Sandy lands classification using GF-1 time series NDVI data
DING Xiangyuan1, GAO Zhihai1, SUN Bin1, WU Junjun2, XUE Chuanping1, WANG Yan1
1. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; 2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Abstract：In this study, GF-1 16 m multispectral images were used as data source, the spectral characteristics of each type of sandy land and its change characteristics of time series NDVI were analyzed, the sandy lands were classified by the GF-1 image at a single time, and time series NDVI data were compared with each other separately; on such a basis, the classification accuracy was evaluated. The results showed that the accuracy was 73.34% and Kappa coefficient was 0.7 by only using single time original data in growing season; however, the accuracy was increased to 92.04% by joining the time series NDVI data, with Kappa coefficient raised to 0.87; the accuracy was 81.44% and Kappa coefficient was 0.77 by using the time series NDVI data combined with non-growing season data, thus improving the classification accuracy obviously. It is indicated that GF-1 time series NDVI data have a huge application potential in the sandy lands classification.
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2017, Vol. 29 
