本文選題：積雪覆蓋 + 隱馬爾科夫隨機(jī)場�。� 參考：《華東師范大學(xué)》2016年博士論文

【摘要】：積雪是地表覆蓋的重要組成部分。季節(jié)性積雪的累積和消融不僅影響了整個(gè)區(qū)域的氣候和水資源平衡,而且能夠進(jìn)一步影響全球的能量平衡和氣候變化。因此,獲取準(zhǔn)確的積雪覆蓋信息、分析積雪覆蓋的時(shí)空動(dòng)態(tài)變化,以及進(jìn)行融雪徑流建模對(duì)于水資源管理和氣候變化研究具有非常重要的科學(xué)意義。MODIS積雪覆蓋產(chǎn)品被廣泛地應(yīng)用于區(qū)域性積雪覆蓋信息提取和水文建模分析。但是作為光學(xué)傳感器,MODIS受到了云層的嚴(yán)重影響,導(dǎo)致其積雪產(chǎn)品,特別是逐日積雪產(chǎn)品出現(xiàn)了大量的數(shù)據(jù)空缺。此外,MODIS積雪覆蓋產(chǎn)品在森林地區(qū)、地形復(fù)雜的山區(qū),以及積雪覆蓋較薄和暫時(shí)性積雪時(shí)期精度較低。季節(jié)性積雪覆蓋的時(shí)空動(dòng)態(tài)變化能夠揭示區(qū)域性氣候變化和水資源平衡。為了監(jiān)測(cè)和分析季節(jié)性積雪覆蓋的時(shí)空變化,之前的研究通常采用基于像元的統(tǒng)計(jì)方法,從時(shí)間序列積雪遙感影像中獲取積雪覆蓋的空間變化和季節(jié)性時(shí)長。但是這種基于像元的方法不能反映積雪場(Snowpack)之間的時(shí)空演化關(guān)系,同時(shí)也無法正確地獲取到暫時(shí)性積雪現(xiàn)象,會(huì)造成季節(jié)性時(shí)長信息偏差。融雪徑流模型(Snowmelt RunoffModel, SRM)是目前在模擬和預(yù)測(cè)融雪徑流方面應(yīng)用最廣泛的模型之一并且以衛(wèi)星影像上獲取的逐日積雪覆蓋作為其中的輸入?yún)?shù)。由于標(biāo)準(zhǔn)的MODIS積雪覆蓋產(chǎn)品受到了云層的嚴(yán)重影響,之前的研究通常采用積雪消融曲線重建的方法獲取逐日積雪覆蓋信息。目前尚缺乏不同積雪覆蓋參數(shù)對(duì)SRM模擬的徑流差異影響的相關(guān)研究。針對(duì)現(xiàn)有研究的不足,本論文基于隱馬爾科夫隨機(jī)場(Hidden Markov Random Field, HMRF)時(shí)空建模技術(shù)改進(jìn)了MODIS積雪覆蓋產(chǎn)品,提出了一種面向?qū)ο蟮姆e雪累積和消融過程時(shí)空分析方法,以及基于SRM模擬的融雪徑流結(jié)果分析了融雪季積雪覆蓋差異導(dǎo)致的模擬徑流差異。本研究的主要內(nèi)容以及結(jié)論如下：(1)基于HMRF時(shí)空建模技術(shù)改進(jìn)MODIS積雪覆蓋產(chǎn)品。本研究基于時(shí)間序列MODIS積雪覆蓋產(chǎn)品,通過時(shí)空建模的方法將光譜信息、時(shí)空背景信息和環(huán)境相關(guān)信息以最優(yōu)的組合方式融入HMRF分析框架,生成完全沒有數(shù)據(jù)空缺的逐日積雪覆蓋產(chǎn)品。改進(jìn)后的積雪覆蓋產(chǎn)品通過與美國Rio Grande流域2006-2007積雪季的33個(gè)SNOwpack TELemetry (SNOTEL)站點(diǎn)的實(shí)測(cè)觀測(cè)數(shù)據(jù)以及原始MODIS積雪覆蓋產(chǎn)品對(duì)比進(jìn)行精度評(píng)價(jià)。原始的Terra和Aqua MODIS合成積雪覆蓋由于受到云層遮擋,在整個(gè)積雪季有高達(dá)34.1%的數(shù)據(jù)空缺。對(duì)于原MODIS標(biāo)準(zhǔn)積雪產(chǎn)品中的數(shù)據(jù)空缺,本研究填補(bǔ)以后的積雪覆蓋總體精度為88.1%；對(duì)于MODIS標(biāo)準(zhǔn)積雪產(chǎn)品中無數(shù)據(jù)空缺的區(qū)域,改進(jìn)后的積雪覆蓋總體精度從原始的85.3%提高到88.6%,高于MODIS標(biāo)準(zhǔn)積雪產(chǎn)品3.3%。在HMRF分析框架中逐步加入時(shí)空背景信息和環(huán)境相關(guān)信息后,數(shù)據(jù)空缺的填補(bǔ)能力和積雪覆蓋產(chǎn)品的總體精度都逐漸提高。原始MODIS積雪覆蓋產(chǎn)品在積雪轉(zhuǎn)化時(shí)期和森林覆蓋地區(qū)具有相對(duì)較低的精度。結(jié)果表明,本研究基于HMRF的方法能夠在整個(gè)積雪轉(zhuǎn)化時(shí)期提高原始MODIS積雪覆蓋產(chǎn)品4.2%的精度,其中在3月積雪消融時(shí)期能夠提高5.8%。此外,原始MODIS積雪覆蓋產(chǎn)品在常綠森林和混合森林地區(qū)的精度也得到了明顯的提升。(2)提出了一種基于面向?qū)ο蟮姆e雪累積和消融過程時(shí)空分析框架。積雪場的累積和消融是一個(gè)復(fù)雜和動(dòng)態(tài)的地理過程。本研究將積雪場定義為一種時(shí)空?qǐng)鰧?duì)象(field-object),并首次提出了一種面向?qū)ο蟮姆e雪累積和消融過程時(shí)空分析框架。這個(gè)分析框架根據(jù)積雪場在時(shí)空由低到高的聚合等級(jí),依次分為了積雪區(qū)(Snow Zone)、積雪序歹(Snow Sequences)和積雪過程(Snow Processes)三個(gè)層次。積雪區(qū)是從積雪專題遙感影像中獲取得到的空間連續(xù)積雪像元的區(qū)域；積雪序列是指具有時(shí)空連續(xù)性的一系列積雪區(qū)的集合；積雪過程是具有時(shí)空相關(guān)性的一系列積雪序列的集合。本研究采用了面向?qū)ο蟮姆椒ńM織和存儲(chǔ)積雪場在這三個(gè)層次的專題屬性、空間屬性、時(shí)間屬性以及專題關(guān)系、空間關(guān)系和時(shí)間關(guān)系。本研究以美國Upper Rio Grande流域?yàn)閷?shí)例樣區(qū),分析了2006-2007積雪季積雪累積和消融的過程。結(jié)果表明本研究提出的面向?qū)ο蟮姆治龇椒ú粌H能夠表達(dá)每一個(gè)積雪場在其生命周期中累積和消融的時(shí)空過程,還能夠揭示積雪場內(nèi)部和積雪場之間的時(shí)空演化關(guān)系。(3)基于SRM模擬的融雪徑流結(jié)果分析了融雪季積雪覆蓋差異導(dǎo)致的模擬徑流差異。本研究分別采用基于HMRF時(shí)空建模技術(shù)改進(jìn)后的逐日積雪覆蓋和對(duì)原始MODIS積雪產(chǎn)品進(jìn)行積雪消融曲線重建擬合得到的逐日積雪覆蓋作為SRM的積雪覆蓋輸入?yún)?shù),對(duì)Rio Grande Headwater流域2007年融雪季進(jìn)行融雪徑流建模,并分析了積雪覆蓋差異導(dǎo)致的模擬徑流差異。兩個(gè)積雪覆蓋產(chǎn)品產(chǎn)生的積雪消融曲線在整個(gè)融雪期的總體變化趨勢(shì)較相似,其中利用HMRF時(shí)空建模技術(shù)改進(jìn)后的逐日積雪覆蓋可以捕捉到更多的積雪覆蓋時(shí)空變化細(xì)節(jié)�；趦蓚�(gè)積雪覆蓋產(chǎn)品模擬的徑流量與實(shí)測(cè)徑流量之間都具有很好的相關(guān)關(guān)系,其中利用HMRF方法改進(jìn)后的積雪覆蓋模擬得到的徑流精度明顯高于MODIS積雪覆蓋產(chǎn)品模擬的徑流精度。本研究結(jié)果表明,在融雪季節(jié)的不同時(shí)期,由積雪覆蓋差異導(dǎo)致的模擬徑流差異不同。在融雪季的前期,由于空氣溫度仍然較低,導(dǎo)致低溫條件下由積雪融化形成的徑流量也低,因此積雪覆蓋率的差異并不會(huì)對(duì)模擬的徑流造成明顯的差異；在融雪季的中后期,由于空氣溫度的回升,使得徑流量對(duì)于融雪貢獻(xiàn)徑流的敏感性增強(qiáng),因此積雪覆蓋率的差異能夠直接導(dǎo)致模擬徑流的差異。
