發(fā)布時(shí)間：2018-12-16 23:55

【摘要】：積雪是地球表面覆蓋物的重要組成部分,其對(duì)全球氣候環(huán)境和人類生存條件都有著十分重要的影響,因此準(zhǔn)確的監(jiān)測(cè)和分析積雪的特征參數(shù)具有十分重要的意義。吉林省西部地區(qū)位于我國(guó)東北地區(qū)松嫩平原的中南部,當(dāng)?shù)囟韭L(zhǎng),且具有積雪覆蓋面積大、覆蓋時(shí)間長(zhǎng)的特點(diǎn),積雪對(duì)當(dāng)?shù)氐慕?jīng)濟(jì)發(fā)展和人們的日常生活產(chǎn)生了較為明顯的影響。此外,該地區(qū)土地鹽堿化程度較高,形成了其特有的下墊面特征。本文主要選取風(fēng)云三號(hào)B星微波成像儀(FY3B-MWRI)數(shù)據(jù)作為實(shí)驗(yàn)數(shù)據(jù),結(jié)合光譜遙感數(shù)據(jù),從積雪覆蓋和積雪深度兩個(gè)方面對(duì)吉林省西部地區(qū)的積雪情況進(jìn)行研究,主要工作及研究成果如下:(1)基于MWRI被動(dòng)微波遙感數(shù)據(jù)的吉林省西部地區(qū)積雪覆蓋判識(shí)算法研究本文通過(guò)對(duì)現(xiàn)有的基于被動(dòng)微波遙感數(shù)據(jù)的積雪覆蓋判識(shí)算法進(jìn)行分析比較,從中選取了具有代表性的Singh積雪決策樹(shù)判識(shí)算法、李曉靜積雪決策樹(shù)判識(shí)算法和潘金梅積雪決策樹(shù)判識(shí)算法,對(duì)2010年12月份和2012年至2016年每年1月份期間研究區(qū)域內(nèi)的積雪覆蓋情況進(jìn)行判識(shí),并將積雪覆蓋判識(shí)結(jié)果與MOD10A1積雪產(chǎn)品進(jìn)行對(duì)比。研究結(jié)果表明,在觀測(cè)期間內(nèi),采用的三種積雪覆蓋判識(shí)算法的判識(shí)精度均未能達(dá)到較高精度。通過(guò)對(duì)其誤差來(lái)源進(jìn)行分析,本文對(duì)原有判識(shí)算法的結(jié)構(gòu)和參數(shù)進(jìn)行了優(yōu)化,提出了一種更加適用于吉林省西部地區(qū)的積雪決策樹(shù)判識(shí)改進(jìn)方法。實(shí)驗(yàn)結(jié)果表明,針對(duì)研究區(qū)域,本文提出的改進(jìn)方法的積雪覆蓋判識(shí)精度達(dá)到了95.4%,明顯高于Singh積雪決策樹(shù)判識(shí)算法的78.3%、潘金梅積雪決策樹(shù)判識(shí)算法的76.7%和李曉靜積雪決策樹(shù)判識(shí)算法的89.6%。(2)基于MWRI被動(dòng)微波遙感數(shù)據(jù)的吉林省西部地區(qū)雪深反演算法研究本文采用FY3B業(yè)務(wù)化雪深反演算法和Chang雪深反演經(jīng)驗(yàn)算法,利用MWRI被動(dòng)微波遙感數(shù)據(jù),實(shí)現(xiàn)吉林省西部地區(qū)2010年12月份及2012年至2015年每年1月份的雪深反演,并結(jié)合土地分類數(shù)據(jù)對(duì)不同下墊面上的雪深均值進(jìn)行了統(tǒng)計(jì)和對(duì)比分析。為了進(jìn)一步提高雪深反演精度,本文將改進(jìn)方法得到的雪蓋判識(shí)結(jié)果與雪深反演算法相結(jié)合,并將雪蓋判識(shí)結(jié)果為無(wú)雪區(qū)域的雪深值進(jìn)行剔除,只統(tǒng)計(jì)雪蓋判識(shí)結(jié)果為有雪區(qū)域內(nèi)的雪深值,此時(shí)統(tǒng)計(jì)結(jié)果顯示四種下墊面上的積雪深度均值明顯增加。此外,研究結(jié)果表明:觀測(cè)地區(qū)的積雪深度在空間分布上呈現(xiàn)出一種自東南向西北逐步遞減的趨勢(shì)。
[Abstract]:Snow cover is an important part of the earth surface cover, which has a very important impact on the global climate environment and human living conditions. Therefore, it is of great significance to accurately monitor and analyze the snow cover characteristics. The western region of Jilin Province is located in the central and southern part of the Songnen Plain in Northeast China. The local winter is long and has the characteristics of large snow cover area and long covering time. Snow has an obvious impact on local economic development and people's daily life. In addition, the salinization degree of the land in this area is relatively high, forming its unique underlying surface characteristics. In this paper, the data of Fengyun No. 3 B Star Microwave Imager (FY3B-MWRI) are selected as experimental data, combined with spectral remote sensing data, the snow cover and snow depth in western Jilin Province are studied from two aspects: snow cover and snow depth. The main work and research results are as follows: (1) based on MWRI passive microwave remote sensing data, snow cover recognition algorithm based on passive microwave remote sensing data in western Jilin Province is studied. In this paper, the existing snow cover recognition algorithm based on passive microwave remote sensing data is studied. Method for analysis and comparison, The typical Singh snow decision tree recognition algorithm, Li Xiaojing snow decision tree recognition algorithm and Pan Jinmei snow decision tree recognition algorithm are selected. The snow cover in the study area between December 2010 and January 2012 to 2016 was identified, and the results were compared with MOD10A1 snow cover products. The results show that the accuracy of the three snow cover recognition algorithms can not reach higher accuracy during the observation period. Based on the analysis of the error sources, this paper optimizes the structure and parameters of the original recognition algorithm, and puts forward an improved method of snow decision tree recognition, which is more suitable for the western region of Jilin Province. The experimental results show that the accuracy of snow cover recognition of the improved method proposed in this paper is 95.4, which is obviously higher than that of Singh snow decision tree. 76.7% of Pan Jinmei snow decision tree and 89.6% of Li Xiaojing snow decision tree recognition algorithm. (2) based on MWRI passive microwave remote sensing data, the snow depth inversion algorithm in western Jilin Province is studied in this paper. FY3B operational snow depth inversion algorithm and Chang snow depth inversion empirical algorithm, Using MWRI passive microwave remote sensing data, the snow depth inversion in December 2010 and from 2012 to 2015 is realized in western Jilin Province, and the mean value of snow depth on different underlying surfaces is statistically analyzed and compared with land classification data. In order to further improve the accuracy of snow depth inversion, this paper combines the result of snow cover recognition obtained by the improved method with the snow depth inversion algorithm, and the result of snow cover recognition is eliminated from the snow depth value of the region without snow. Only the result of snow cover recognition is the snow depth in the region with snow, and the statistical results show that the mean value of snow depth on the four kinds of underlying surfaces is obviously increased. In addition, the results show that the depth of snow in the observed area is decreasing gradually from southeast to northwest.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P407

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