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), 2023, 48(5): 756-764. 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), 2023, 48(5): 756-764. DOI: 10.13203/j.whugis20200633
MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Remote Sensing Data to Land Cover Classification in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University, 2023, 48(5): 756-764. DOI: 10.13203/j.whugis20200633
Citation: MAO Lijun, LI Mingshi. Integrating Sentinel Active and Passive Remote Sensing Data to Land Cover Classification in a National Park from GEE Platform[J]. Geomatics and Information Science of Wuhan University, 2023, 48(5): 756-764. 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

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

  • 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。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。地面实验表明,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。通过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,适用于国家公园管理实践。

     

    Abstract:
      Objectives  Land cover classification in national parks plays an important role in understanding the status of natural resources, identifying the existing ecological security threats and responding to them quickly.
      Methods  Two land cover classification methods are developed based on Google Earth Engine (GEE) platform by combining Sentinel active and passive remote sensing data, and spectral indices, textural features and topographic features derived from the data to classify land cover types in the Qianjiangyuan National Park (cropland, forest, grassland, water body, artificial surface and bare land). One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm.
      Results  The ground experimental results show that the highest overall classification accuracies of the pixel-based method and the object-oriented method are 92.37% and 93.98%, respectively. Furthermore, the integration of synthetic aperture radar (SAR) data can substantially improve the classification accuracy when using the pixel-based method, but there is no apparent escalating effect for the object-oriented method.
      Conclusions  Land cover classification map generated by SNIC+RF algorithm in GEE platform is more complete and the algorithm requires fewer features and runs quickly in GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.

     

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