Liaoning Province, as an old industrial urban agglomeration since the founding of China, is an important link between the Bohai Economic Zone and the Northeast Economic Zone, and it has made great contributions to the economic development of China. The transformation of China’s economy and heavy industrial development have posed great challenges to the long-lasting growth of Liaoning Province. In this study, the driving force of land expansion was detected using the patch-generating land use simulation (PLUS) model in Liaoning Province, and the land situation in 2030 was predicted under natural development, ecological protection, and economic development scenarios. We then further coupled the PLUS model with the integrated valuation of ecosystem services and trade-offs (InVEST) model to explore the spatial autocorrelation and synergistic relationship between carbon storage and habitat quality. The results indicated the following: (1) The total accuracy of the simulation in 2020 using the PLUS model reached 94.16%, and the Kappa coefficient reached 0.9089; therefore, the simulation result was highly reliable. (2) The overall spatial pattern of both carbon storage and habitat quality decreased from the northwest and southeast to the middle, and habitat quality had an impact on carbon storage to a certain extent, with a positive spatial correlation. (3) The ecological protection (EP) scenario was the only development prospect with increasing total carbon storage, which could increase carbon sequestration by approximately 7.83 × 10⁶ Mg/C, and development prospects with optimal habitat quality. (4) Weak trade-off and weak synergy dominated in the 2030 natural development (ND) scenario; most regions showed weak synergy in the ecological protection scenario, spatial heterogeneity became more pronounced in the economic development (ED) scenario, and a strong trade-off and strong synergy emerged in individual regions. The results of the study have a positive feedback effect on establishing an ecological security barrier in Liaoning Province and furthering long-lasting low-carbon urban development.

During the vegetation growing season, the forest in the remote sensing image is more distinguishable from other background features, and the forest features are obvious and can show prominent forest area characteristics. However, deep convolutional neural network-based methods tend to overlearn the forest features in the forest extraction task, which leads to the extraction speed still having a large amount of room for improvement. In this paper, a convolutional neural network-based model is proposed based on the incorporation of spatial and channel reconstruction convolution in the U-Net model for forest extraction from remote sensing images. The network obtained an extraction accuracy of 81.781% in intersection over union (IoU), 91.317% in precision, 92.177% in recall, and 91.745% in F1-score, with a maximum improvement of 0.442% in precision when compared with the classical U-Net network. In addition, the speed of the model’s forest extraction has been improved by about 6.14 times. On this basis, we constructed a forest land dataset with high-intraclass diversity and fine-grained scale by selecting some Sentinel-2 images in Northeast China. The spatial and temporal evolutionary changes of the forest cover in the Fuxin region of Liaoning province, China, from 2019 to 2023, were obtained using this region as the study area. In addition, we obtained the change of the forest landscape pattern evolution in the Fuxin region from 2019 to 2023 based on the morphological spatial pattern analysis (MSPA) method. The results show that the core area of the forest landscape in the Fuxin region has shown an increasing change, and the non-core area has been decreasing. The SC-UNet method proposed in this paper can realize the high-precision and rapid extraction of forest in a wide area, and at the same time, it can provide a basis for evaluating the effectiveness of ecosystem restoration projects.

As an important input parameter of the ecological network, the accuracy and detail with which forest cover is extracted directly constrain the accuracy of forest ecological network construction. The development of medium- and high-resolution remote sensing technology has provided an opportunity to obtain accurate and high-resolution forest coverage data. As forests have diverse contours and complex scenes on remote sensing images, a model of them will be disturbed by the natural distribution characteristics of complex forests, which in turn will affect the extraction accuracy. In this study, we first constructed a rather large, complex, diverse, and scene-rich forest extraction dataset based on Sentinel-2 multispectral images, comprising 20,962 labeled images with a spatial resolution of 10 m, in a manually and accurately labeled manner. At the same time, this paper proposes the Dynamic Large Kernel Segformer and conducts forest extraction experiments in Liaoning Province, China. We then used forest coverage as an input parameter and classified the forest landscape patterns in the study area using a landscape spatial pattern characterization method, based on which a forest ecological network was constructed. The results show that the Dynamic Large Kernel Segformer obtains 80.58% IoU, 89.29% precision, 88.63% recall, and a 88.96% F1 Score in extraction accuracy, which is 4.02% higher than that of the Segformer network, and achieves large-scale forest extraction in the study area. The forest area in Liaoning Province increased during the 5-year period from 2019 to 2023. With respect to the overall spatial pattern change, the Core area of Liaoning Province saw an increase in 2019–2023, and the overall quality of the forest landscape improved. Finally, we constructed the forest ecological network for Liaoning Province in 2023, which consists of ecological sources, ecological nodes, and ecological corridors based on circuit theory. This method can be used to extract large areas of forest based on remote sensing images, which is helpful for constructing forest ecological networks and achieving coordinated regional, ecological, and economic development.