time, using the NOAA series satellite data, the paper interprets and analyzes the dispersion and drift of sewage pellets discharged by vessels in the Laotou Sea area of Qinhuangdao, Hebei Province in August 2nd 2010. When retrieving the seawater quality in Hong Kong waters, Taking the index of chlorophyll concentration inversion as an example, the HJ1A/1B image data of Hong Kong sea area in December 26th 2012 were obtained from the China Resource Satellite Application Center. Secondly, the field data of chlorophyll concentration in 76 water quality monitoring sites in Hong Kong waters in December 2012 were obtained from the Hong Kong Environmental Protection Agency. The chlorophyll concentration in Hong Kong waters was analyzed by using three methods: 1 and Pearson correlation analysis. More than 100 band combinations, The band combination with the largest correlation coefficient of Pearson was selected to carry out multivariate linear regression analysis on chlorophyll concentration. The four bands of HJ1A/1B remote sensing data were used as input. A BP neural network model was established to invert chlorophyll concentration in Hong Kong Sea area. (3) RBF neural network model was established to invert chlorophyll concentration. The results show that multiple linear regression model and RBF neural network model are used to invert chlorophyll concentration. The relative error of fruit is large, The inversion error of BP neural network is about 0.67 and the relative error is about 0.38. In August 2nd, 2010, the sea water pollution accident occurred in the Laotou sea area of Qinhuangdao, which caused huge losses to the local farmers. In this paper, the method of remote sensing image enhancement is used in this paper. The artificial visual interpretation of NOAA series remote sensing images was carried out, and the source of seawater pollution and the rule of sewage drift diffusion were found.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：X87

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