Assessing spatiotemporal characteristics of native and invasive species with multi‐temporal remote sensing images in the Yellow River Delta, China

Abstract

As the main invasive plant species in the Yellow River Delta, Spartina alterniflora (S. alterniflora) has been a great threat to local ecosystems, but there are few studies on the growth scale, habitat structural changes, and expansion pattern of S. alterniflora. In this study, the native and invasive species in the Yellow River Delta were examined for their spatiotemporal characteristics and succession patterns. First, multi‐temporal SENTINEL‐2 and LANDSAT‐8 images from 2018 were selected. A random forest algorithm was used to verify the image spectral band's significance and separability to determine the native and invasive species. Then, the spatiotemporal variation of habitat structure of the species was discussed in depth from the perspective of landscape ecology. Finally, the expansion direction and expansion mode of S. alterniflora were further analyzed. The main results obtained were as follows: (a) SENTINEL‐2 images with red‐edge bands had obvious advantages in vegetation community extraction as compared to LANDSAT‐8 images (SENTINEL‐2, OA = 82.86%, kappa coefficient = 0.79; LANDSAT‐8, OA = 78.77%, kappa coefficient = 0.74). (b) The expansion pattern of the S. alterniflora community became spatially continuous, more regularized, and aggregated over time. (c) The expansion in the north shore mainly faced toward the sea, and the south bank mainly faced toward the land, and this phenomenon was closely related to the sedimentation of the Yellow River Delta. Marginal and external expansion both occurred, but marginal expansion predominated. The results have important theoretical and scientific value for the environmental protection and sustainable development of the entire Yellow River Delta.

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

“…Due to the surface roughness and dielectric properties of wetland vegetation (i.e., wetness of soil and level of tide), SAR has an impressive capa bility for classifying coastal wetland vegetation [35,36]. Time-series analysis has also been widely used as a method of monitoring, and Sentinel-1 delivers a large amount of time-series information from which important phenological features can be obtained [23,37]. Therefore, timeseries analysis combined with remote sensing images is more effective than images of single stages for identifying the types of vegetation with distinct phenological features [38][39][40].…”

Synergetic Classification of Coastal Wetlands over the Yellow River Delta with GF-3 Full-Polarization SAR and Zhuhai-1 OHS Hyperspectral Remote Sensing

“…The total area of wetland in the YRD is approximately 700 km 2 , of which the area of natural wetland accounts for approximately 89%, mainly including shallow sea, tidal flat, river, and vegetation, and the area of human-made wetland accounts for approximately 11%, mainly including aquaculture ponds, salt pans, and farmland. Four dominant salt marsh plant species, namely, Phragmites australis, Tamarix chinensis, Spartina alterniflora, and Suaeda salsa, occupy the broad tidal flats of the YRD [20,37]. Suaeda salsa and Tamarix chinensis are the only halophytic plants in heavy saline soil and tidal flats.…”

“…The above two categories of data constitute sample data and validation data, which are used to establish classifiers and verify accuracy. Table 3 lists According to the investigation of wetlands in the YRD, the main wetland types in the wetland are Tamarix chinensis, Suaeda salsa, Phragmites australis, and invasive species Spartina alterniflora [7,37,41].…”

“…Previous studies have shown that multisensor remote sensing information fusion can improve the final quality of information extraction by relying on the existing sensor data without increasing the cost [23,[36][37][38][39]. Due to the variety and complexity of coastal wetland types, it is necessary to consider multisource data fusion to improve the accuracy of wetland classification [7,[40][41][42].…”

Genus-Level Mapping of Invasive Floating Aquatic Vegetation Using Sentinel-2 Satellite Remote Sensing

“…The Sentinel-2 sensors provide the opportunity to discriminate between multiple FAV at the species and genus level throughout the growing season because of their high revisit frequency and high spatial resolution along with additional bands (relative to Landsat) that are intrinsically linked to vegetation traits [20]. Sentinel-2 has been used to distinguish between multiple emergent aquatic vegetation species (e.g., Spartina alterniflora, Phragmites australis) but the potential to spectrally discriminate between different types of FAV has yet to be determined [21,22]. Although Sentinel-2 has previously been used to map invasive FAV, such as water hyacinth, most study sites in the literature focus on relatively homogenous environments and community types, or target only one invasive species [23][24][25].…”