Ecological vulnerability is the focus of research on global environmental impact, regional sustainable development, ecological civilization, and green development. There are eight deserts and four sandy lands in northern China. The ecological environment is sensitive to climate change and human activities. It is of great significance to carry out long-term sequential ecological vulnerability assessments. Therefore, taking northern China as the research area, this paper selects 13 data indicators such as climate, topography, and soil based on the ecological sensitivity-ecological recovery-ecological pressure model (SPR) and uses the spatial principal component analysis method (SPCA) to quantitatively evaluate the spatial and temporal differentiation characteristics and driving forces of ecological vulnerability in this area from 1980 to 2020. The results showed that areas with extreme, severe, and moderate vulnerability dominated northern China, accounting for 74.58% of the total area. The analysis revealed a decrease in ecological vulnerability from west to east and north to south. Meanwhile, from the perspective of timing, the overall level of ecological vulnerability showed an upward trend before 2000, and the overall level of ecological vulnerability continued to decline after 2000, and the quality of the ecological environment improved. During the study period, areas in northern China with severe vulnerability and slight vulnerability showed a change of 15.53% and -14.01%, respectively. The main reason for the change in ecological vulnerability is the frequent transformation between forest land, grassland, water, and cultivated land. In addition, the study found a spatial autocorrelation of ecological vulnerability of northern China and a significantly positive correlation. After 2000, the spatial aggregation of vulnerability was high-high cluster, which was mainly distributed in northwest China. The study’s findings will provide a robust scientific basis for ecosystem management and sustainable development.

Reduced vegetation cover caused by grassland degradation results in the interception of solar illuminance significantly decreasing, then leading to an increase in ground temperature, which has a significant impact on biological growth and regional climate. Based on the field experiment, we explore the interception of solar illuminance by grasslands with three degrees of degradation and its effect on the soil temperature. Solar illuminance at various heights and times was measured to obtain the interception by vegetation, which included reduction by physical shielding and consumption by the plants’ life activities. Solar illuminance in the subareas sprayed with herbicide was merely reduced by physical shielding, and the difference in solar illuminance interception between normally growing grasslands and fatal grasslands was used for the plants’ life activities. This method described above was almost the first to be used for the exploration of the functional allocation of solar illuminance interception. The percentage of solar illuminance interception was largest in the non-degraded grassland (80–95% at different times), including a 50–60% reduction on account of physical shielding and a 20–45% consumption by the grass’s life activities. Light interception by grassland vegetation directly reduced the grassland temperature. The increment of ground temperature reaches 4–13 °C when a non-degraded grassland turns into a severely degraded grassland.

The goal of this study is to analyze the land use and land cover change (LUCC) in the Northeast Sandy Land from 1990 to 2023 and reveal the characteristics of the changes in the value of ecosystem services in the area, aiming to provide scientific references for the ecological sustainable development of the Northeast Sandy Land. Using Landsat series remote sensing images from 1990 to 2023, we endeavored to obtain the information mapping of LUCC in the study area in four periods (1990, 2000, 2010, and 2023), applied GIS spatial analysis and numerical statistical analysis methods to study the LUCC in the area, and calculated the resulting changes in ecosystem service value (ESV) based on the table of ESV coefficients. The results show that: (1) in the past 33 years, LUCC in the Northeast Sandy Land has changed significantly, with an increase in the area of farmland, sandy land, and other lands, and a decrease in the area of forest, grassland, and water; (2) the value of ecosystem services has increased from CNY 1,624,557.77 billion in 1990 to CNY 173,999.99 billion in 2023, the average annual growth rate is 0.24%/a; (3) in the single ESV, the ESV of biodiversity increase is the greatest, with an increase of 0.72%/a; (4) LUCC is obvious in the Northeast Sandy Land, and LUCC is the main reason affecting the change in regional ESV.

In this study, an ecosystem service value evaluation method was applied to establish a coupling coordination degree model, quantify the coupling and coordination relationship between land-use cover change and ecosystem service value changes, and examine the impacts of different driving factors on ecosystem service value changes. The results were as follows. (1) In 2020, the coupling degree between these two variables peaked at a level above 0.9. (2) Their coupling degree was one in 2000, indicating a transformative shift and the entry into a new stage during this period within the study area. This study provides valuable information for the development of ecological compensation and restoration strategies within Horqin Sandy Land’s ecological civilization construction and development plan.

It is significant to adopt deep learning algorithms and higher-resolution remote sensing images in mapping large-scale and high-precision of aeolian landform. In this study, the western part of Horqin Sandy Land was taken as the study area. Based on the data collected from 2,786 verification points located in sandy land and remote sensing images of high-spectral and spatial resolution Sentinel-1, Sentinel-2, and GDEM (V3), this article made a research on data of large-scale and high-precision mapping classification of this area between 2015 and 2020 by using convolutional neural network deep learning algorithm. The results showed that the types of aeolian sandy landform in the west of Horqin Sandy Land mainly include longitudinal dune, flat sandy land, mild undulating sand land, nest-shaped land, parabolic dune, barchan dune, and dune chain, with an area of 1735.62, 51.32, 251.38, 902.07, 49.57, and 101.63 km². Among them, longitudinal dune, barchan dune, and dune chain have the largest area, while parabolic dunes and flat sand land are smaller. Between 2015 and 2020, the area of aeolian landforms was reduced by 89.27 km² and transformed into an oasis from a desert. This study adopted remote sensing data by high-resolution Sentinel and GDEM (V3) and convolutional neural network deep learning algorithm to map the aeolian landforms effectively. The precision of aeolian landform classification and Kappa coefficient in the western part of Horqin Sandy Land is as high as 95.51% and 0.8961. Combined with Sentinel-1, Sentinel-2, and GDEM (V3), the deep learning algorithm based on the convolution neural network can timely and effectively monitor the changes of sand dunes, which can be used for large-scale aeolian landforms.