MODIS陸表產(chǎn)品數(shù)據(jù)重建與時(shí)間序列分析
發(fā)布時(shí)間：2018-09-04 20:03
【摘要】：航天遙感經(jīng)過(guò)40多年的發(fā)展,累積了海量的遙感數(shù)據(jù)。云覆蓋嚴(yán)重影響了遙感對(duì)地觀測(cè)的數(shù)據(jù)質(zhì)量,使傳感器無(wú)法獲取有效的地表觀測(cè)數(shù)據(jù),導(dǎo)致遙感觀測(cè)數(shù)據(jù)產(chǎn)生空間不連續(xù),時(shí)間間隔不規(guī)律的現(xiàn)象,從而降低了遙感數(shù)據(jù)時(shí)序分析的應(yīng)用水平,限制了對(duì)遙感數(shù)據(jù)時(shí)間維度隱藏規(guī)律的認(rèn)知。如何對(duì)遙感缺失和低質(zhì)量數(shù)據(jù)進(jìn)行數(shù)據(jù)重建,及對(duì)重建數(shù)據(jù)進(jìn)行時(shí)序分析逐漸成為遙感應(yīng)用領(lǐng)域一個(gè)新的研究熱點(diǎn)。本文選擇MODIS陸表產(chǎn)品中時(shí)序變化特征有代表性的歸一化植被指數(shù)(Normalized Difference Vegetation Index,NDVI)和地表溫度(Land Surface Temperature,LST)作為研究對(duì)象進(jìn)行數(shù)據(jù)重建及時(shí)間序列分析。根據(jù)兩類數(shù)據(jù)的時(shí)空特征分別設(shè)計(jì)了分形插值算法進(jìn)行NDVI數(shù)據(jù)重建,以及基于逐步回歸模型的LST時(shí)序重建算法,實(shí)現(xiàn)提高數(shù)據(jù)時(shí)空連續(xù)性的目的。通過(guò)對(duì)重建數(shù)據(jù)進(jìn)行時(shí)序分析探求它們時(shí)間維度包含的信息。具體研究?jī)?nèi)容及研究成果包含以下幾方面:(1)對(duì)研究區(qū)的云覆蓋時(shí)空特征進(jìn)行分析,定量闡述數(shù)據(jù)重建的必要性。設(shè)計(jì)了月無(wú)云概率(cfP),月平均無(wú)云率(cfA),月內(nèi)80%無(wú)云期占比,月內(nèi)連續(xù)無(wú)云期眾數(shù)四個(gè)指標(biāo)對(duì)云覆蓋時(shí)空特征進(jìn)行分析。結(jié)果表明云覆蓋對(duì)整個(gè)研究區(qū)影響較大,cfP最大值為57.19%,最小值為18.95%,且時(shí)空分布存在差異;可以36°N為界將研究區(qū)分為南北兩部分,研究區(qū)北部較南部受云覆蓋影響更小;從時(shí)序上看3、4月份各指標(biāo)總體表現(xiàn)相對(duì)較好,該時(shí)段云對(duì)研究區(qū)影響相對(duì)較小。(2)對(duì)NDVI的空間平穩(wěn)性和分形特征進(jìn)行研究,確定NDVI數(shù)據(jù)重建方法。低海拔區(qū)NDVI通常具有空間平穩(wěn)性特征,但在四川西部、陜西南部和西藏東北的高海拔區(qū)非平穩(wěn)特征明顯(15%VC.),此外城市建成區(qū),河流等與耕地或林地混雜交錯(cuò)區(qū)域也表現(xiàn)為非平穩(wěn)特征;NDVI行(列)剖面線具有明顯的分形特征,可把NDVI行(列)看作分形集,對(duì)不同季節(jié)、地類的NDVI行(列)剖面線進(jìn)行抽樣計(jì)算發(fā)現(xiàn)盒維數(shù)基本處于1.30-1.60之間。(3)根據(jù)NDVI行(列)剖面線的分形特征設(shè)計(jì)了NDVI數(shù)據(jù)的分形插值重建算法。算法先以分組的方法確定初始點(diǎn)集,利用解析法確定縱向壓縮因子(id),并設(shè)計(jì)檢核點(diǎn)集C控制迭代函數(shù)系(IFS)生成吸引子的精度;精度分析發(fā)現(xiàn)分形插值的精度對(duì)NDVI空間缺失尺度的響應(yīng)規(guī)律不明顯,缺失尺度較小時(shí)與普通克里格法(OK)的插值精度相當(dāng),當(dāng)缺失尺度較大時(shí)分形插值的精度優(yōu)于OK和距離反比插值法(IDW);并且分形插值較空間插值的方法能保留更多的紋理細(xì)節(jié)特征。NDVI重建數(shù)據(jù)是LST數(shù)據(jù)重建的基礎(chǔ)數(shù)據(jù)。(4)在LST與高程、NDVI、經(jīng)度和緯度因子相關(guān)性分析的基礎(chǔ)上,設(shè)計(jì)了LST時(shí)序重建算法。重建算法利用后向剔除法進(jìn)行自變量因子篩選,并通過(guò)赤池信息量準(zhǔn)則對(duì)全回歸模型進(jìn)行壓縮篩選,對(duì)單因子模型進(jìn)行擴(kuò)張篩選來(lái)確定最優(yōu)回歸函數(shù)。重建數(shù)據(jù)的誤差較小,白天兩個(gè)時(shí)點(diǎn)71.8%的數(shù)據(jù),夜晚兩個(gè)時(shí)點(diǎn)78.2%的數(shù)據(jù)可控制在3℃以內(nèi),總體上90%以上數(shù)據(jù)誤差可控制在5℃以內(nèi)。利用氣象站點(diǎn)的實(shí)測(cè)數(shù)據(jù)進(jìn)行時(shí)點(diǎn)LST重建數(shù)據(jù)精度驗(yàn)證時(shí)需將站點(diǎn)實(shí)測(cè)數(shù)據(jù)進(jìn)行尺度擴(kuò)展,并提出了站點(diǎn)實(shí)測(cè)數(shù)據(jù)尺度擴(kuò)展的方法。(5)設(shè)計(jì)了云覆蓋LST修正模型。該方法利用低日照時(shí)數(shù)天數(shù)對(duì)LST影響的突變特征,以期內(nèi)低日照時(shí)數(shù)天數(shù)為判別條件對(duì)LST重建地溫進(jìn)行修正,該方法可提高云覆蓋區(qū)LST的估計(jì)精度。(6)設(shè)計(jì)了基于加窗DTW距離的貼近度模糊分類算法。算法首先利用樣點(diǎn)數(shù)據(jù)迭代計(jì)算得到各地類標(biāo)準(zhǔn)時(shí)間序列曲線,通過(guò)加窗處理提高DTW距離的計(jì)算效率及精度,結(jié)合貼近度模糊分類的方法對(duì)NDVI重建時(shí)序數(shù)據(jù)各像元進(jìn)行分類。整體分類精度較高,總體分類精度為83.8%,Kappa系數(shù)為0.77,該方法適用于無(wú)采樣數(shù)據(jù)年度NDVI時(shí)序數(shù)據(jù)植被信息提取。(7)對(duì)研究區(qū)內(nèi)不同高程和不同地類的LST重建數(shù)據(jù)進(jìn)行時(shí)序特征分析。發(fā)現(xiàn)不同高程的年平均LST呈平行分布的特點(diǎn),高程1-2Km的區(qū)域年均LST比高程小于1Km的區(qū)域平均低2.0℃,高程大于2Km的區(qū)域年均LST比高程1-2Km區(qū)域平均低2.6℃,研究時(shí)段內(nèi)不同高程區(qū)域的年均LST均呈緩慢增長(zhǎng)趨勢(shì);不同地類的年均地溫則呈水田旱地林地的特征。通過(guò)地類間地溫差異分析,認(rèn)為第7-11期和第24-26期地溫?cái)?shù)據(jù)可輔助用于水田與旱地分類。另外通過(guò)LST和NDVI的季節(jié)效應(yīng)分析,認(rèn)為季節(jié)效應(yīng)對(duì)LST的影響比NDVI的更顯著。(8)構(gòu)建LST和NDVI時(shí)序數(shù)據(jù)的自向量回歸模型,分析二者之間的時(shí)滯變化規(guī)律。LST和NDVI時(shí)序數(shù)據(jù)均為1階單整時(shí)序變量,對(duì)水田、旱地、林地分別構(gòu)建了VAR(7)、VAR(5)和VAR(2)模型,結(jié)合Granger因果關(guān)系分析認(rèn)為L(zhǎng)ST和NDVI的滯后變量對(duì)NDVI的解釋能力較強(qiáng);通過(guò)脈沖分析認(rèn)為L(zhǎng)ST受外部條件的某一沖擊后,會(huì)給NDVI帶來(lái)同向的沖擊,但不同地類的沖擊持續(xù)時(shí)間及強(qiáng)度不盡相同。利用本文數(shù)據(jù)重建算法實(shí)現(xiàn)了研究區(qū)2005-2014年的NDVI和LST兩種代表性數(shù)據(jù)的數(shù)據(jù)重建,實(shí)現(xiàn)了提高兩類數(shù)據(jù)時(shí)空連續(xù)性的目的。并充分利用NDVI時(shí)序變化特征進(jìn)行植被信息提取,整體分類精度較高;利用自向量回歸的方法對(duì)不同地類LST和NDVI時(shí)序數(shù)據(jù)進(jìn)行時(shí)滯關(guān)系分析,發(fā)現(xiàn)LST和NDVI的滯后變量對(duì)NDVI的影響顯著。
