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Open AccessArticle

A Comprehensive and Automated Fusion Method: The Enhanced Flexible Spatiotemporal DAta Fusion Model for Monitoring Dynamic Changes of Land Surface

by Chenlie Shi 1,2,3, Xuhong Wang 1,2,3,*, Meng Zhang 1,2,3, Xiujuan Liang 1,2, Linzhi Niu 1,2, Haiqing Han 1,2 and Xinming Zhu 4
1
College of Urban and Environmental Science, Northwest University, Xi’an 710127, China
2
Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
3
Shaanxi Key Laboratory of Ecology and Environment of River Wetland, Weinan Normal University, Weinan 714099, China
4
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3693; https://doi.org/10.3390/app9183693
Received: 30 July 2019 / Revised: 3 September 2019 / Accepted: 3 September 2019 / Published: 5 September 2019
(This article belongs to the Special Issue Remote Sensing and Geoscience Information Systems in Applied Sciences)
Spatiotemporal fusion methods provide an effective way to generate both high temporal and high spatial resolution data for monitoring dynamic changes of land surface. But existing fusion methods face two main challenges of monitoring the abrupt change events and accurately preserving the spatial details of objects. The Flexible Spatiotemporal DAta Fusion method (FSDAF) was proposed, which can monitor the abrupt change events, but its predicted images lacked intra-class variability and spatial details. To overcome the above limitations, this study proposed a comprehensive and automated fusion method, the Enhanced FSDAF (EFSDAF) method and tested it for Landsat–MODIS image fusion. Compared with FSDAF, the EFSDAF has the following strengths: (1) it considers the mixed pixels phenomenon of a Landsat image, and the predicted images by EFSDAF have more intra-class variability and spatial details; (2) it adjusts the differences between Landsat images and MODIS images; and (3) it improves the fusion accuracy in the abrupt change area by introducing a new residual index (RI). Vegetation phenology and flood events were selected to evaluate the performance of EFSDAF. Its performance was compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Spatial and Temporal Reflectance Unmixing Model (STRUM), and FSDAF. Results show that EFSDAF can monitor the changes of vegetation (gradual change) and flood (abrupt change), and the fusion images by EFSDAF are the best from both visual and quantitative evaluations. More importantly, EFSDAF can accurately generate the spatial details of the object and has strong robustness. Due to the above advantages of EFSDAF, it has great potential to monitor long-term dynamic changes of land surface. View Full-Text
Keywords: image fusion; high spatiotemporal resolution; EFSDAF; FSDAF; Landsat; MODIS image fusion; high spatiotemporal resolution; EFSDAF; FSDAF; Landsat; MODIS
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Shi, C.; Wang, X.; Zhang, M.; Liang, X.; Niu, L.; Han, H.; Zhu, X. A Comprehensive and Automated Fusion Method: The Enhanced Flexible Spatiotemporal DAta Fusion Model for Monitoring Dynamic Changes of Land Surface. Appl. Sci. 2019, 9, 3693.

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