【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域得到廣泛應用,但是較長的重訪周期以及云的影響導致實際可用的數(shù)據(jù)較少,極大限制了其在時序分析方面的應用。反之,MODIS數(shù)據(jù)的高時間分辨率更適用于時序分析,但MODIS250米～1000米的空間分辨率具有較少的空間細節(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行時序變化研究。 STARFM(Spatial and Temporal Adaptive Reflectance Fusion Model)是目前應用較多、精度較高、基于反射率數(shù)據(jù)的時空融合模型之一。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、短時間內(nèi)的漸變性特征,引入時間權重系數(shù),提出了針對GVI數(shù)據(jù)的時空融合模型,提高了融合影像質(zhì)量。 本研究的主要貢獻包括： (1)將STARFM中移動窗口、分類數(shù)、影像不確定性值、距離權重常數(shù)設置為一系列不同值,利用Landsat TM與MODIS的纓帽變換分量數(shù)據(jù)獲取相應融合影像,并與實際獲取的Landsat TM纓帽變換分量影像對比,結果顯示：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,而距離權重常數(shù)的變化基本不會對融合結果質(zhì)量造成影響,當MODIS與TM影像不確定性值為非0值時,對結果影響較小。所以原算法中參數(shù)值的調(diào)整,對結果會產(chǎn)生一定的影響,但對融合結果質(zhì)量的改善程度有限。 (2)本文以2007年的實際獲取Landsat TM數(shù)據(jù)及相應時間的MODIS數(shù)據(jù)為例,探討了輸入影像獲取時間對融合結果的影響：1)融合影像時間與輸入影像獲取時間相差時間越長,精度越低；2)輸入影像中植被處于生長高峰期時,綠度植被指數(shù)融合影像精度相對較高,但隨時間推移,植被生長狀況發(fā)生顯著變化時,精度會明顯降低。 (3)本文在原算法基礎上假設綠度植被指數(shù)在短期內(nèi)呈均勻變化,提出針對GVI的GSTARFM(GVI STARFM)。GSTARFM基于兩個時刻的輸入影像,在相似像元選取上采用兩個時刻6個纓帽分量,引入時間權重系數(shù),使GVI融合結果得到提高。 (4)GVI時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)時間與實地調(diào)查結果相符,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.
【學位授予單位】：蘭州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP202;P237

【相似文獻】

相關期刊論文 前1條

1 孫潔娣;靳世久;孫健;;基于多地震波傳感器數(shù)據(jù)融合的管道安全監(jiān)測預警系統(tǒng)[J];石油學報;2009年03期

相關碩士學位論文 前3條

1 張鵬;基于Landsat TM與MODIS纓帽變換分量的時空數(shù)據(jù)融合方法研究[D];蘭州大學;2014年

2 田林;海水透明度的衛(wèi)星遙感反演及其多傳感器融合方法[D];中國海洋大學;2013年

3 馬國勝;基于多傳感器融合技術的瓦斯監(jiān)控系統(tǒng)實現(xiàn)[D];武漢理工大學;2010年

，

本文編號：2383466