本文選題：渤海灣 + 葉綠素a　； 參考：《中國(guó)地質(zhì)大學(xué)(北京)》2015年碩士論文

【摘要】：過(guò)去幾十年中,在我國(guó)經(jīng)濟(jì)持續(xù)快速發(fā)展的同時(shí),受氣候和人類(lèi)活動(dòng)的雙重影響,我國(guó)沿海海域的環(huán)境和空間結(jié)構(gòu)發(fā)生了很大的變化,海洋環(huán)境的服務(wù)和生態(tài)平衡功能受到極大影響。其中,渤海灣由于其半封閉的地理位置,海水循環(huán)自凈能力低于我國(guó)其他海域,因此監(jiān)測(cè)渤海灣水質(zhì)狀況對(duì)合理評(píng)估其生態(tài)環(huán)境、資源開(kāi)發(fā)和利用的程度具有重要意義。測(cè)定渤海灣葉綠素a濃度,對(duì)于評(píng)價(jià)其水質(zhì)健康狀況、水產(chǎn)資源分布及污染程度等具有指示作用。應(yīng)用遙感技術(shù)可以彌補(bǔ)傳統(tǒng)調(diào)查手段的不足,實(shí)現(xiàn)大范圍、長(zhǎng)時(shí)間連續(xù)的水質(zhì)參數(shù)監(jiān)測(cè)。盡管現(xiàn)在已有成形的方法可以作用于海洋葉綠素a濃度的反演,可是這些模型可能不適用于渤海灣的渾濁水體,因此,需要建立針對(duì)渤海灣海域的監(jiān)測(cè)模型。本文基于渤海灣水體的光譜特征研究,利用實(shí)測(cè)葉綠素a濃度數(shù)據(jù)和準(zhǔn)同步的多光譜ETM影像,構(gòu)建了具有渤海灣區(qū)域特色的葉綠素a濃度的遙感監(jiān)測(cè)反演模型。依據(jù)定量遙感的反演需求,針對(duì)ETM影像進(jìn)行了一系列預(yù)處理工作,完成了精確提取遙感影像信息的前期準(zhǔn)備。通過(guò)分析ETM影像各波段及波段組合與水體葉綠素a濃度的相關(guān)性,選取了敏感性較強(qiáng)的波段組合作為變量,運(yùn)用逐步回歸分析方法,探尋這些變量與葉綠素a濃度之間的定量關(guān)系,建立了R2為0.864、RMSE為0.957的統(tǒng)計(jì)模型。為了進(jìn)一步提高反演精度,運(yùn)用人工神經(jīng)網(wǎng)絡(luò)技術(shù),通過(guò)對(duì)比不同結(jié)構(gòu)的網(wǎng)絡(luò)的訓(xùn)練結(jié)果,確定了一個(gè)三層結(jié)構(gòu)的BP神經(jīng)網(wǎng)絡(luò)模型,其中ETM影像1-4波段反射率作為輸入節(jié)點(diǎn),葉綠素a濃度實(shí)測(cè)值作為輸出節(jié)點(diǎn),隱含層包含8個(gè)節(jié)點(diǎn),該模型的R2為0.956、RMSE為0.856。結(jié)果表明,BP神經(jīng)網(wǎng)絡(luò)模型具有更好的擬合效果,為利用遙感技術(shù)快速、準(zhǔn)確地監(jiān)測(cè)水域葉綠素a濃度提供了可靠的基礎(chǔ)和方法。應(yīng)用構(gòu)建的統(tǒng)計(jì)模型反演獲得了2007年至2010年的葉綠素a濃度分布圖,用以研究渤海灣葉綠素a濃度的空間分布特征和時(shí)間變化規(guī)律。通過(guò)分析發(fā)現(xiàn),渤海灣葉綠素a濃度分布大致呈現(xiàn)南部稍高于北部,近岸高于遠(yuǎn)岸,且隨離岸距離增加而減小的空間特征。其葉綠素a濃度年季差異不明顯,由于降水、水溫等因素影響,春夏季總體高于秋季。
[Abstract]:In the past few decades, with the sustained and rapid economic development of our country, the environment and spatial structure of our coastal waters have undergone great changes due to the dual influence of climate and human activities. The service and ecological balance functions of the marine environment are greatly affected. Because of its semi-closed geographical location, the self-purification capacity of seawater circulation in Bohai Bay is lower than that in other sea areas in China. Therefore, monitoring the water quality of Bohai Bay is of great significance to evaluate its ecological environment, exploitation and utilization of resources. The determination of chlorophyll a concentration in Bohai Bay can be used to evaluate the health of water quality, distribution of aquatic resources and the degree of pollution. The application of remote sensing technology can make up for the deficiency of traditional investigation methods and realize the monitoring of water quality parameters in a wide range and for a long time. Although the existing methods can be used to invert the concentration of chlorophyll a in the ocean, these models may not be applicable to the muddy waters of the Bohai Bay. Therefore, it is necessary to establish a monitoring model for the Bohai Bay. Based on the spectral characteristics of the Bohai Bay water body and the measured chlorophyll a concentration data and quasi-synchronous multispectral ETM image, a remote sensing monitoring inversion model of chlorophyll a concentration in the Bohai Bay region is established in this paper. According to the demand of quantitative remote sensing inversion, a series of pre-processing work was carried out for ETM images, and the preparation for accurate extraction of remote sensing image information was completed. By analyzing the correlation between each band and band combination of ETM image and the concentration of chlorophyll a in water body, the sensitive band combination was selected as the variable, and the stepwise regression analysis method was used. To explore the quantitative relationship between these variables and the concentration of chlorophyll a, a statistical model with R2 of 0.864 and RMSE of 0.957 was established. In order to further improve the inversion accuracy, a three-layer BP neural network model is established by comparing the training results of different structures using artificial neural network technology, in which the 1-4 band reflectivity of ETM image is used as the input node. The measured value of chlorophyll a concentration is used as the output node, and the hidden layer contains 8 nodes, and the R2 of the model is 0.956% RMSE (0.856 6). The results show that the BP neural network model has better fitting effect and provides a reliable basis and method for rapid and accurate monitoring of chlorophyll a concentration in water using remote sensing technology. The statistical model was used to invert the chlorophyll a concentration distribution from 2007 to 2010, which was used to study the spatial distribution characteristics and temporal variation of chlorophyll a concentration in Bohai Bay. It is found that the distribution of chlorophyll a concentration in the Bohai Bay is slightly higher in the south than in the north, and higher in the nearshore than in the far shore, and decreases with the increase of offshore distance. The annual and seasonal differences of chlorophyll a concentration were not obvious. Due to the influence of precipitation and water temperature, the concentration of chlorophyll a in spring and summer was generally higher than that in autumn.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：X87;X834

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