Carbon neutrality is an important goal for addressing global warming. It can be achieved by increasing carbon storage and reducing carbon emissions. Vegetation plays a key role in storing carbon, but it is often lost or damaged, especially in areas affected by desertification. Therefore, restoring vegetation in these areas is crucial. Using advanced techniques to improve ecosystem structure can support ecological processes, and enhance soil and environmental conditions, encourage vegetation growth, and boost carbon storage effectively. This study focuses on optimizing Ecological Spatial Networks (ESNs) for revitalization and regional development, employing advanced techniques such as the MCR model for corridor construction, spatial analysis, and Gephi for mapping topological attributes. Various ecological and topological metrics were used to evaluate network performance, while the EFCT model was applied to optimize the ESN and maximize carbon sinks. In the Thal Desert, ecological source patches (ESPs) were divided into four modularity levels (15.6% to 49.54%) and five communities. The northeastern and southwestern regions showed higher ecological functionality but lower connectivity, while the central region exhibited the reverse. To enhance the ESN structure, 27 patches and 51 corridors were added to 76 existing patches, including 56 forest and 20 water/wetland patches, using the EFCT model. The optimized ESN resulted in a 14.97% improvement in carbon sink capacity compared to the unoptimized structure, primarily due to better functioning of forest and wetland areas. Enhanced connectivity between components contributed to a more resilient and stable ESN, supporting both ecological sustainability and carbon sequestration.

The Mongolian Plateau, a critical area for global climate change response, faces increasing vulnerability from climate change and human activities impacting its arid ecosystems. This study integrates GeoDetector and machine learning to predict vegetation Carbon Use Efficiency (CUE) dynamics. It utilizes multi-source remote sensing data (MODIS, ERA5-Land) from 2000 to 2020 and incorporates four Shared Socioeconomic Pathways (SSPs) from CMIP6. The results indicate the following: (1) significant spatial variation exists, with high-value CUE areas (≥0.7) in the northwest due to favorable climatic conditions, while low-value areas (<0.6) in the east are affected by decreasing precipitation and overgrazing; (2) CUE increased at an annual rate of 1.03%, with a 43% acceleration after the 2005 climate shift, highlighting the synergistic effects of ecological engineering; (3) our findings reveal that the interaction of evapotranspiration and temperature dominates CUE spatial differentiation, with the random forest model accurately predicting CUE dynamics (root mean square error (RMSE) = 0.0819); (4) scenario simulations show the SSP3-7.0 pathway will peak CUE at 0.6103 by 2050, while the SSP5-8.5 scenario will significantly reduce spatial heterogeneity. The study recommends enhancing water–heat regulation in the northwest and implementing vegetation restoration strategies in the east, alongside establishing a CUE warning system. This research offers valuable insights for improving carbon sequestration and climate resilience in arid ecosystems, with significant implications for carbon management under high-emission scenarios.

The Yellow River Basin (YRB) is recognized as an essential ecological safeguard in China, significantly contributing to ecological stability and environmental protection. However, rapid socioeconomic development poses significant threats to the ecological security of the region. To address this issue, this study used normalized difference vegetation index data to identify ecological source areas. Using optimal parameters-based geographical detector model processed a multi-source data set to develop an ecological spatial network. Methods for topological and pattern analysis were utilized to examine the network. Additionally, various ecosystem services (ESs) within the ecological source areas and their interrelationships were calculated to analyze their associations with network characteristics, climate, and human factors under different network patterns. Finally, based on the importance of the structure and function of regional vegetation, a series of strategies has been proposed to optimize and protect the stability of its spatial distribution. The research findings show that: (1) areas with high ES indicator values are mainly found in the upper reaches of the YRB and the adjacent regions of the Taihang Mountains (2) the ecological spatial network of the YRB is denser in the mid-upper reaches and sparser in the lower reaches. (3) There is a significant synergy between water supply and water conservation within the ecological source areas, while landscape aesthetics and carbon storage, as well as water supply and carbon storage, exhibit significant trade-off effects. (4) Five network patterns and six ES bundles were identified. The correlation between ESs, network topological metrics, and climate-human factors varies across different patterns, with more structurally complete network patterns generally providing better ESs.

Dryland phenology is tightly constrained by water availability, yet the temporal depth of drought influence remains poorly resolved at regional scales. We analyzed the start and end of season across the Mongolian Plateau using 500 m MODIS kNDVI for 2001–2020 and a phenology-anchored framework that linked multi-timescale SPEI directly to the month of each phenological event. By varying accumulation windows and testing month-wise lags up to twelve months, we mapped pixel-level optimal timescales and sensitivities. Phenology exhibits a clear north–south gradient with weak long-term shifts relative to large interannual variability. Drought acts through two pathways. Multi-month winter–spring moisture deficits delay spring green-up, with the strongest SOS sensitivity to antecedent drought about six to nine months prior. Summer–autumn dryness advances dormancy, and EOS is governed mainly by near-term moisture over the previous one to two months. Responses differ among ecoregions, with deserts and desert steppes the most sensitive and forests and alpine meadows less responsive. These asymmetric timescales imply that prolonged deficits can postpone spring emergence into the following year, whereas short deficits truncate the current season, offsetting warming-driven extensions of growing-season length. Incorporating phenology-anchored, multi-timescale drought indicators can improve model forecasts of dryland carbon–water dynamics and inform monitoring and adaptation in the most water-limited ecoregions.

Desertification presents a significant ecological challenge in arid and semi-arid regions, posing a severe threat to regional ecological security and sustainable development. This study introduces an integrated framework for desertification vulnerability assessment, combining the MEDALUS model with the XGBoost algorithm, to evaluate desertification dynamics across the Mongolian Plateau from 2000 to 2020 and project future trends under four Shared Socioeconomic Pathways (SSPs) for 2030. The findings are as follows: (1) Between 2000 and 2020, desertification vulnerability was most pronounced in the southern and western regions of the plateau, with lower vulnerability observed in the northern and eastern areas. High-vulnerability zones expanded over time, highlighting the need for targeted and prioritized management efforts. (2) Climate factors—particularly temperature, wind speed, and precipitation—emerged as the dominant drivers of desertification, followed by soil characteristics and vegetation (NDVI). The influence of human activities on desertification became increasingly significant, stressing the need for improved land management and sustainable practices. (3) Future risks show that desertification vulnerability in the Mongolian Plateau will intensify under high-emission scenarios (SSP3-7.0, SSP5-8.5), with significant expansion of high vulnerability areas. Lower-emission scenarios (SSP1-2.6, SSP2-4.5) may reduce some impacts, but high vulnerability will persist, highlighting the need for urgent climate mitigation and adaptation efforts.