Spatiotemporal Pattern of Invasive Pedicularis 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.

Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping

“…This result may suggest that the number of spectral bands is important, as WorldView-2 also has eight spectral bands, the same as PlanetScope, and thus achieves lower results than Sentinel-2, which also offers SWIR bands such as B11 (1610 nm) and B12 (2190 nm). Wang et al [50] concentrated on the classification of invasive Pedicularis spp., using multitemporal Sentinel-2 data, reporting F1-scores ranging from 0.92 to 0.97, while PlanetScope achieved F1-scores in the range of 0.71 to 0.82, which is a similar observation to our ones, in that PlanetScope offers several percentage points lower than Sentinel-2. The researchers also stressed that the number of features is more important than the spatial resolution.…”

A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD)

“…PUL has found applications across various domains, including bioinformatics [24][25][26], network link prediction [27], text classification [28], transportation [29], and image processing [30][31][32][33][34]. In the field of bioinformatics [24], the authors systematically reviewed various biological problems in 29 different bioinformatics applications based on PUL.…”

“…They stated that PUL, which stands out for its advantages of ease of parameter adjustment and controllable training time, also demonstrated the highest performance. In another study [31], Wang et al used a PUL method to accurately identify invasive plants using satellite images. In their study, carried out with three different datasets and utilizing decision trees as the basic classifier, they were able to identify the invasive plant Pedicularis with an accuracy exceeding 0.70 in all datasets using the PUL method.…”

Effects of Short-Term Nitrogen Additions on Biomass and Soil Phytochemical Cycling in Alpine Grasslands of Tianshan, China

“…Bayanbulak Grassland, located in the Tianshan Mountains, is the largest subalpine alpine grassland in China and the second-largest alpine grassland in China [ 28 ]. According to the observation data of the Chinese Academy of Sciences, the environmental nitrogen deposition of the grassland is about 8 kg·N·ha −1 ·yr −1 [ 29 ], which is slightly lower than the normal critical load of global grassland nitrogen deposition (10–20 kg·N·ha −1 ·yr −1 ) [ 30 ], but given its unique geographical location and climatic conditions, it is particularly important to study the changes in the Bayanbulak alpine grassland ecosystem in the context of nitrogen deposition to predict future changes in grassland structure and function.…”