The 2017 Jiuzhaigou earthquake, registering a magnitude of 7.0, triggered a series of devastating geohazards, including landslides, collapses, and mudslides within the Jiuzhaigou World Natural Heritage Site. These destructive events obliterated extensive tracts of vegetation, severely compromising carbon storage in the terrestrial ecosystems. Net Primary Productivity (NPP) reflects the capacity of vegetation to absorb carbon dioxide. Accurately assessing changes in NPP is crucial for unveiling the recovery of terrestrial ecosystem carbon storage after the earthquake. To this end, we designed this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Productivity datasets. The findings are as follows. NPP in the co-seismic landslide areas remained stable between 525 and 575 g C/m² before the earthquake and decreased to 533 g C/m² after the earthquake. This decline continued, reaching 483 g C/m² due to extreme rainfall events in 2018, 2019, and 2020. Recovery commenced in 2021, and by 2022, NPP had rebounded to 544 g C/m². The study of NPP recovery rate revealed that, five years after the earthquake, only 18.88% of the co-seismic landslide areas exhibited an NPP exceeding the pre-earthquake state. However, 17.14% of these areas had an NPP recovery rate of less than 10%, indicating that recovery has barely begun in most areas. The factor detector revealed that temperature, precipitation, and elevation significantly influenced NPP recovery. Meanwhile, the interaction detector highlighted that lithology, slope, and aspect also played crucial roles when interacting with other factors. Therefore, the recovery of NPP is not determined by a single factor, but rather by the interactions among various factors. The ecosystem resilience study demonstrated that the current recovery of NPP primarily stems from the restoration of grassland ecosystems. Overall, while the potential for NPP recovery in co-seismic landslide areas is optimistic, it will require a considerable amount of time to return to the pre-earthquake state.

Deep, unstable slopes are highly developed in mountainous areas, especially in the Minjiang River Basin, Sichuan Province, China. In this study, to reveal their deformation evolution characteristics for stability evaluation and disaster prevention, multi-period optical remote sensing images (2010–2019), SBAS-InSAR data (January 2018–December 2019), and on-site real-time monitoring (December 2017–September 2020) were utilized to monitor the deformation of a large deep-seated toppling, named the Tizicao (TZC) Toppling. The obtained results by different techniques were cross-validated and synthesized in order to introduce the spatial and temporal characteristics of the toppling. It was found that the displacements on the north side of the toppling are much larger than those on the south side, and the leading edge exhibits a composite damage pattern of “collapse failure” and “bulging cracking”. The development process of the toppling from the formation of a tensile crack at the northern leading edge to the gradual pulling of the rear edge was revealed for a time span of up to ten years. In addition, the correlation between rainfall, earthquakes, and GNSS time series showed that the deformation of the toppling is sensitive to rainfall but does not change under the effect of earthquakes. The surface-displacement-monitoring method in this study can provide a reference for the evolution analysis of unstable slopes with a large span of deformation.

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

This study systematically elucidates the mechanisms of hydraulic performance of geosynthetic clay liners (GCLs) by using tailings leachates from two representative mines, Xikuangshan and Shizhuyuan, in Hunan Province. A series of physical parameter tests, including fluid loss, free swell index, and liquid limit, together with microstructural characterization using X-ray diffraction (XRD), X-ray fluorescence (XRF), and scanning electron microscopy (SEM), were conducted to clarify the governing processes underlying the degradation of GCL performance in the presence of tailings leachates. The results demonstrate that the Xikuangshan tailings leachate markedly diminishes the hydraulic barrier performance of the bentonite component of the GCLs. This reduction primarily results from the exchange reactions between the high-valence cations in the leachate and the interlayer cations of bentonite. Such cation exchange compresses the diffuse double layer, reduces the swelling potential, and consequently manifests macroscopically as a pronounced increase in fluid loss and decreases in both the free swell index and the liquid limit. Microstructural observations further reveal that although the dominant mineralogical composition of the bentonite remains unchanged, its pore structure undergoes notable alteration: the hydraulic radius increases, the clay fabrics (crystallites) become looser and more dispersed, and the permeant pathways become more conductive. In addition, the hydraulic conductivity of GCLs permeated by different test solutions was estimated based on the measured fluid loss. The calculated values were slightly higher than the measured permeabilities, indicating that the proposed method can provide conservative estimates for rapid evaluation of GCL hydraulic performance.