五种典型遥感时空信息融合算法在湿地区域植被指数重建中的适用性比较

Comparing the applicability of five typical spatio-temporal information fusion algorithms based on remote sensing data in vegetation index reconstruction of wetland areas

为探讨不同遥感时空信息融合算法在水陆转换频繁、地物类型多样的湿地区域的适用性问题,该文以鄱阳湖样区为研究区,选取5种典型的时空信息融合算法(STARFM,ESTARFM,FSDAF,Fit-FC和STNLFFM)。根据不同时期地物差异状况,选取Landsat和MODIS遥感数据分别开展枯水期、平水期2个时段的归一化植被指数(normalized difference vegetation index,NDVI)影像融合实验,并在空间和光谱2个维度进行算法精度评估。结果表明,仅一对粗细分辨率影像输入时,FSDAF算法在枯水期的融合预测效果最好,总体误差为0.4335;STNLFFM算法在平水期的融合预测效果最好,总体误差为0.5147;同时应用枯水期、平水期2对粗细分辨率影像时,ESTARFM算法融合预测效果最好,总体误差为0.4670。不同时空信息融合算法在湿地地区的适用性与研究区域内水体面积的占比情况有关,STNLFFM算法在水体区域的融合预测效果最好。

This study aims to explore the applicability of various spatio-temporal information fusion algorithms based on remote sensing data to wetland areas characterized by frequent land-water conversion and diverse surface features.With the Poyang Lake sample area as the study area,this study examined five typical spatio-temporal information fusion algorithms(STARFM,ESTARFM,FSDAF,Fit-FC,and STNLFFM).Considering the differences in surface features among different periods,Landsat and MODIS remote sensing data were selected to conduct image fusion experiments for normalized difference vegetation indices(NDVIs)during low-and normal-water periods.Moreover,the accuracy of these algorithms was evaluated in spatial and spectral dimensions.The results of this study are as follows:①In the case of only one pair of coarse-and fine-resolution images as input,the FSDAF exhibited the optimal fusion prediction effect for the low-water period,with an overall error of 0.4335,whereas the STNLFFM manifested the optimal fusion prediction effect for the normal-water period,with an overall error of 0.5147;②In the case of two pairs of coarse-and fine-resolution images of low-and normal-water periods as input,the ESTARFM demonstrated the optimal fusion prediction effect,with an overall error of 0.4670;③The applicability of different algorithms to a wetland area is associated with the proportion of water bodies in the study area.The STNLFFM displayed the optimal fusion prediction effect for water bodies.