收稿：2018-05-10，
录用：2018-8-17，
纸质出版：2019-09
移动端阅览
高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集，目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响，提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial and Temporal Fusion Model)，VISTFM采用模糊C聚类算法，对存量时序VI数据按土地利用类型划分为若干子类，从高低分辨率影像中随土地覆被类的变化规律提取子类，结合低分辨率影像提取的土地覆被类变化规律融合生成高时空分辨率的VI数据。用常用的Landsat和MODIS数据验证该算法，测试表明，VISTFM能够较好的捕获VI的中间变化过程，与常用的基于线性混合模型的模型和时空自适应反射率融合模型及其改进模型相比，利用VISTFM获得的植被指数数据集具有更高的时空分辨率。
Vegetation Index (VI) data with high spatial and temporal resolution are highly important in the use of remote sensing technology to observe the earth. Owing to technological limitations
obtaining VI data that exhibits both high spatial and temporal resolution is impossible. The arrival of multi-source remote sensing data spatial and temporal fusion model enables the retrieval of data with high spatial and temporal resolution. The commonly used model does not have the ability to capture the intermediate change process of pixel value
and a certain regularity occurs in the change of the VI of the landmark. This study proposes a new multi-source data fusion model (FCMVISTFM) based on Fuzzy C-Mean algorithm (FCM). Making pixels that group together have similar VI values and law of VI changes throughout the period. FCMVISTFM uses FCM to divide land-cover types into certain categories based on multi-phase VI data
which are defined as subclasses of each land-cover class. Each land-cover class average value is calculated by using the linear unmixed model
and subclass average value is calculated by the law between land-cover class and subclass. The VI data are fused by Landsat8 OLI and MODIS data based on the assumption that the average VI value of subclass S is the same as the VI value of pixels that belong to subclass S. Results show that FCMVISTFM can achieve relatively high accuracy. The average values of correlation coefficient (
R
)
RMSE
ERGAS
and variance are 0.9057
0.0674
1.9795
and 0.0045
respectively. With this level of accuracy
VI data can be used for vegetation research and observations of the earth. Commonly used line unmixed models
spatial and temporal adaptive reflectance fusion model (STARFM)
and its improved models have the problem of uncertain ability to capture the intermediate change process of VI. Thus
FCMVISTFM is more accurate compared with STDFA and ESTARFM. FCMVISTFM is developed for obtaining high spatial and temporal resolution VI data
making it easier to capture the intermediate changes of VI
which can be applied where high spatial and temporal resolution VI data are needed. In this study
the accuracy of the multi-source data fusion model can be increased by the following aspects. (1) The models based on line unmixed model
regardless of pixel classes or subset S average value calculations
are based on the entire image. However
in the STARFM model and improved models based on STARFM
the data fusion based on high and low resolutions pixels in a certain window
cloud cover only affects the calculation of pixels near its coverage area. The acquisition of multiphase cloudless images is especially difficult when the study area is large. In this case
the STARFM model and improved models based on STARFM have more application advantages. (2) All of the multi-source remote sensing data fusion models are based on certain assumptions
though these assumptions are based on a certain theoretical and have certain rationality. Errors are mainly caused by assumptions. A complete model assumption is proposed as the main way to improve the accuracy of the multi-source remote sensing data fusion model. (3) The time sequence laws of the VI of various landmark are not disordered
but a certain regularity
such as the specific laws of the crop’s VI
occurs. If the multi-source remote sensing data fusion model is established based on these laws
then it can also improve the accuracy of fusion results to some extent.
