Feng Jin

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage.

The dynamic changes of sea ice exhibit spatial clustering, and this clustering has characteristics extending from its origin, through its development, and to its dissipation. Current research on sea ice change primarily focuses on spatiotemporal variation trends and remote correlation analysis, and lacks an analysis of spatiotemporal evolution characteristics. This study utilized monthly sea ice concentration (SIC) data from the National Snow and Ice Data Center (NSIDC) for the period from 1979 to 2022, utilizing classical spatiotemporal clustering algorithms to analyze the clustering patterns and evolutionary characteristics of SIC anomalies in key Arctic regions. The results revealed that the central-western region of the Barents Sea was a critical area where SIC anomaly evolutionary behaviors were concentrated and persisted for longer durations. The relationship between the intensity and duration of SIC anomaly events was nonlinear. A positive correlation was observed for shorter durations, while a negative correlation was noted for longer durations. Anomalies predominantly occurred in December, with complex evolution happening in April and May of the following year, and concluded in July. Evolutionary state transitions mainly occurred in the Barents Sea. These transitions included shifts from the origin state in the northwestern margin to the dissipation state in the central-north Barents Sea, from the origin state in the central-north to the dissipation state in the central-south, and from the origin state in the northeastern to the dissipation state in the central-south Barents Sea and southeastern Kara Sea. Various evolutionary states were observed in the same area on the southwest edge of the Barents Sea. These findings provide insights into the evolutionary mechanism of sea ice anomalies.

Agricultural greenhouses have a negative impact on the ecological environment while bringing huge economic and social benefits. Therefore, it is of great significance to obtain greenhouse information in a timely and accurate manner. Due to the complex spectral characteristics and dense spatial distribution characteristics of greenhouses, although the extraction of greenhouses based on a single semantic segmentation model can extract the area with high precision, the segmentation process has a serious problem of boundary adhesion between greenhouses, which makes it difficult to accurately obtain the quantity of greenhouses. To address this, our study proposes a method for greenhouse extraction that integrates semantic segmentation and edge constraints, using high-spatial-resolution remote sensing images to accurately extract the area and quantity of greenhouses. This method employs an improved semantic segmentation model (AtDy-D-LinkNet) to extract the greenhouse area, which embeds a convolutional attention module into the D-LinkNet and adopts a dynamic upsampling strategy, achieving precise greenhouse extraction. Experiments demonstrate that the improved model increased the recall, precision, F1 score, and intersection over union by 1.68%, 2.27%, 1.93%, and 3.54%, respectively, compared to the original model. To address the significant edge adhesion issue in semantic segmentation and accurately extract the quantity of greenhouses, we developed an edge constraint approach. This approach uses an edge detection model to extract greenhouse boundaries, further constrains the greenhouse surfaces, separates adhered greenhouses, and outputs vector patches representing individual greenhouses, thereby achieving precise greenhouse quantity extraction. The experiments show that this method effectively combines the advantages of semantic segmentation and edge detection. It not only ensures the accuracy of greenhouse area extraction but also effectively solves the boundary adhesion issue, significantly improving quantity extraction accuracy, resulting in vector patches that align with the actual area, quantity, and spatial distribution of greenhouses. This can provide a data foundation for greenhouse management and planning in agriculture.