Spatiotemporal Pattern of Invasive <i>Pedicularis</i> in the Bayinbuluke Land, China, during 2019–2021: An Analysis Based on PlanetScope and Sentinel-2 Data

Abstract

The accurate identification and monitoring of invasive plants are of great significance to sustainable ecological development. The invasive Pedicularis poses a severe threat to native biodiversity, ecological security, socioeconomic development, and human health in the Bayinbuluke Grassland, China. It is imperative and useful to obtain a precise distribution map of Pedicularis for controlling its spread. This study used the positive and unlabeled learning (PUL) method to extract Pedicularis from the Bayinbuluke Grassland based on multi-period Sentinel-2 and PlanetScope remote sensing images. A change rate model for a single land cover type and a dynamic transfer matrix were constructed under GIS to reflect the spatiotemporal distribution of Pedicularis. The results reveal that (1) the PUL method accurately identifies Pedicularis in satellite images, achieving F1-scores above 0.70 and up to 0.94 across all three datasets: PlanetScope data (seven features), Sentinel-2 data (seven features), and Sentinel-2 data (thirteen features). (2) When comparing the three datasets, the number of features is more important than the spatial resolution in terms of use in the PUL method of Pedicularis extraction. Nevertheless, when compared with PlanetScope data, Sentinel-2 data demonstrated a higher level of accuracy in predicting the distribution of Pedicularis. (3) During the 2019–2021 growing season, the distribution area of Pedicularis decreased, and the distribution was mainly concentrated in the northeast and southeast of Bayinbuluke Swan Lake. The acquired spatiotemporal pattern of invasive Pedicularis could potentially be used to aid in controlling Pedicularis spread or elimination, and the methods proposed in this study could be adopted by the government as a low-cost strategy to identify priority areas in which to concentrate efforts to control and continue monitoring Pedicularis invasion.

Keywords

• <i>Pedicularis</i>
• positive and unlabeled learning
• dynamic variation
• change detection
• PlanetScope