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Remote Sens. 2016, 8(9), 741;

Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series

State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Resources Science and Technology, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
Author to whom correspondence should be addressed.
Academic Editors: Geoffrey M. Henebry, Forrest M. Hoffman, Jitendra Kumar, Xiaoyang Zhang, Josef Kellndorfer, Clement Atzberger and Prasad S. Thenkabail
Received: 17 June 2016 / Revised: 24 August 2016 / Accepted: 4 September 2016 / Published: 8 September 2016
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Accurate regional and global information on land cover and its changes over time is crucial for environmental monitoring, land management, and planning. In this study, we selected Fengning County, in China’s Hebei Province, as a case study area. Using satellite data, we generated fused normalized-difference vegetation index (NDVI) data with high spatial and temporal resolution by utilizing the STARFM algorithm to produce a fused GF-1 and MODIS NDVI dataset. We extracted seven phenological parameters (including the start, end, and length of the growing season, base value, mid-season date, maximum NDVI, seasonal NDVI amplitude) from a fused NDVI time-series after reconstruction using the TIMESAT software. We developed four classification scenarios based on different combinations of GF-1 spectral features, the fused NDVI time-series, and the phenological parameters. We then classified the land cover using a support vector machine and analyzed the classification accuracies. We found that the proposed method achieved satisfactory classification results, and that the combination of the fused NDVI data with the extracted phenological parameters significantly improved classification accuracy. The classification accuracy based on the composited GF-1 multi-spectral bands combined with the phenological parameters was the highest among the four scenarios, with an overall classification accuracy of 88.8% and a Kappa coefficient of 0.8714, which represent increases of 9.3 percentage points and 0.1073, respectively, compared with GF-1 spectral data alone. The producer’s and user’s accuracy for different land cover types improved, with a few exceptions, and cropland and broadleaf forest had the largest increase. View Full-Text
Keywords: land cover; classification; STARFM; NDVI; time-series; phenology; SVM land cover; classification; STARFM; NDVI; time-series; phenology; SVM

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Kong, F.; Li, X.; Wang, H.; Xie, D.; Li, X.; Bai, Y. Land Cover Classification Based on Fused Data from GF-1 and MODIS NDVI Time Series. Remote Sens. 2016, 8, 741.

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