%A Xianbao Yang,Wangfei Zhang,Bin Sun,Zhihai Gao,Yifu Li,Han Wang %T Recognition of Vegetation Types in Hulunbuir Sandy Land and Its Surrounding Areas based on GEE Cloud Platform and Sentinel-2 Time Series Data %0 Journal Article %D 2022 %J Remote Sensing Technology and Application %R 10.11873/j.issn.1004-0323.2022.4.0982 %P 982-992 %V 37 %N 4 %U {http://www.rsta.ac.cn/CN/abstract/article_3560.shtml} %8 2022-08-20 %X

Sand and its surrounding vegetation types play an important role in fixing dunes, preventing soil erosion and environmental management for sandy land. Identification of Sand and its surrounding vegetation types can objectively reflect the vegetation growth environment of sandy land and its surrounding areas, so as to provide a valuable reference for ecological restoration and the control policies formulating of sandy land. With huge amount of long-term earth observation data and powerful cloud computing capabilities, Google Earth Engine (GEE) cloud platform provides a convenient way for the identification of vegetation types in a large areas. In this study, based on the Sentinel-2 time series data of 2019 stored in the GEE cloud platform, the applied potentialities of GEE cloud platform in vegetation types identification was explored by combining the RF algorithm and vegetation phenology information in Hulunbuir sandy land and its surroundings. Results showed that: ① The spectral information of Sentinel-2 image and the texture information obtained from the near-infrared band have limited ability to identify the main vegetation types in the study area, but the phenological characteristics effectively make up for this shortcoming; ② Accuracy of the vegetation types identification method achieved by the RF algorithm and considering the phenological characteristics extracted from the long time series remote sensing data is 84.37% (with the Kappa coefficient of 0.8), which is 10.01% higher than that identification result acquired based on single-phase data; ③Phenological characteristics of the main vegetation types in the Hulunbuir sandy land and its surroundings show significant differences, which is helpful for the identification of the vegetation types, especially to improve the recognition accuracy of shrubs and grassland.The research shows that the use of Sentinel-2 data and GEE cloud platform to identify vegetation types in large areas such as sandy land has great potential and broad application prospects.