Bezdek J C, Ehrlich R and Full W. 1984. FCM: the fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2/3): 191–203
蔡学良, 崔远来. 2009. 基于异源多时相遥感数据提取灌区作物种植结构. 农业工程学报, 25(8): 124–130
Cai X L and Cui Y L. 2009. Crop planting structure extraction in irrigated areas from multi-sensor and multi-temporal remote sensing data. Transactions of the CSAE, 25(8): 124–130
Cerasoli S, Costa e Silva F and Silva J M N. 2016. Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland. International Journal of Biometeorology, 60(6): 813–825
Gao F, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207–2218
Gevaert C M and García-Haro F J. 2015. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sensing of Environment, 156: 34–44
Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G and White J C. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8): 1613–1627
Liao L M, Song J L, Wang J D, Xiao Z Q and Wang J. 2016. Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI. Remote Sensing, 8(6): 452
罗亚, 徐建华, 岳文泽. 2005. 基于遥感影像的植被指数研究方法述评. 生态科学, 24(1): 75–79
Luo Y, Xu J H and Yue W Z. 2005. Research on vegetation indices based on the remote sensing images. Ecologic Science, 24(1): 75–79
Ojeda-Magaña B, Quintanilla-Domínguez J, Ruelas R, Barba L G and Andina D. 2018. Improvement of the image sub-segmentation for identification and differentiation of atypical regions. International Journal of Pattern Recognition and Artificial Intelligence, 32(1): 1860011
邱庆伦, 赵鸿燕, 郭剑, 宋福, 吴玉珍. 2004. 遥感植被指数在农业生态环境监测中的应用. 农机化研究(6): 215–217
Qiu Q L, Zhao H Y, Guo J, Song F and Wu Y Z. 2004. The applying RS vegetation indexes in monitoring agricultural environment. Journal of Agricultural Mechanization Research(6): 215–217
Rao Y H, Zhu X L, Chen J and Wang J M. 2015. An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM + images. Remote Sensing, 7(6): 7865–7891
石月婵, 杨贵军, 李鑫川, 宋健, 王纪华, 王锦地. 2015. 融合多源遥感数据生成高时空分辨率数据的方法对比. 红外与毫米波学报, 34(1): 92–99
Shi Y C, Yang G J, Li X C, Song J, Wang J H and Wang J D. 2015. Intercomparison of the different fusion methods for generating high spatial-temporal resolution data. Journal of Infrared and Millimeter Waves, 34(1): 92–99
田庆久, 闵祥军. 1998. 植被指数研究进展. 地球科学进展, 13(4): 327–333
Tian Q J and Min X J. 1998. Advances in study on vegetation indices. Advance in Earth Sciences, 13(4): 327–333
邬明权, 牛铮, 王长耀. 2010. 利用遥感数据时空融合技术提取水稻种植面积. 农业工程学报, 26(S2): 48–52
Wu M Q, Niu Z and Wang C Y. 2010. Mapping paddy fields by using spatial and temporal remote sensing data fusion technology. Transactions of the CSAE, 26(S2): 48–52
邬明权, 王洁, 牛铮, 赵永清, 王长耀. 2012. 融合MODIS与Landsat数据生成高时间分辨率Landsat数据. 红外与毫米波学报, 31(1): 80–84
Wu M Q, Wang J, Niu Z, Zhao Y Q and Wang C Y. 2012. A model for spatial and temporal data fusion. Journal of Infrared and Millimeter Waves, 31(1): 80–84
Wu M Q, Wu C Y, Huang W J, Niu Z, Wang C Y, Li W and Hao P Y. 2016. An improved high spatial and temporal data fusion approach for combining Landsat and MODIS data to generate daily synthetic Landsat imagery. Information Fusion, 31: 14–25
谢登峰, 张锦水, 孙佩军, 潘耀忠, 云雅, 袁周米琪. 2016. 结合像元分解和STARFM模型的遥感数据融合. 遥感学报, 20(1): 62–72
Xie D F, Zhang J S, Sun P J, Pan Y Z, Yun Y and Yuan Z M Q. 2016. Remote sensing data fusion by combining STARFM and downscaling mixed pixel algorithm. Journal of Remote Sensing, 20(1): 62–72
Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610–2623
Zhukov B, Oertel D, Lanzl F and Reinhackel G. 1999. Unmixing-based multisensor multiresolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3): 1212–1226
相关作者
相关机构
京公网安备11010802024621