[Abstract]:Over the past 40 years, space remote sensing has accumulated a great deal of remote sensing data. Cloud cover has seriously affected the quality of remote sensing earth observation data, making it impossible for sensors to obtain effective surface observation data, resulting in spatial discontinuity and irregular time intervals of remote sensing observation data, thus reducing the response of time series analysis of remote sensing data. How to reconstruct the missing and low-quality remote sensing data and how to analyze the time series of the reconstructed data are becoming a new research hotspot in the field of remote sensing application. In this paper, we choose the normalized vegetation which has typical temporal variation characteristics in MODIS land surface products. The Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were used to reconstruct data and analyze time series. The research contents and achievements include the following aspects: (1) The spatial-temporal characteristics of cloud cover in the study area are analyzed, and the necessity of data reconstruction is explained quantitatively. Mean cloud-free rate (cfA), 80% of cloud-free period in a month and mode of continuous cloud-free period in a month were used to analyze the spatial and temporal characteristics of cloud cover. The northern part of the study area is less affected by cloud cover than the southern part; the overall performance of the indicators in March and April is relatively good, and the impact of cloud on the study area is relatively small. The non-stationary characteristics of the high altitude areas in southern Shaanxi and northeastern Tibet (15% VC.) are obvious, and the non-stationary characteristics of the urban built-up areas, rivers and other mixed interlaced areas with cultivated land or forest land are also obvious; the NDVI row (column) profile has obvious fractal characteristics, which can be regarded as a fractal set, and the NDVI row (column) profile of different seasons and terrain types can be regarded as a fractal set. (3) According to the fractal characteristics of NDVI row (column) profiles, a fractal interpolation reconstruction algorithm for NDVI data is designed. Firstly, the initial point set is determined by grouping method, the longitudinal compression factor (id) is determined by analytic method, and the checkpoint set C control iterative function system (IFS) is designed. Accuracy analysis shows that the fractal interpolation accuracy has no obvious response to the missing scale of NDVI space, and the interpolation accuracy of fractal interpolation is equivalent to that of ordinary Kriging method (OK) when the missing scale is small, and the accuracy of fractal interpolation is better than that of OK and inverse distance ratio interpolation (IDW) when the missing scale is large. (4) Based on the correlation analysis between LST and elevation, NDVI, longitude and latitude, a LST temporal reconstruction algorithm is designed. The reconstruction algorithm uses the backward elimination method to filter the independent variables and uses the red pool information criterion to complete the reconstruction. The regression model is compressed and screened, and the single factor model is expanded and screened to determine the optimal regression function. In order to validate the accuracy of LST reconstructed data, we need to scale up the