Evolutions of 30-Year Spatio-Temporal Distribution and Influencing Factors of Suaeda salsa in Bohai Bay, China

Abstract

Suaeda salsa (L.) Pall. (S. salsa) acts as a pioneer species in coastal wetlands due to its high salt tolerance. It has significant biodiversity maintenance, socioeconomic values (e.g., tourism) due to its vibrant color, and carbon sequestration (blue carbon). Bohai bay region, the mainly distributed area of S. salsa, is an economic intensive region with the largest economic aggregate and population in northern China. The coastal wetland is one of the most vulnerable ecosystems with the urbanization and economic developments. S. salsa in Bohai Bay has been changed significantly due to several threats to its habitat in past decades. In this paper, we analyzed all available archived Landsat TM/ETM+/OLI images of the Bohai Bay region by using a decision tree algorithm method based on the Google Earth Engine (GEE) platform to generate annual maps of S. salsa from 1990 to 2020 at a 30-m spatial resolution. The temporal-spatial dynamic changes in S. salsa were studied by landscape metric analysis. The influencing factors of S. salsa changes were analyzed based on principal component analysis (PCA) and a logistic regression model (LRM). The results showed that S. salsa was mainly distributed in three regions: the Liao River Delta (Liaoning Province), Yellow River Delta (Shandong Province), and Hai River Estuary (Hebei Province, Tianjin). During the past 31 years, the total area of S. salsa has dramatically decreased from 692.93 km2 to 51.04 km2, which means that 92.63% of the area of S. salsa in the Bohai Bay region was lost. In the 641.89 km2 area of S. salsa that was lost, 348.80 km2 of this area was converted to other anthropic land use categories, while 293.09 km2 was degraded to bare land. The landscape fragmentation of S. salsa has gradually intensified since 1990. National Nature Reserves have played an important role in the restoration of suitable S. salsa habitats. The analysis results for the natural influencing factors indicated that precipitation, temperature, elevation, and distance to the coastline were considered to be the major influencing factors for S. salsa changes. The results are valuable for monitoring the dynamic changes of S. salsa and can be used as effective factors for the restoration of S. salsa in coastal wetlands.

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China

“…The built-up land, forest land, cropland/grassland, water bodies, tidal flats, and bare land are distinguished, and misclassifications are rare. The OA and Kappa coefficient of the traditional RF classification algorithm using only the reflectance bands, support vector machine classification, and minimum distance classification [ [51] , [52] , [53] , [54] ] are shown in Table 4 and Table 5 , respectively.…”

Mapping Coastal Wetlands and Their Dynamics in the Yellow River Delta over Last Three Decades: Based on a Spectral Endmember Space

“…Additionally, the MTS dataset indicated approximately 1574.03 ha more P. australis than our result for 2020, though the spatial distribution of S. salsa was generally consistent in both datasets. These discrepancies could be attributed to variations in remote sensing imagery, resolutions, and classification variables between the two datasets [41].…”

“…Previous studies primarily focused on fine-classifying coastal wetlands based on differences in band spectral indices [47]. For instance, Yin et al mapped S. salsa from 1990 to 2020 in the Bohai Bay using spectral indices and a decision tree algorithm applied to Landsat images [41]. Zhang et al mapped typical salt marsh species in the YRD utilizing temporal-spatial-spectral multidimensional features [28].…”

Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation

“…In the sparse area, the species density and biomass are relatively high. The diversity index of the mixed area was slightly higher than that of a dense area, mainly because Suaeda salsa played a positive role in soil remediation [43]. Furthermore, the species richness, species evenness, and diversity index of Spartina alterniflora area are further discussed.…”