[Abstract]:Snow is an important part of surface cover. The accumulation and ablation of seasonal snow not only affects the climate and water balance of the whole region, but also can further influence the global energy balance and climate change. Therefore, the accurate snow cover information is obtained, the spatial and temporal changes of snow cover are analyzed, and the snowmelt diameter is analyzed. Flow modeling has a very important scientific significance for water resource management and climate change research..MODIS snow covered products are widely used for regional snow cover information extraction and hydrological modeling analysis. But as optical sensors, MODIS is seriously affected by the cloud layer, leading to its snow products, especially day by day snow products. There are a large number of data vacancies. In addition, MODIS snow cover products in forest areas, complex terrain in mountainous areas, and low snow cover and temporary snow time. Seasonal snow cover time and space dynamic changes can reveal regional climate change and water balance. In order to monitor and analyze seasonal snow cover Spatiotemporal change, previous studies usually use a pixel based statistical method to obtain the spatial variation and seasonal length of snow cover from the snow remote sensing images of time series, but this pixel based method can not reflect the spatio-temporal evolution of the snow field (Snowpack), and can not obtain the temporary snow now correctly. The snow melt runoff model (Snowmelt RunoffModel, SRM) is one of the most widely used models for simulating and predicting snowmelt runoff and the daily snow cover obtained on satellite images as input parameters. The standard MODIS snow cover products are severely affected by the cloud layer. The previous study usually uses the method of snow ablation curve reconstruction to obtain daily snow cover information. There is still a lack of research on the influence of different snow cover parameters on the runoff difference of SRM simulation. In view of the shortage of existing research, this paper is based on the construction of Hidden Markov Random Field (HMRF). Model technology improved MODIS snow cover products, proposed a spatio-temporal analysis method of object oriented snow accumulation and ablation process, and the results of snowmelt runoff based on SRM simulation analysis of simulated runoff differences caused by snow cover difference in snowmelt season. The main contents of this study are as follows: (1) based on HMRF spatio-temporal modeling techniques In this study, MODIS snow covered products were improved. Based on the time series MODIS snow cover products, the spectral information, spatio-temporal background information and environmental related information were integrated into the HMRF analysis framework by the time space modeling method to generate a day by day snow covered product without data vacancy. The improved snow cover production was made. The accuracy is evaluated by comparison with measured observations of 33 SNOwpack TELemetry (SNOTEL) sites in the 2006-2007 snow season of the Rio Grande Valley in the United States and the original MODIS snow covered products. The original Terra and Aqua MODIS synthetic snow cover is covered by cloud layer and up to 34.1% of the data vacancy in the whole snowpack season. The total precision of snow cover in the original MODIS standard snow product is 88.1%. For the area with no data vacancy in MODIS standard snow products, the improved snow cover overall precision is increased from original 85.3% to 88.6%, which is higher than that of the MODIS standard snow product 3.3%. in the HMRF analysis framework. After the time and space background information and the environment related information, the filling ability of the data vacancy and the overall precision of the snow covered products have been improved gradually. The original MODIS snow covered products have relatively low precision in the period of snow conversion and the forest covered