Ecological environmental quality assessment of Chinese estuarine wetlands during 2000–2020 based on a remote sensing ecological index

Abstract

Coastal estuarine wetlands are important transition zones between rivers and oceans and are extremely rich in biodiversity. In recent years in China, large-scale reclamation and development of coastal cities have imposed serious pressures on coastal ecosystems. Thus, assessing the ecological quality of estuarine wetlands is extremely important for sustainable development. Our study focuses on four typical estuarine wetlands at the mouths of the Yangtze, Yellow, Liaohe and PearRivers. Their ecological quality between 2000 and 2020 was assessed using a remote sensing ecological index (RSEI), which was derived from several remote sensing indexes processed via the online Google Earth Engine platform. From 2000 to 2020, the RSEIs of coastal wetland increased from 0.42 to 0.63 in the Liao River estuary, 0.27 to 0.45 in the Pearl River estuary and from 0.47 to 0.54 in the Yangtze River estuary, and decreased from 0.56 to 0.49 in the Yellow River estuary. The spatial distribution in ecological environmental quality was significantly clustered. High-high clusters occurred mainly in areas of lush vegetation, while low-low clusters were mostly found in built-up areas or coastal zones. Aquacultural and built-up areas had negative impacts on the ecological environment, while vegetation cover had a positive influence. The quality of the ecological environment within these wetlands has gradually improved due to the environmental management policies of national and local governments. This approach to evaluating the ecological quality of estuarine wetlands using the RSEI and Google Earth Engine provides critical information to inform sustainable development policy.

The HANTS-fitted data in the vegetation growing season can improve RSEI accuracy in revealing the spatiotemporal patterns of ecological quality in Yuxi of China

“…Noises such as clouds, fog, atmospheric scattering, and ground obstructions cause the loss of continuity and integrity of image data in some areas during a specific period, seriously affecting the credibility of the RSEI ecological quality evaluation 35 . In previous studies, the construction of RSEI model was usually based on the best quality image data screened out in a year, or using interpolation method to optimize the data 19,33,66 , but this would ignore phase changes in RSEI evaluation accuracy. HANTS is a technique for reconstructing time series data using Fourier analysis.…”

The HANTS-fitted RSEI constructed in the vegetation growing season reveals the spatiotemporal patterns of ecological quality

“…The use of such data can severely affect the credibility of ecological quality assessment using Remote Sensing Ecological Index (RSEI). In previous studies, the construction of RSEI models typically relied on selecting the best quality image data for a given year or using interpolation methods to optimize the data, but this approach often ignored the temporal variations in building the RSEI 18 , 30 , 60 . This study employs the HANTS to fit ecological indices (NDVI, TCW, NDBSI, LST) for constructing RSEI.…”

“…In regions with distinct wet and dry seasons, the vegetation growing season is further subdivided into growing and non-growing seasons. Previous studies utilizing RSEI for ecological quality assessment have often included a substantial amount of non-growing season imagery to filter out high-quality image data, thereby neglecting the influence of seasonal windows on the construction 18,59,60 of the RSEI model. Contrary to the commonly held belief that www.nature.com/scientificreports/ higher vegetation greenness indicates better ecological quality, this study uncovers that during the non-growing season, RSEI values for forests, farmland, and impervious surfaces are all elevated compared to the growing season (refer to Fig.…”

Multi-Scale Remote Sensing Assessment of Ecological Environment Quality and Its Driving Factors in Watersheds: A Case Study of Huashan Creek Watershed in China

“…Regarding the other variables, there was significant spatial heterogeneity in the influencing factors in different regions and at different scales [73]. Li et al [34] suggest that human activities contribute to the improvement of eco-environmental quality by reducing the degree of landscape fragmentation and the intensity of land use.…”