Wetland ecosystems are one of the three great ecosystems on Earth. With a deepening of research on wetland ecosystems, researchers have paid more and more attention to wetland ecosystem services such as flood mitigation, climate control, pollution prevention, soil-erosion prevention, biodiversity maintenance, and bio-productivity protection. This study focuses on a lakeside wetland ecosystem in Hefei, a city in central China, and estimates the value of ecosystem services such as material production, air purification, water conservation, biodiversity, recreation, species conservation, education and scientific research. We adopted the market value method, carbon tax method, afforestation cost method, shadow engineering method and contingent value method (CVM) using questionnaire survey data during the study period. The results show that the total value of the ecosystem services of Lakeside Wetland Park was 144 million CNY in 2015. Among these services, the value of society service is the maximum at 91.73 million CNY, followed by ecological service and material production service (42.23 million CNY and 10.43 billion CNY in 2015 respectively). When considering wetland ecosystems for economic development, other services must be considered in addition to material production to obtain a longer-term economic value. This research reveals that there is scope for more comprehensive and integrated model development, including multiple wetland ecosystem services and appropriate handling of wetland ecosystem management impacts.

Pine wilt disease (PWD) is a great danger, due to two aspects: no effective cure and fast dissemination. One key to the prevention and treatment of pine wilt disease is the early detection of infected wood. Subsequently, appropriate treatment can be applied to limit the further spread of pine wilt disease. In this work, a UAV (Unmanned Aerial Vehicle) with a RGB (Red, Green, Blue) camera was employed as it provided high-quality images of pine trees in a timely manner. Seven flights were performed above seven sample plots in northwestern Beijing, China. Then, raw images captured by the UAV were further pre-processed, classified, annotated, and formed the research datasets. In the formal analysis, improved YOLOv5 frameworks that integrated four attention mechanism modules, i.e., SE (Squeeze-and-Excitation), CA (Coordinate Attention), ECA (Efficient Channel Attention), and CBAM (Convolutional Block Attention Module), were developed. Each of them had been shown to improve the overall identification rate of infected trees at different ranges. The CA module was found to have the best performance, with an accuracy of 92.6%, a 3.3% improvement over the original YOLOv5s model. Meanwhile, the recognition speed was improved by 20 frames/second compared to the original YOLOv5s model. The comprehensive performance could well support the need for rapid detection of pine wilt disease. The overall framework proposed by this work shows a fast response to the spread of PWD. In addition, it requires a small amount of financial resources, which determines the duplication of this method for forestry operators.