measured data and propose a method to scale up the measured data. (5) A cloud-covered LST modified model is designed. The method can improve the accuracy of LST estimation in cloud coverage area. (6) A fuzzy classification algorithm based on windowed DTW distance is designed. Firstly, the standard time series curves of different classes are obtained by iteration of sample data, and the calculation efficiency and precision of DTW distance are improved by windowed processing. The overall classification accuracy is 83.8% and the Kappa coefficient is 0.77. This method is suitable for extracting vegetation information from NDVI time series data in the year without sampling data. (7) The temporal characteristics of LST reconstructed data from different elevations and different land types in the study area are analyzed. The annual average LST of 1-2km is 2.0 6550 Through the analysis of the difference of soil temperature between different land types, it is concluded that the 7-11 and 24-26 soil temperature data can be used to classify paddy field and dry land. The time series data of LST and NDVI are all one-order single-integer time series variables. VAR (7), VAR (5) and VAR (2) models are constructed for paddy field, dry land and forest land respectively. Combined with Granger causality analysis, the lagging variables of LST and NDVI have stronger explanatory power to NDVI. NDVI is impacted in the same direction, but the duration and intensity of impacts of different terrain types are not the same. The data reconstruction algorithm of this paper is used to reconstruct two representative data of NDVI and LST from 2005 to 2014 in the study area, and the purpose of improving the spatiotemporal continuity of the two types of data is realized. The results show that the overall classification accuracy is high, and the lag variables of LST and NDVI have a significant effect on NDVI.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P237
本文編號(hào)：2223188
[Abstract]:Over the past 40 years, space remote sensing has accumulated a great deal of remote sensing data. Cloud cover has seriously affected the quality of remote sensing earth observation data, making it impossible for sensors to obtain effective surface observation data, resulting in spatial discontinuity and irregular time intervals of remote sensing observation data, thus reducing the response of time series analysis of remote sensing data. How to reconstruct the missing and low-quality remote sensing data and how to analyze the time series of the reconstructed data are becoming a new research hotspot in the field of remote sensing application. In this paper, we choose the normalized vegetation which has typical temporal variation characteristics in MODIS land surface products. The Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were used to reconstruct data and analyze time series. The research contents and achievements include the following aspects: (1) The spatial-temporal characteristics of cloud cover in the study area are analyzed, and the necessity of data reconstruction is explained quantitatively. Mean cloud-free rate (cfA), 80% of cloud-free period in a month and mode of continuous cloud-free period in a month were used to analyze the spatial and temporal characteristics of cloud cover. The northern part of the study area is less affected by cloud cover than the southern part; the overall performance of the indicators in March and April is relatively good, and the impact of cloud on the study area is relatively small. The non-stationary characteristics of the high altitude areas in southern Shaanxi and northeastern Tibet (15% VC.) are obvious, and the non-stationary characteristics