基于Landsat TM與MODIS纓帽變換分量的時(shí)空數(shù)據(jù)融合方法研究
發(fā)布時(shí)間：2018-12-17 02:11
【摘要】：Landsat TM數(shù)據(jù)的高空間分辨率及多光譜特性使得其在多領(lǐng)域得到廣泛應(yīng)用,但是較長的重訪周期以及云的影響導(dǎo)致實(shí)際可用的數(shù)據(jù)較少,極大限制了其在時(shí)序分析方面的應(yīng)用。反之,MODIS數(shù)據(jù)的高時(shí)間分辨率更適用于時(shí)序分析,但MODIS250米～1000米的空間分辨率具有較少的空間細(xì)節(jié)信息,更適用于空間大尺度范圍的研究。 而基于Landsat TM與MODIS數(shù)據(jù)的時(shí)空數(shù)據(jù)融合方法將TM數(shù)據(jù)的高空間分辨率與MODIS數(shù)據(jù)的高時(shí)間分辨率有效地融合在一起獲得新的數(shù)據(jù),以滿足在較高的空間分辨率上進(jìn)行時(shí)序變化研究。 STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)是目前應(yīng)用較多、精度較高、基于反射率數(shù)據(jù)的時(shí)空融合模型之一。本文通過調(diào)整原算法中參數(shù)值大小,完成了纓帽變換分量的數(shù)據(jù)融合。通過對比分析確定了數(shù)據(jù)融合最佳的參數(shù)組合。利用綠度植被指數(shù)的周期性、短時(shí)間內(nèi)的漸變性特征,引入時(shí)間權(quán)重系數(shù),提出了針對GVI數(shù)據(jù)的時(shí)空融合模型,提高了融合影像質(zhì)量。 本研究的主要貢獻(xiàn)包括： (1)將STARFM中移動窗口、分類數(shù)、影像不確定性值、距離權(quán)重常數(shù)設(shè)置為一系列不同值,利用Landsat TM與MODIS的纓帽變換分量數(shù)據(jù)獲取相應(yīng)融合影像,并與實(shí)際獲取的Landsat TM纓帽變換分量影像對比,結(jié)果顯示：移動窗口的增大以及分類數(shù)的調(diào)整有助于融合質(zhì)量的提高,而距離權(quán)重常數(shù)的變化基本不會對融合結(jié)果質(zhì)量造成影響,當(dāng)MODIS與TM影像不確定性值為非0值時(shí),對結(jié)果影響較小。所以原算法中參數(shù)值的調(diào)整,對結(jié)果會產(chǎn)生一定的影響,但對融合結(jié)果質(zhì)量的改善程度有限。 (2)本文以2007年的實(shí)際獲取Landsat TM數(shù)據(jù)及相應(yīng)時(shí)間的MODIS數(shù)據(jù)為例,探討了輸入影像獲取時(shí)間對融合結(jié)果的影響：1)融合影像時(shí)間與輸入影像獲取時(shí)間相差時(shí)間越長,精度越低；2)輸入影像中植被處于生長高峰期時(shí),綠度植被指數(shù)融合影像精度相對較高,但隨時(shí)間推移,植被生長狀況發(fā)生顯著變化時(shí),精度會明顯降低。 (3)本文在原算法基礎(chǔ)上假設(shè)綠度植被指數(shù)在短期內(nèi)呈均勻變化,提出針對GVI的GSTARFM(GVI STARFM)。GSTARFM基于兩個時(shí)刻的輸入影像,在相似像元選取上采用兩個時(shí)刻6個纓帽分量,引入時(shí)間權(quán)重系數(shù),使GVI融合結(jié)果得到提高。 (4)GVI時(shí)序融合影像能夠顯示植被生長的基本特征。植被生長、高峰以及衰落在趨勢變化曲線上表現(xiàn)明顯,且峰值大小排序以及出現(xiàn)時(shí)間與實(shí)地調(diào)查結(jié)果相符,表明GSTARFM的有效性。
[Abstract]:Landsat TM data is widely used in many fields due to its high spatial resolution and multispectral characteristics. However, the long period of revisiting and the influence of cloud result in less available data, which greatly limits its application in time series analysis. On the contrary, the high temporal resolution of MODIS data is more suitable for time series analysis, but the spatial resolution of MODIS250 meters to 1000 meters has less spatial detail information and is more suitable for the study of large scale spatial range. The spatio-temporal data fusion method based on Landsat TM and MODIS data can effectively fuse the high spatial resolution of TM data with the high temporal resolution of MODIS data to obtain new data to satisfy the time series change research on higher spatial resolution. STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) is one of the spatiotemporal fusion models based on reflectivity data. In this paper, the data fusion of the tasseled hat transform component is completed by adjusting the value of the parameters in the original algorithm. Through comparative analysis, the best parameter combination of data fusion is determined. Based on the periodicity of green vegetation index and the characteristics of gradual change in a short period of time, the temporal weight coefficient is introduced, and a spatio-temporal fusion model for GVI data is proposed, which improves the quality of fusion image. The main contributions of this study are as follows: (1) the moving window, the classification number, the image uncertainty and the distance weight constant in STARFM are set to a series of different values, and the corresponding fusion image is obtained by using the tassel transform component data of Landsat TM and MODIS. Compared with the actual Landsat TM tasseled cap transform image, the results show that the increase of moving window and the adjustment of classification number are helpful to the improvement of fusion quality, while the change of distance weight constant has little effect on the quality of fusion result. When the uncertain value of MODIS and TM images is non-zero, the effect on the results is small. Therefore, the adjustment of the parameters in the original algorithm will have a certain effect on the results, but the quality of the fusion results will be improved to a limited extent. (2) taking the actual Landsat TM data and the corresponding time MODIS data obtained in 2007 as an example, the paper discusses the influence of input image acquisition time on the fusion results: 1) the longer the difference between the fusion image time and the input image acquisition time, the longer the time difference between the fusion image acquisition time and the input image acquisition time; The lower the precision; 2) in the input image, the accuracy of green-degree vegetation index fusion image is relatively high when the vegetation is in the peak growth period, but with time, the precision will decrease