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

  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.