Mangrove forests are important coastal ecosystems and are crucial for the equilibrium of the global carbon cycle. Monitoring and mapping of mangrove forests are essential for framing knowledge-based conservation policies and funding decisions by governments and managers. The purpose of this study was to monitor mangrove forest dynamics in the Quanzhou Bay Estuary Wetland Nature Reserve. To achieve this goal, we compared and analyzed the spectral discrimination among mangrove forests, mudflats and Spartina using multi-seasonal Landsat images from 1990, 1997, 2005, 2010, and 2017. We identified the spatio-temporal distribution of mangrove forests by combining an optimal segmentation scale model based on object-oriented classification, decision tree and visual interpretation. In addition, mangrove forest dynamics were determined by combining the annual land change area, centroid migration and overlay analysis. The results showed that there were advantages in the approaches used in this study for monitoring mangrove forests. From 1990 to 2017, the extent of mangrove forests increased by 2.48 km², which was mostly converted from mudflats and Spartina. Environmental threats including climate change and sea-level rise, aquaculture development and Spartina invasion, pose potential and direct threats to the existence and expansion of mangrove forests. However, the implementation of reforestation projects and Spartina control plays a substantial role in the expansion of mangrove forests. It has been demonstrated that conservation activities can be beneficial for the restoration and succession of mangrove forests. This study provides an example of how the application of an optimal segmentation scale model and multi-seasonal images to mangrove forest monitoring can facilitate government policies that ensure the effective protection of mangrove forests.

The formation of a canopy gap results in changes to the microenvironment which, in turn, affect litter decomposition and nutrient release. However, the mechanisms underlying these effects in differently sized gaps and non-gaps remain poorly understood. To address this gap in knowledge, we selected three large gaps (above 150 m²), three medium gaps (50–100 m²), three small gaps (30–50 m²), and three non-gaps with basically the same site conditions. We then used the litter bag method to investigate leaf and branch litter decomposition over a year in a Castanopsis kawakamii natural forest with the aim of characterizing the litter mass remaining and the nutrient release in canopy gaps and non-gaps. Our results revealed that the remaining litter mass of leaf and branch litter was lower in medium gaps compared to other gaps, and leaf litter decomposed faster than branch litter. Environmental factors were identified as the primary drivers of total carbon and nitrogen release during litter decomposition. Gap size (canopy openness), taxonomic Margalef index, the Brillouin index of soil microbes, soil total nitrogen content, soil pH value, and average air temperature were identified as the main factors driving carbon and nitrogen release from branch litter. In the late decomposition stage, the taxonomic Pielou index, soil total potassium content, soil water content, and average relative air humidity were the main drivers of nutrient release from branch litter. The soil water content and average relative air humidity were also found to be the main factors affecting the nutrient release from leaf litter throughout the different stages of decomposition. Overall, our study highlights the impact of canopy gaps on microenvironmental variation, taxonomic community diversity, and soil microbial functional diversity and how these factors ultimately influence litter decomposition and nutrient release. Our findings provide an important foundation for further research into soil nutrient cycling in subtropical natural forests.

The concentration of negative air ions (NAIs) is an important indicator of air quality. Here, we analyzed the distribution patterns of negative air ion (NAI) concentrations at different time scales using statistical methods; then described the contribution of meteorological factors of the different season to the concentration of NAIs using correlation analysis and regression analysis; and finally made the outlook for the trends of NAI concentrations in the prospective using the auto regressive integrated moving average (ARIMA) models. The dataset of NAI concentrations and meteorological factors measured at the fixed stations in the Mountain Wuyi National Park were obtained from the Fujian Provincial Meteorological Bureau. The study showed that NAI concentrations were correlated with relative humidity spanning all seasons. Water was an important factor affecting the distribution of NAI concentrations in different time series. Compared with other ARIMA models, the outlook value of the ARIMA (0,1, 1) model was closer to the original data and the errors were smaller. This article provided a unique perspective on the study of the distribution of negative air oxygen ions over time series.