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.

An accurate, reliable and stable air quality prediction system is conducive to the public health and management of atmospheric ecological environment; therefore, many models, individual or hybrid, have been implemented widely to deal with the prediction problem. However, many of these models do not take into consideration or extract improperly the period information in air quality index (AQI) time series, which impacts the models’ learning efficiency greatly. In this paper, a period extraction algorithm is proposed by using a Luenberger observer, and then a novel period-aware hybrid model combined the period extraction algorithm and tradition time series models is build to exploit the comprehensive forecasting capacity to the AQI time series with nonlinear and non-stationary noise. The hybrid model requires a multi-phase implementation. In the first step, the Luenberger observer is used to estimate the implied period function in the one-dimensional AQI series, and then the analyzed time series is mapped to the period space through the function to obtain the period information sub-series of the original series. In the second step, the period sub-series is combined with the original input vector as input vector components according to the time points to establish a new data set. Finally, the new data set containing period information is applied to train the traditional time series prediction models. Both theoretical proof and experimental results obtained on the AQI hour values of Beijing, Tianjin, Taiyuan and Shijiazhuang in North China prove that the hybrid model with period information presents stronger robustness and better forecasting accuracy than the traditional benchmark models.

PM${}_{2.5}$ concentration prediction is a hot topic in atmospheric environment research and management. In this study, we adopt an extended dynamics differentiator and regression model to construct the novel multivariate short-term trend information-based time series forecasting algorithm (M-STI-TSF) to tackle this issue. The advantage of this model is that the dynamical short-term trend information, based on tracking-differentiator, is insensitive to high-frequency noise and is complementary to traditional statistical information. Due to the fact that the dynamical short-term trend information provided by the tracking-differentiator can effectively describe the trend of time series fluctuations, it greatly supplements the empirical information of the prediction system. It cannot be denied that short-term trend information is an effective way to improve prediction accuracy. The modeling process can be summarized as the following main steps. Firstly, each one-dimensional time series composed of an input feature is predicted using a dynamical prediction model, including short-term trend information. Then, the predicted results of multiple one-dimensional influence factors are linearly regressed to obtain the final predicted value. The simulation experiment selected major cities in North China as the research object to demonstrate that the proposed model performs better than traditional models under different model generalization ability evaluation indexes. The M-STI-TS model successfully extracted the inherent short-term trend information of PM${}_{2.5}$ time series, which was effectively and reasonably integrated with traditional models, resulting in significantly improved prediction accuracy. Therefore, it can be proven that the short-term trend information extracted by tracking-differentiator not only reflects the intrinsic characteristics of time series for practical applications, but also serves as an effective supplement to statistical information.