Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R² = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R² = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting.

The continuous extraction of mining resources has led to the destruction of landscape space, which has had a great impact on the human living environment and pristine ecosystems. Optimizing the ecological spatial networks of mining areas can restore and enhance the damaged ecological environment. However, there are few cases of ecological spatial network optimization in mining areas, and there are still some shortcomings. Therefore, in this study, we propose an ecological spatial network theory and a synergistic enhancement of ecological functions and carbon sink optimization model (SEEC) for urban restoration in mining areas, emphasizing the functional and carbon sink nature of ecological sources. We selected a typical mining area in Xuzhou City as the study area, explored the changes in the nature and function of the ecological spatial network from 2000 to 2020, and selected the ecological spatial network in the mining area of Xuzhou City in 2020 as the optimization study case, adding 27 ecological stepping stones and 72 ecological corridors. Through the comparison of robustness before and after optimization, we found that the optimized ecological spatial network has a stronger stability and ecological restoration ability. This study provides strategies and methods for ecological restoration projects in national mining cities and also provides references and lessons for ecological restoration in other mining areas in the future.

Quercus aquifolioides is one of the most representative broad-leaved plants in Qinghai-Tibet Plateau with important ecological status. So far, understanding how to quickly estimate the chlorophyll content of plants in plateau areas is still an urgent problem. Field Spec 3 spectrometer was used to measure hyperspectral reflectance data of Quercus aquifolioides leaves at different altitudes, and CCI (chlorophyll relative content) of corresponding leaves was measured by a chlorophyll meter. The correlation and univariate linear fitting analysis techniques were used to establish their relationship models. The results showed that: (1) Chlorophyll relative content of Quercus aquifolioides, under different altitude gradients, were significantly different. From 2905 m to 3500 m, chlorophyll relative content increased first and then decreased. Altitude 3300 m was the most suitable growth area. (2) In 350~550 nm, the spectral reflectance was 3500 m > 3300 m > 2905 m. In 750~1100 nm, the spectral reflectivity was 2905 m > 3500 m > 3300 m. (3) There were 4 main reflection peaks and 5 main absorption valleys in the leaf surface spectral reflection curve. While, 750~1400 nm was the sensitive range of leaf spectral response of Quercus aquifolioides. (4) The red edge position and red valley position moved to short wave direction with the increase of altitude, while the yellow edge position and green peak position moved to long wave direction first and then to short wave direction. (5) The correlation curve between the original spectrum and the CCI value was the best between the wavelengths 509~650 nm. The correlation between the first derivative spectrum and CCI value was the best and most stable at 450~500 nm. The green peak reflectance was most sensitive to the relative chlorophyll content of Quercus aquifolioides. The estimation model R² of green peak reflectance was the highest (y = 206.98e^−10.85x, R² = 0.8523), and the prediction accuracy was 95.85%. The research results can provide some technical and theoretical support for the protection of natural Quercus aquifolioides forests in Tibet.