The ecological redline defines areas where industrialization and urbanization development should be prohibited. Its purpose is to establish the most stringent environmental protection system to meet the urgent needs of ecological function guarantee and environmental safety. Nowadays, deep learning methods have been widely used in change detection tasks based on remote sensing images, which can just be applied to the monitoring of the ecological redline. Considering the convolution-based neural networks’ lack of utilization of global information, we choose a transformer to devise a Siamese network for change detection. We also use a transformer to design a pyramid pooling module to help the network maintain more features. Moreover, we construct a self-supervised network based on a contrastive method to obtain a pre-trained model, especially for remote sensing images, aiming to achieve better results. As for study areas and data sources, we chose Hebei Province, where the environmental problem is quite nervous, and used its GF-1 satellite images to do our research. Through ablation experiments and contrast experiments, our method is proven to have significant advantages in terms of accuracy and efficiency. We also predict large-scale areas and calculate the intersection recall rate, which confirms that our method has practical values.

As important green energy, wind power and photovoltaic power have great development prospects. The suitability evaluation of wind and solar power plants is a popular research field, which is related to the sustainable and healthy development of wind and solar power generation. In this paper, based on multiple dimensions such as land types, climatic conditions, topographic features and policy environment, we selected 10 indicators and combined (analytical hierarchy process) AHP method to build a suitability assessment model for evaluating the suitability of solar and wind power in Inner Mongolia, China. The findings revealed that, Inner Mongolia has a great potential to generate wind and solar electricity, for wind power, the category of ‘excellent’ regions covers 83855 km² and represents 7.10% of the total surface area; for solar power, 7.66% (nearly 90420 km²) are classified as ‘excellent’. The suitability of both solar and wind energy in the western region is considered to have the most suitable development region, parts of Alxa League and Bayannur City have great potential for combined wind and solar power generation. The research results could provide important technical and data support for capacity evaluation and power station location decision.

Photovoltaics, a clean energy source, have received widespread attention worldwide recently. Many countries are carrying out photovoltaic construction, while also compiling and analyzing their photovoltaic development status. The same goes for China. In northwestern China, a considerable number of cities lack electricity. Meanwhile, its vast plains and abundant sunlight are conducive to the construction of photovoltaics. Therefore, the northwestern China has vigorously carried out photovoltaic construction nowadays. With the support of high-resolution and multi-temporal remote sensing images, we are able to analyze the development status of photovoltaics in these regions. We chose six provinces in northwestern China as our research areas and took three steps to complete our studies. Firstly, we extracted patterns of photovoltaics using deep learning methods. Secondly, based on the patterns and national land use survey data, we calculated the distribution and development status of photovoltaics in each province. Thirdly, we present the statistical results in figures and charts, showing the photovoltaic construction status and its development trend. We finally made conclusions and discussions about our insufficiency in work and future plans for further study.

By two or more modes of transportation to connect with each other and complete the transport process, multimodal transport could significantly improve transport efficiency, reduce costs, while promote energy conservation and emission reduction, compared with the traditional single mode of transport. Therefore, the multimodal transport development reflects the level of national or regional economic development to some extent. In this study, multimodal transport potential index was built to evaluate the possibility of multimodal transport. Remote sensing images from satellites were used to extract roads, railway stations and airports. A method was proposed to derive the road-rail transport potential index and road-air transport potential index, and then for the analysis of multimodal transport potential in 36 cities. Results show that there is a big gap among 36 cities on the development potential of road-rail and road-air transport. Compared with the road-rail transport potential index, the distribution of road-air transport potential index is more consistent among and within the seven regions. Index values of megacity and super cities are much higher than type I and II big cities. Moreover, in the 36 cities, there is a positive correlation between the multimodal transport potential index and GDP. The research results could be used to quantitatively evaluate the development of transport infrastructure, consequently provide more solid and reliable basic information for the planning, design and scheme adjustment of multimodal transport, thus comprehensively improve the efficiency and quality of multimodal transport, serve the territorial spatial planning.

Offshore wind farm (OWF)’s impact on marine ecological environment has attracted widespread attention since its rapid development. The paper uses high-resolution satellite image data to extract the distribution of offshore wind farms (OWFs) in China based on deep learning method. Chlorophyll-a (CHL-a) concentration and sea surface temperature (SST) were used as indicators to analyze the impact of OWFs’ development. We can then draw the following conclusions: 1) From 2016 to 2022, the number of OWFs in China continues to increase, the growth rate reachs 28% in 2022. 2) The construction of OWFs presents a development trend from near sea to far sea and from shallow sea to deep sea. From 2016 to 2022, the new OWFs are mainly concentrated in sea areas with 5-50 meters water depth, accounting for 84.7 % of the total increase in OWFs. 3) The distribution density of OWFs is negatively correlated with CHL-a concentration and SST, the more OWFs, the greater the impact on CHL-a and SST. In one word, our work analyzed the development trend of OWFs in China and evaluated the impact of OWFs’ expansion on marine ecological environment and climate.

The rapid expansion of wind energy has raised growing concerns about its impacts on land cover and the environment. This study focuses on county-level areas with high-density wind farm distribution in the Xing’an League, Inner Mongolia, China. Using high-resolution satellite imagery from 2016 to 2024, we visually interpreted land cover within and around wind farms and analyzed their spatiotemporal dynamics. The results show that: (1) Land cover change in the study area was primarily driven by wind farm expansion, which increased by 130.87 km² (+260.35%) from 2016 to 2024, exhibiting the highest dynamic degree among all land cover categories (LK = +32.54%/yr). (2) Grassland was the most affected land cover type, with 78.74 km² converted to wind farm land, accounting for 59.66% of newly established wind farm area, followed by cultivated land (20.06%) and forest land (18.33%). (3) With wind power expansion, the land cover composition within wind farms shifted from a cultivated land–grassland balance toward grassland dominance. (4) Areas temporarily disturbed by wind farm construction tended to recover progressively, with cultivated land recovering faster than grassland. These findings provide an important basis for assessing the environmental impacts of wind farm development in Inner Mongolia.