High-Resolution Mapping Changes in the Invasion of Spartina Alterniflora in the Yellow River Delta

Abstract

The propagation of the invasive <i>Spartina alterniflora</i> (<i>S. alterniflora</i>) has seriously affected the health of coastal wetland ecosystems in China and thus requires an urgent response. In this research, we construct a feature vector set containing phenological and other time-series features based on the Google Earth Engine (GEE) platform by combining dense time-series images from the Sentinel-1 and Sentinel-2 satellites. We obtained the dataset of the annual distribution of <i>S. alterniflora</i> in the Yellow River Delta (YRD) from 2016 to 2021 by developing an object-oriented random forest classification model. The results show that <i>S. alterniflora</i> has different phenological features from other wetland plants that played an important role in its classification based on the images. A combination of multiple phenological and temporal features improved the classification accuracy of <i>S. alterniflora</i> (multi-year average OA: 95.38&#x0025;; UA: 95.01&#x0025;; PA: 95.17&#x0025;). Our results suggest that from 2016 to 2021, the growth rate of the area occupied by <i>S. alterniflora</i> was 2.17 km² per year, and a new patch of the <i>S. alterniflora</i> appeared in the south of the study area in 2018. The work here provides scientific data to support the monitoring and control of the invasive <i>S. alterniflora</i> at a large scale.