The successful launch of the Sentinel-2 constellation satellite, along with advanced cloud detection algorithms, has enabled the generation of continuous time series at high spatial and temporal resolutions, which is in turn expected to enable the classification of salt marsh vegetation over larger spatiotemporal scales. This study presents a critical comparison of vegetation index (VI) and curve fitting methods—two key factors for time series construction that potentially influence vegetation classification performance. To accomplish this objective, the stability of five different VI time series, namely Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Water-Adjusted Vegetation Index (WAVI), was compared empirically; the suitability between three curve fitting methods, namely Asymmetric Gaussian (AG), Double Logistic (DL), and Two-term Fourier (TF), and VI time series was measured using the coefficient of determination, and the salt marsh vegetation separability among different combinations of VI time series and curve fitting methods (i.e., VI time series-based curve fitting model) was quantified using overall the Jeffries–Matusita distance. Six common types of salt marsh vegetation from three typical coastal sites in China were used to validate these findings, which demonstrate: (1) the SAVI performed best in terms of time series stability, while the EVI exhibited relatively poor time series stability with conspicuous outliers induced by the sensitivity to omitted clouds and shadows; (2) the DL method commonly resulted in the most accurate classification of different salt marsh vegetation types, especially when combined with the EVI time series, followed by the TF method; and (3) the SAVI/NDVI-based DL/TF model demonstrated comparable efficiency for classifying salt marsh vegetation. Notably, the SAVI/NDVI-based DL model performed most strongly for high latitude regions with a continental climate, whilst the SAVI/NDVI-based TF model appears to be better suited to mid- to low latitude regions dominated by a monsoonal climate.

The South China Sea (SCS) is one of the most important fishery resource bases in the world. Marine fisheries, as a crucial component of regional food security and national revenue, raise wide concern about marine ecology, social-economic and political consequences at regional, national and local scales. The large-scale dynamic detection and analysis of fishing activity in the SCS is still unclear because of the accessibility of in-site data, finite automatic identification system (AIS) usage, complex geopolitics and poor additional data coverage. Nighttime light imagery (NTL) derived from low light imaging sensors and the popularity of light fishing in the SCS offers a unique way to unveil fishing activities and its dynamics. In this study, we proposed a set of algorithms for automatic detection of nighttime fishing activity and provided the first large-scale dynamic analysis of nighttime fishing activity in the SCS using monthly Visible Infrared Imaging Radiometer Suite (VIIRS) images between 2012 and 2019. The proposed method effectively minimized the spatio-temporal fluctuations in radiance values of background and their implications to ship detection by integrating high radiance gradient detection and local adaptive thresholding. Further, nighttime fishing activity trajectories were decomposed into trend and seasonal components by using Hilbert-Huang transformation (HHT) to accurately access general trends and the seasonality of nighttime fishing activity in the SCS. The typical subregions analysis, environmental driver analysis, correlation coefficient analysis and hot spot analysis were integrated to characterize the nighttime fishing activity. It appears that the nighttime fishing activity in the SCS exhibited spatio-temporal variability and heterogeneity and was shaped by policy and natural factors such as holidays, annual Chinese fishery moratoria in the Chinese Exclusive Economic Zone (EEZ) and seasonal tropical storm activity.

Water–land ecotones (WLEs) of lakes are key regulators of the carbon cycle and climate dynamics, playing a pivotal role in both terrestrial and aquatic ecosystems. Structural information on surface water, such as patch number and morphology, is essential for lake restoration and biodiversity conservation. However, such structural information remains limited for China’s major freshwater lakes. This study assessed the spatiotemporal variations in total area (TA), patch number (PN), and the structure characteristics of surface water bodies of different sizes in China’s three major freshwater lakes (WLEs) from 1990 to 2023, and examined the impacts of key driving factors. We found that TA in Dongting Lake and Poyang Lake experienced substantial losses, whereas Taihu Lake exhibited recent recoveries. PN trends in the three lakes showed relatively minor fluctuations, they declined over time. Moreover, changes due to reclamation, and the implementation of Yangtze River protection policies significantly influenced the surface water structure of the lakes’ WLEs. Our findings provide critical insights for policymakers and stakeholders seeking to address the growing challenges of ecosystem restoration, governance, and sustainable development in the WLEs of China’s major lakes.