Yaoxiang Liu’s research while affiliated with Lanzhou Jiaotong University and other places

Global warming is accelerating the permafrost degradation along the Qinghai-Tibet Railway (QTR), causing the surface deformation (SD) of the railway subgrade. Especially in the Salt Lake to Wuli section of the QTR, the permafrost is widely distributed, and the SD has been the most serious. However, the spatio-temporal characteristics and mechanism of SD are still unclear. In addition, it is very important to predict the future trend of SD. Therefore, we acquired time series SD results from 2019 to 2022 based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and analyzed the spatio-temporal characteristics and mechanism of SD in the Salt Lake to Wuli section. Subsequently, the EnvCA-GRU model for SD prediction was developed, integrating the Multi-Head Cross-Attention (MHCA) mechanism and Gated Recurrent Unit (GRU) to account for changes in environmental factors (EFs). The model was then employed to forecast SD trends over the next two years. Our results showed that the SD was uneven in the Salt Lake to Wuli section of the QTR from 2019 to 2022, there were six typical deformation areas, and the maximum cumulative ground subsidence reached 126.79 mm. The SD velocity of the sunny slope was higher than that of the shady slope, and the closer to the QTR, the greater the ground subsidence. Land surface temperature (LST), normalized difference vegetation index (NDVI), and precipitation are the main factors affecting SD. Our proposed EnvCA-GRU prediction model fusing NDVI, LST, and precipitation showed an RMSE of 0.153 and an R² of 0.991, the proposed model was reliable. The maximum cumulative ground subsidence of six typical areas by July 2024 reached 177.52, 268.08, 287.73, 270.99, 190.70, and 211.89 mm, respectively. The results of this study can play a guiding role in the early warning and mitigation of ground subsidence disasters along the QTR.

Surface deformation of alpine peatland in China has an important effect on runoff and is of great significance for wetland ecosystem protection. However, spatio-temporal characteristics of alpine peatland surface deformation in China lack systematic studies, and the driving mechanism is not yet clear. In this study, we selected the alpine peatland of Maduo County in China as the research object, surface deformation of peatland based on the small baseline subset radar interferometry (SBAS-InSAR) technique was obtained, we analyzed spatio-temporal deformation characteristics and patterns of peatland, explored the driving mechanism of the peatland surface deformation with single-factor and multi-factor combinations of Geo-detector, respectively. The results showed that the overall subsidence rates of peatlands in Maduo County, China slowed down year by year from 2018 to 2020, but there was seasonal freezing and thawing, subsidence rates of peatlands at high elevation and high slopes were stable, peatlands at low elevation and low slope were vulnerable to disturbance, subsidence rates are largest. Maliecuo, Bailongqu, and Gaerlawangzang regions were serious subsidence, the maximum subsidence rate was 159 mm/year. Meteorological factors and geological conditions were the main reasons for the surface deformation of alpine peatland in Maduo County, China. This study provides a theoretical basis for the conservation and restoration of peatland ecosystems in the alpine regions of China.

Accurate and efficient landslide identification is an important basis for landslide disaster prevention and control. Due to the diversity of landslide features, vegetation occlusion, and the complexity of the surrounding surface environment in remote sensing images, deep learning models (such as U-Net) for landslide detection based only on optical remote sensing images will lead to false and missed detection. The detection accuracy is not high, and it is difficult to satisfy the demand. SAR has penetrability, and SAR images are highly sensitive to changes in surface morphology and structure. In this study, a multi-input channel U-Net landslide detection method fusing SAR, optical, and topographic multi-source remote sensing data is proposed. Firstly, a multi-input channel U-Net model fusing SAR multi-source remote sensing data is constructed, then an attention mechanism is introduced into the multi-input channel U-Net to adjust the spatial weights of the feature maps of the multi-source data to emphasize the landslide-related features, and finally, the proposed model is applied to the experimental scene for validation. The experimental results demonstrate that the proposed model combined with SAR multi-source remote sensing data improves the perception ability of landslide features, focuses on learning landslide-related features, improves the accuracy of landslide detection, and reduces the rate of false detections and missed detections. Compared with the traditional U-Net landslide detection method based on SAR multi-source remote sensing data and the traditional U-Net method that disregards SAR multi-source remote sensing data, the proposed method has the best quantitative evaluation indicators. Among them, the proposed method obtained the highest F1 value (66.18%), indicating that fused SAR remote sensing data can provide rich and complementary landslide feature information, simultaneously setting up a multi-channel U-Net model to input multi-source remote sensing data can effectively process landslide feature information. The proposed method can provide theoretical and technical support for landslide disaster prevention and control.

Net ecosystem productivity (NEP) quantifies magnitude of the terrestrial vegetation carbon sinks. Drought is one of the most important stressors affecting vegetation NEP. At present, the spatiotemporal dynamics of vegetation NEP in drought-prone of Northwest China (NWC) lack discussion under different climatic zones and land cover types, and the response of vegetation NEP to drought remains unclear. Hence, we estimated the vegetation NEP in NWC using ground and remote sensing data and quantified the spatiotemporal differentiation of NEP under different climatic zones and land cover types. The drought fluorescence monitoring index (DFMI) was developed to examine the relationship between vegetation NEP and drought response based on the solar-induced chlorophyll fluorescence (SIF) data. Our results suggested that vegetation carbon sinks increased significantly at 7.09 g C m⁻² yr⁻¹ in NWC during 2000–2019, mainly in northern Shaanxi, eastern and southern Gansu, and southern Ningxia. NEP showed increasing trends under different climatic zones and land cover types, but there were differences in carbon sink capacity. The strongest carbon sink capacity was in humid regions and forests, while the weakest was in arid regions and grasslands. The vegetation carbon sinks showed a non-linear relationship with the drought degree reflecting multiple trend differences, especially in forests and grasslands. The response to drought was faster and more significant in semi-arid and semi-humid transition zones and extreme humid regions when vegetation carbon sinks decreased. DFMI was a good indicator to monitor drought conditions in NWC. NEP and DFMI were an 8–20-month periodic positive correlation and showed a high correlation with high–high and low–low clustering spatially. Drought significantly weakened vegetation carbon sinks in NWC. This study emphasizes the demand to rapidly identify climatic conditions that lead to decrease significantly in vegetation carbon sinks and to formulate adaptation strategies aimed at reducing drought risk under global warming.

Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results.