obviously when the vegetation growth status changes significantly. (3) on the basis of the original algorithm, the green vegetation index is assumed to change uniformly in a short time, and the GSTARFM (GVI STARFM). GSTARFM for GVI is based on the input image of two times, and the two time and six tasseled cap components are used in the selection of similar pixels. The time weight coefficient is introduced to improve the fusion result of GVI. (4) GVI temporal fusion images can show the basic characteristics of vegetation growth. The vegetation growth, peak and fading are obvious on the trend curve, and the ranking of peak value and the time of occurrence are consistent with the results of field investigation, which shows the validity of GSTARFM.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP202;P237
本文編號：2383466
[Abstract]:Landsat TM data is widely used in many fields due to its high spatial resolution and multispectral characteristics. However, the long period of revisiting and the influence of cloud result in less available data, which greatly limits its application in time series analysis. On the contrary, the high temporal resolution of MODIS data is more suitable for time series analysis, but the spatial resolution of MODIS250 meters to 1000 meters has less spatial detail information and is more suitable for the study of large scale spatial range. The spatio-temporal data fusion method based on Landsat TM and MODIS data can effectively fuse the high spatial resolution of TM data with the high temporal resolution of MODIS data to obtain new data to satisfy the time series change research on higher spatial resolution. STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) is one of the spatiotemporal fusion models based on reflectivity data. In this paper, the data fusion of the tasseled hat transform component is completed by adjusting the value of the parameters in the original algorithm. Through comparative analysis, the best parameter combination of data fusion is determined. Based on the periodicity of green vegetation index and the characteristics of gradual change in a short period of time, the temporal weight coefficient is introduced, and a spatio-temporal fusion model for GVI data is proposed, which improves the quality of fusion image. The main contributions of this study are as follows: (1) the moving window, the classification number, the image uncertainty and the distance weight constant in STARFM are set to a series of different values, and the corresponding fusion image is obtained by using the tassel transform component data of Landsat TM and MODIS. Compared with the actual Landsat TM tasseled cap transform image, the results show that the increase of moving window and the adjustment of classification number are helpful to the improvement of fusion quality, while the change of distance weight constant has little effect on the quality of fusion result. When the uncertain value of MODIS and TM images is non-zero, the effect on the results is small. Therefore, the adjustment of the parameters in the original algorithm will have a certain effect on the results, but the quality of the fusion results will be improved to a limited extent. (2) taking the actual Landsat TM data and the corresponding time MODIS data obtained in 2007 as an example, the paper discusses the influence of input image acquisition time on the fusion results: 1) the longer the difference between the fusion image time and the input image acquisition time, the longer the time difference between the fusion image acquisition time and the input image acquisition time; The lower the precision; 2) in the input image, the accuracy of green-degree vegetation index fusion image is relatively high when the vegetation is in the peak growth period, but with time, the precision will decrease obviously when the vegetation growth status changes significantly. (3) on the basis of the original algorithm, the green vegetation index is assumed to change uniformly in a short time, and the GSTARFM (GVI STARFM). GSTARFM for GVI is based on the input image of two times, and the two time and six tasseled cap components are used in the selection of similar pixels. The time weight coefficient is introduced to improve the fusion result of GVI. (4) GVI temporal fusion images can show the basic characteristics of vegetation growth. The vegetation growth, peak and fading are obvious on the trend curve, and the ranking of peak value and the time of occurrence are consistent with the results of field investigation, which shows the validity of GSTARFM.
【學(xué)位授予單位】：蘭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP202;P237
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