areas. The results show that the method based on HMRF can be used in the period of the whole snow transformation. The precision of 4.2% of the MODIS snow covered products at the beginning of the plateau, which can improve the 5.8%. in the period of the snow melting in March, is also improved. The precision of the original MODIS snow covered products in the evergreen forest and the mixed forest area has also been significantly improved. (2) a spatio-temporal analysis framework based on the object oriented snow accumulation and ablation process is proposed. The accumulation and ablation is a complex and dynamic geographical process. The snow field is defined as a spatio-temporal field object (field-object), and a spatio-temporal analysis framework for the accumulation and ablation process of an object oriented snow accumulation is proposed for the first time. This framework is divided into the accumulated snow field from low to high aggregation level in the time space. The snow area (Snow Zone), the snow sequence (Snow Sequences) and the snow process (Snow Processes) are three levels. The snow area is the area of the spatial continuous snow pixel obtained from the snow thematic remote sensing images; the snow sequence is a collection of a series of snow areas with space-time continuity; the snow process is a spatio-temporal correlation. This study uses an object-oriented method to organize and store the thematic attributes of the snow field at these three levels, spatial attributes, time attributes, and thematic relationships, spatial relationships and time relations. This study takes the Upper Rio Grande basin in the United States as an example area to analyze the accumulation and elimination of snow in the 2006-2007 snow season. The results show that the object-oriented analysis method proposed by this study not only can express the spatio-temporal process of each snow field accumulated and ablation in its life cycle, but also can reveal the spatio-temporal evolution relationship between the snow field and the snow field. (3) snow melting season based on the SRM simulated snowmelt runoff results in snow melting season In this study, the daily snow cover based on HMRF space-time modeling technology improved and the snow cover of the original MODIS snow products were used as the input parameters for the snow cover of SRM, and the snow melting season of the Rio Grande Headwater Valley in 2007 was snowmelted. Modeling of runoff, and analysis of simulated runoff differences caused by snow cover differences. The overall change trend of snow melting curve produced by two snow covered products is similar in the whole snowmelt period, and more details of spatio-temporal change of snow cover can be captured by the improved snow cover of HMRF spatio-temporal modeling technology. Based on two There is a good correlation between the simulated runoff of a snow covered product and the measured runoff, and the runoff precision obtained by the improved snow cover simulation using the HMRF method is significantly higher than that of the MODIS snow covered product. The results show that the snow cover difference in the different periods of the snowmelt season In the early period of the snowmelt season, because the air temperature is still low, the flow of snow melting under low temperature is also low, so the difference of snow cover will not cause significant difference to the simulated runoff; in the middle and late period of the snowmelt season, due to the rise of air temperature, the runoff is made. The sensitivity of snowmelt to runoff is enhanced, so the difference of snow cover can directly lead to the difference of simulated runoff.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P426.635

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