A method for creating annual land cover data based on Google Earth Engine: A case study of the Yellow River basin
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摘要: 研究黄河流域多年土地覆盖情况对科学推动黄河流域高质量发展有着重要的意义,而高频次高精度土地覆盖数据对于土地覆盖监测等至关重要。该文以多年稳定不变区域的几何中心作为样本点,快速选取了一套可用于逐年影像监督分类的样本点; 而后通过Google Earth Engine(GEE)对黄河流域2000—2020年间年均近千景Landsat影像进行无云筛选和逐年拼接操作,得到黄河流域逐年无云拼接影像; 再通过随机森林分类方法对无云影像进行监督分类,制作了黄河流域20 a逐年土地覆盖数据; 最后选择了2010年土地覆盖数据对比国内外知名逐年土地覆盖数据。结果表明: ①样本点选取方法合理可靠,样本点选取精度高于94.7%,满足监督分类样本精度要求; ②基于GEE平台制作的逐年土地覆盖数据总体精度为0.82±0.03,平均Kappa系数为0.82,分类精度、整体及局部分类结果均优于MCD12Q1数据集和ESA-CCI数据集; ③基于GEE平台制作逐年土地覆盖数据的方法一定程度上解决了大尺度土地覆盖数据频次与精度无法兼顾的问题。
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关键词:
- Google Earth Engine /
- 土地覆盖数据 /
- 黄河流域
Abstract: The study on many years’ land cover plays a crucial role in promoting the high-quality development of the Yellow River basin. Meanwhile, high-frequency and high-precision land cover data are vital for land cover monitoring. This study took the basin‘s geometric center that has been stable for many years to sample and quickly selected a set of sample points that can be used for annual image supervised classification. Then, cloudless images were screened out from nearly one thousand Landsat images on average of the Yellow River basin of each year from 2000 to 2020 and were spliced by year using Google Earth Engine. Then, the random forest classification method was used to conduct the supervised classification of the cloudless images, producing the annual land cover data of the Yellow River basin in the recent 20 years. Finally, the land cover data of 2010 of the basin were compared with well-known annual land cover data at home and abroad. The results are as follows. ① The selection method of sample points used in this study is reasonable and reliable, with a selection accuracy of more than 94.7%, meeting the requirements of sample accuracy for supervised classification. ② The overall accuracy of the annual land cover data created based on Google Earth Engine is 0.82±0.03, with an average Kappa coefficient of 0.82. The classification accuracy and the overall and local classification results are better than the MCD12Q1 and ESA-CCI datasets. ③ Using the method for creating annual land cover data using Google Earth Engine, the frequency and accuracy of large-scale land cover data can be considered at the same time to a certain extent.-
Key words:
- Google Earth Engine /
- land cover data /
- Yellow River basin
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