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

Abstract

The propagation of the invasive Spartina alterniflora (S. alterniflora) has seriously affected the health of coastal wetland ecosystems in China and thus requires an urgent response. In this article, we construct a feature vector set containing phenological and other time-series features based on the google earth engine platform by combining dense time-series images from the sentinel-1 and sentinel-2 satellites. We obtained the dataset of the annual distribution of S. alterniflora in the Yellow river delta from 2016 to 2021 by developing an object-oriented random forest classification model. The results show that S. alterniflora 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 S. alterniflora (multi-year average overall accuracy: 95.38%; user accuracy: 95.01%; producer accuracy: and 95.17%). Our results suggest that from 2016 to 2021, the growth rate of the area occupied by S. alterniflora was 2.17 km2 per year, and a new patch of the S. alterniflora appeared in the south of the study area in 2018. The article here provides scientific data to support the monitoring and control of the invasive S. alterniflora at a large scale.