基于GEE和Sentinel-2时序数据的呼伦贝尔沙地及其周边植被类型识别研究

沙地及其周边植被对固定沙丘、防止水土流失和环境治理等方面具有重要作用，开展沙地及其周边植被类型识别研究对于客观地反映沙地及其周边的生态环境，进而为沙地恢复治理政策制定具有重要意义。GEE云平台丰富的长时间序列遥感数据和强大的云计算能力，为开展大区域植被类型识别提供了便捷。本研究基于GEE云平台存储的2019年Sentinel-2时序数据，采用RF算法开展呼伦贝尔沙地及其周边主要植被类型的空间判识研究，探索了GEE平台下顾及植被物候信息的植被类型识别效果。研究发现：①Sentinel-2影像的光谱信息和近红外波段的纹理信息对研究区的主要植被类型识别能力有限，而物候特征有效地弥补了原始光谱特征等对研究区不同植被类型区分能力的不足；②基于RF算法顾及物候特征的植被类型识别精度达到84.37%，Kappa系数为0.8，比单一时相数据的识别精度提高了10.01%；③呼伦贝尔沙地及其周边主要植被类型的物候特征差异明显，有助于不同类型植被的空间识别，特别是提高了灌草丛和草原的识别精度。研究表明利用Sentinel-2数据和GEE云平台对沙地等大区域植被类型的识别具有较大的潜力和广阔的应用前景。

Recognition of Vegetation Types in Hulunbuir Sandy Land and Its Surrounding Areas based on GEE Cloud Platform and Sentinel-2 Time Series Data

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.