The landscape grain effect reflects the spatial heterogeneity of a landscape and it is used as a research core of landscape ecology. The landscape grain effect can be used to not only explore spatiotemporal variation characteristics of a landscape pattern, but also to disclose variation laws of ecological structures and functions of landscapes. In this study, the sensitivity of landscape pattern indexes to grain sizes 50–1000 m was studied based on landscape data in Yancheng Coastal Wetland acquired in 1991, 2000, 2008, and 2017. Response of the grain effect to landscape changes was analyzed and an optimal grain size for analysis in the study area was determined. Results indicated that: (1) among 27 indexes (12 in a class level and 15 in a landscape level), eight indexes were highly sensitive to grains, ten indexes presented moderate sensitivity, eight indexes presented low sensitivity, and one was unresponsive. It was shown that the area-margin index and the shape index were more sensitive to the different grain sizes. The aggregation index had some differences in the grain size change, and the diversity index had a low response degree to the grain size. (2) Landscape indexes showed six different responses to different grains, including slow reduced response, fast reduced and then slow reduced response, monotonically increased response, fluctuating reduced response, up-down responses, and stable response, which indicated that the landscape index was closely related to the spatial grain. (3) From 1991 to 2017, variation curves of the landscape grain size of different landscape types could be divided into four types: fluctuation rising type, fluctuation type, monotonous decreasing type, and monotonous rising type. Different grain size curves had different interpretations of landscape changes, but in general, Yancheng Coastal Wetland’s landscape tended to be fragmented and complicated, internal connectivity was weakened, and dominant landscape area was reduced. Natural wetlands were more sensitive to grain size effects than artificial wetlands. (4) The landscape index at the 50 m grain size had a strong response to different grain size changes, and the loss of landscape information was the smallest. Therefore, it was determined that the optimal landscape grain size in the study area was 50 m.

Aquaculture has enormous potential for ensuring global food security and has experienced rapid growth globally. Thus, the accurate monitoring and mapping of coastal aquaculture ponds is necessary for the sustainable development and efficient management of the aquaculture industry. Here, we developed a map of coastal aquaculture ponds in China using Google Earth Engine (GEE) and the ArcGIS platform, Sentinel-1 SAR image data for 2020, the Sentinel-1 Dual-Polarized Water Index (SDWI), and water frequency obtained by identifying the special object features of aquaculture ponds and postprocessing interpretation. Our map had an overall accuracy of 93%, and we found that the coastal aquaculture pond area in China reached 6937 km² in 2020. The aquaculture pond area was highest in Shandong, Guangdong, and Jiangsu Provinces, and at the city level, Dongying, Binzhou, Tangshan, and Dalian had the most aquaculture pond area. Aquaculture ponds had spatial heterogeneity; the aquaculture pond area in north China was larger than in south China and seaside areas had more pond area than inland regions. In addition, aquaculture ponds were concentrated near river estuaries, coastal plains, and gulfs, and were most dense in the Huang-Huai-Hai Plain and Pearl River Delta. We showed that GEE cloud processing and ArcGIS local processing could facilitate the classification of coastal aquaculture ponds, which can be used to inform and improve decision-making for the spatial optimization and intelligent monitoring of coastal aquaculture, with certain potential for spatial migration.

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.

By evaluating the stability of coastal wetland ecosystems, health conditions of regional ecosystems can be revealed and the sustainable development of coastal wetlands can be promoted. Coastal wetlands have been scarcely involved in present ecosystem stability evaluation studies, these being performed with relatively simple evaluation data sources. Therefore, in this research, a comprehensive and representative ecosystem stability evaluation index system was constructed by using the pressure-state-response model and multi-source datasets from perspectives of internal and external environmental changes of the Yancheng coastal wetlands, Jiangsu, China. The analysis results indicated that: (1) The ecosystem stability of the Yancheng coastal wetlands was at an early warning stage, and all segments except the Binhai segment (relatively stable) were in an early warning state. (2) In the criterion layer, the Dafeng District and the whole Yancheng District were faced with the highest pressure, followed by the Dongtai, Xiangshui and Binhai segments, successively. The Sheyang segment reached the highest state level, followed by the Binhai, Xiangshui and Dafeng segments in succession. (3) In the factor layer, the whole Yancheng District was faced with high resource and socioeconomic double pressures, with a poor water quality state and relatively low environmental pressure; favorable soil, biological and landscape states; and positive response to wetland protection. Various factors varied from county to county. (4) In the index layer, the ecosystem stability of the Yancheng coastal wetlands was significantly influenced by the invasion of alien species, change rate of natural wetland area (D32), change rate of artificial wetland area, increment of aquafarm area, intensity of fertilizer application and coverage of dominant vegetations. The novel significance of this research lies in enriching global coastal wetlands ecosystem stability evaluation investigations by providing a typical case study.

Ecosystem services (ESs) is a term used to describe the foundations of the well-being of human society, and several relevant studies have been carried out in this area. However, given the fact that the complex trade-offs/synergy relationships of ESs are a challenging area, studies on matching mechanisms for ES supply and demand are still rare. In this study, using the InVEST model, ArcGIS, and other professional tools, we first mapped and quantitatively evaluated the supply and demand of five ES types (water yield, soil conservation, carbon retention, food supply, and leisure and entertainment) in Hangzhou, China, based on land use, meteorology, soil, and socio-economic data. Then, we analyzed the matching characteristics between the supply and demand of these ESs and analyzed the complex trade-offs and synergy between the supply and demand of ESs and factors affecting ESs. The results of this analysis indicate that although the ES supply and demand of carbon retention tended to be out of balance (supply was less than demand), the supply and demand of the other four ES types (i.e., water yield, soil conservation, food supply, and leisure and entertainment) were in balance (supply exceeded demand). Finally, the spatial heterogeneity of the supply and demand of ESs in Hangzhou was significant, especially in urban areas in the northeast and mountainous areas in the southwest. The supply of ESs was based on trade-offs, whereas the demand of ESs was based on synergy. Our results further show that the supply and demand of ESs in the urban area in Hangzhou were out of balance, whereas the supply and demand of ESs in the western region were coordinated. Therefore, the linkage of ES flows between this urban area and the western region should be strengthened. This innovative study could provide useful information for regional land use planning and environmental protection.