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GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

毛丽君 李明诗

毛丽君, 李明诗. GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20200633
引用本文: 毛丽君, 李明诗. GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20200633
MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633
Citation: MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633

GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

doi: 10.13203/j.whugis20200633
基金项目: 

中央高校基本科研业务费专项资金(LGYB201704);国家自然科学基金(31971577);江苏高校优势学科建设项目(PAPD)。

详细信息
    作者简介:

    毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform

Funds: 

The Fundamental Research Funds for the Central Universities (LGYB201704)

  • 摘要: 国家公园的土地覆盖分类对于掌握自然资源现状、查明存在的生态安全威胁并快速应对具有基础性数据支撑作用。本文基于谷歌地球引擎(Google Earth Engine,GEE)平台,结合哨兵(Sentinel)主被动遥感数据及其导出的光谱指数、纹理特征和地形特征,分别采用基于像元的随机森林(random forest,RF)算法和面向对象的简单非迭代聚类(simple non-iterative clustering,SNIC)+RF算法实现了钱江源国家公园异质性景观的土地覆盖(耕地、森林、草地、水体、人造地表和裸地)分类。经地面验证表明,在多种输入数据组合中,基于像元和面向对象方法分类获得的最高总体精度分别为92.37%和93.98%。合成孔径雷达(synthetic aperture radar,SAR)数据的纳入能够提高基于像元方法的分类精度,但在面向对象方法中未能体现精度提升效果。SNIC+RF算法生成的土地覆盖分类图完整性更好,所需特征数量较少,并且算法能够在GEE环境下快速执行,适合推广应用于国家公园管理实践中。
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  • 收稿日期:  2020-11-19
  • 网络出版日期:  2021-05-07

GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类

doi: 10.13203/j.whugis20200633
    基金项目:

    中央高校基本科研业务费专项资金(LGYB201704);国家自然科学基金(31971577);江苏高校优势学科建设项目(PAPD)。

    作者简介:

    毛丽君,博士生,讲师,主要从事遥感与GIS应用研究。111207@nfpc.edu.cn

摘要: 国家公园的土地覆盖分类对于掌握自然资源现状、查明存在的生态安全威胁并快速应对具有基础性数据支撑作用。本文基于谷歌地球引擎(Google Earth Engine,GEE)平台,结合哨兵(Sentinel)主被动遥感数据及其导出的光谱指数、纹理特征和地形特征,分别采用基于像元的随机森林(random forest,RF)算法和面向对象的简单非迭代聚类(simple non-iterative clustering,SNIC)+RF算法实现了钱江源国家公园异质性景观的土地覆盖(耕地、森林、草地、水体、人造地表和裸地)分类。经地面验证表明,在多种输入数据组合中,基于像元和面向对象方法分类获得的最高总体精度分别为92.37%和93.98%。合成孔径雷达(synthetic aperture radar,SAR)数据的纳入能够提高基于像元方法的分类精度,但在面向对象方法中未能体现精度提升效果。SNIC+RF算法生成的土地覆盖分类图完整性更好,所需特征数量较少,并且算法能够在GEE环境下快速执行,适合推广应用于国家公园管理实践中。

English Abstract

毛丽君, 李明诗. GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20200633
引用本文: 毛丽君, 李明诗. GEE环境下联合Sentinel主被动遥感数据的国家公园土地覆盖分类[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20200633
MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633
Citation: MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Data to Map Land Cover in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20200633
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