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

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

Content Provider	MDPI
Author	Kong, Fanjie Li, Xiaobing Wang, Hong Xie, Dengfeng Li, Xiang Bai, Yunxiao
Abstract	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.
File Size	4251648
File Format	PDF
e-ISSN	20724292
DOI	10.3390/rs8090741
Journal	Remote Sensing
Issue Number	9
Volume Number	8
Language	English
Publisher Date	2016-09-08
Access Restriction	Open
Subject Keyword	land cover classification STARFM NDVI time-series phenology SVM
Content Type	Text
Resource Type	Article

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