of the urban built-up areas, rivers and other mixed interlaced areas with cultivated land or forest land are also obvious; the NDVI row (column) profile has obvious fractal characteristics, which can be regarded as a fractal set, and the NDVI row (column) profile of different seasons and terrain types can be regarded as a fractal set. (3) According to the fractal characteristics of NDVI row (column) profiles, a fractal interpolation reconstruction algorithm for NDVI data is designed. Firstly, the initial point set is determined by grouping method, the longitudinal compression factor (id) is determined by analytic method, and the checkpoint set C control iterative function system (IFS) is designed. Accuracy analysis shows that the fractal interpolation accuracy has no obvious response to the missing scale of NDVI space, and the interpolation accuracy of fractal interpolation is equivalent to that of ordinary Kriging method (OK) when the missing scale is small, and the accuracy of fractal interpolation is better than that of OK and inverse distance ratio interpolation (IDW) when the missing scale is large. (4) Based on the correlation analysis between LST and elevation, NDVI, longitude and latitude, a LST temporal reconstruction algorithm is designed. The reconstruction algorithm uses the backward elimination method to filter the independent variables and uses the red pool information criterion to complete the reconstruction. The regression model is compressed and screened, and the single factor model is expanded and screened to determine the optimal regression function. In order to validate the accuracy of LST reconstructed data, we need to scale up the measured data and propose a method to scale up the measured data. (5) A cloud-covered LST modified model is designed. The method can improve the accuracy of LST estimation in cloud coverage area. (6) A fuzzy classification algorithm based on windowed DTW distance is designed. Firstly, the standard time series curves of different classes are obtained by iteration of sample data, and the calculation efficiency and precision of DTW distance are improved by windowed processing. The overall classification accuracy is 83.8% and the Kappa coefficient is 0.77. This method is suitable for extracting vegetation information from NDVI time series data in the year without sampling data. (7) The temporal characteristics of LST reconstructed data from different elevations and different land types in the study area are analyzed. The annual average LST of 1-2km is 2.0 6550 Through the analysis of the difference of soil temperature between different land types, it is concluded that the 7-11 and 24-26 soil temperature data can be used to classify paddy field and dry land. The time series data of LST and NDVI are all one-order single-integer time series variables. VAR (7), VAR (5) and VAR (2) models are constructed for paddy field, dry land and forest land respectively. Combined with Granger causality analysis, the lagging variables of LST and NDVI have stronger explanatory power to NDVI. NDVI is impacted in the same direction, but the duration and intensity of impacts of different terrain types are not the same. The data reconstruction algorithm of this paper is used to reconstruct two representative data of NDVI and LST from 2005 to 2014 in the study area, and the purpose of improving the spatiotemporal continuity of the two types of data is realized. The results show that the overall classification accuracy is high, and the lag variables of LST and NDVI have a significant effect on NDVI.
【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P237
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