Changes in rainfall play an important role in agricultural production, water supply and management, and social and economic development in arid and semi-arid regions. The objective of this study was to examine the trend of rainfall series from 18 meteorological stations for monthly, seasonal, and annual scales in Shanxi province over the period 1957–2019. The Mann–Kendall (MK) test, Spearman’s Rho (SR) test, and the Revised Mann–Kendall (RMK) test were used to identify the trends. Sen’s slope estimator (SSE) was used to estimate the magnitude of the rainfall trend. An autocorrelation function (ACF) plot was used to examine the autocorrelation coefficients at various lags in order to improve the trend analysis by the application of the RMK test. The results indicate remarkable differences with positive and negative trends (significant or non-significant) depending on stations. The largest number of stations showing decreasing trends occurred in March, with 10 out of 18 stations at the 10%, 5%, and 1% levels. Wutai Shan station has strong negative trends in January, March, April, November, and December at the level of 1%. In addition, Wutai Shan station also experienced a significant decreasing trend over four seasons at a significance level of 1% and 10%. On the annual scale, there was no significant trend detected by the three identification methods for most stations. MK and SR tests have similar power for detecting monotonic trends in rainfall time series data. Although similar results were obtained by the MK/SR and RMK tests in this study, in some cases, unreasonable trends may be provided by the RMK test. The findings of this study could benefit agricultural production activities, water supply and management, drought monitoring, and socioeconomic development in Shanxi province in the future.

Strong soil erosion and increasing human activities have made Loess Plateau areas ecologically fragile regions. Constructing the ecological security pattern (ESP) is imperative to maintain their ecosystem functions and sustainable development. However, it is still challenging to establish the ESP in such an unstable and scattered ecological environment. In this study, we take Shanxi Province, which suffers severe ecological problems in Loess Plateau areas, as an example to construct the ESP in a pattern of “source-resistance-corridor”. The proposed methods include the following steps: (1) potential ecological sources are selected with important ecosystem functions based on contributions of soil and water conservation, habitat quality, and carbon storage; (2) ecological sources are determined by considering core areas at the landscape scale based on morphological spatial pattern analysis (MSPA) along with stability based on dynamic assessment on previous sources; (3) the comprehensive resistance surface is constructed by multiple resistance factors and remotely sensed nighttime light data; (4) ecological corridors are simulated and extracted based on circuit theory. As a result, the proposed ESP in our study area mainly includes 13,592 km² of ecological sources, 8519.64 km of ecological corridors, and 277 ecological nodes. Meanwhile, an ecological framework of “two axes, three belts, and three zones” was proposed based on the optimization and reorganization of ecological components within the ESP. Our research lays a methodological and practical foundation for regional ESP construction and sustainable development in Loess Plateau areas.

Precise delineation of marine aquaculture areas is vital for the monitoring and protection of marine resources. However, due to the coexistence of diverse marine aquaculture areas and complex marine environments, it is still difficult to accurately delineate mariculture areas from very high spatial resolution (VHSR) images. To solve such a problem, we built a novel Transformer–CNN hybrid Network, named TCNet, which combined the advantages of CNN for modeling local features and Transformer for capturing long-range dependencies. Specifically, the proposed TCNet first employed a CNN-based encoder to extract high-dimensional feature maps from input images. Then, a hierarchical lightweight Transformer module was proposed to extract the global semantic information. Finally, it employed a coarser-to-finer strategy to progressively recover and refine the classification results. The results demonstrate the effectiveness of TCNet in accurately delineating different types of mariculture areas, with an IoU value of 90.9%. Compared with other state-of-the-art CNN or Transformer-based methods, TCNet showed significant improvement both visually and quantitatively. Our methods make a significant contribution to the development of precision agricultural in coastal regions.

Despite extensive research on how climate and environmental factors influence leaf stoichiometry at national and global scales, experimental evidence on their effects at the community level remains limited, particularly in extremely arid regions. Herein, we investigated the leaf stoichiometry including carbon (C), nitrogen (N), and phosphorus (P) along a fine-scale riparian gradient (50–1250 m from the riverbank) in an extremely arid Populus euphratica forest in northwest China. Our results show that the community-averaged leaf total carbon (TC), total nitrogen (TN), and total phosphorus (TP) contents were 442.58 mg/g, 21.69 mg/g, and 1.18 mg/g, respectively. The community-averaged C:N, C:P, and N:P ratios were 20.74, 379.97, and 18.43, respectively. Compared to findings from other studies, the P. euphratica community exhibited lower leaf TC and TP contents but higher TN content and N:P ratios. A high N:P ratio (mean = 18.43, N:P > 16) suggests that the P. euphratica community is more susceptible to phosphorus limitation. Along the riparian gradient, community-averaged leaf TC, C:N, and C:P increased significantly, reaching their maximum (479.49 mg/g, 27.12, and 478.06, respectively) at 1250 m from the riverbank. Conversely, leaf TN and TP contents, as well as N:P, decreased significantly with increasing distance from the river, reaching their minimum values (17.49 mg/g, 0.99 mg/g, and 17.17, respectively) at 1100–1250 m. Soil available phosphorus, soil water content, soil bulk density, and soil electrical conductivity significantly influenced the leaf stoichiometry of the P. euphratica community, collectively explaining 61.78% of the total variation. Among these factors, soil water content had the most pronounced effect, surpassing soil available phosphorus, bulk density, and electrical conductivity in shaping leaf stoichiometric characteristics. Our findings indicate that at fine spatial scales, the distribution of leaf nutrients and stoichiometry seem to be predominantly influenced by local-scale factors such as soil water content, soil nutrient levels, and salt stress; P. euphratica forests would be experiencing more negative impacts in leaf nutrients and stoichiometry due to increased droughts or salt stress.