Fluctuations in reservoir water levels exert a strong triggering effect on landslides along reservoir banks, constituting a long-term concern in the safe operation of hydroelectric projects and in the prevention and management of geological disasters. While existing research has investigated the impact of periodic water level changes on the deformation of reservoir bank landslides, observation and detection of such deformation are challenging, with noticeable gaps in understanding how these deformations respond to water level changes during the water impoundment period. To address this, our study targets the Baihetan Reservoir, leveraging 567 ascending and descending LiCSAR data and LiCSBAS (the small-baseline subset within LiCSAR) technology to construct a time series of ground deformations in the study area from 2019 to 2023. The TLCC (Time Lag Cross Correlation) model was employed to examine the time-lag response pattern of reservoir bank landslide deformations to reservoir water level changes during the impoundment period. Our findings indicate a clear time-lag response in reservoir bank landslide deformations to water level changes during the impoundment process. The rise in water levels emerged as a primary factor influencing the instability of reservoir bank landslides. During the half-year impoundment period of the Baihetan Reservoir, a time lag of 5–7 days was observed between landslide deformations and increases in water levels, with landslides on the eastern and western banks exhibiting differing time-lag response patterns. Our study illuminates the time-lag effect between water level changes during reservoir impoundment and reservoir bank landslide deformation monitoring. By proposing a quantitative analysis methodology utilizing LiCSBAS technology and the TLCC model, our findings can inform decision-making in the field of disaster prevention and reduction in reservoir engineering.

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.

Time-series Interferometric Synthetic Aperture Radar (InSAR) technology, renowned for its high-precision, wide coverage, and all-weather capabilities, has become an essential tool for Earth observation. However, the quality of the interferometric baseline network significantly influences the monitoring accuracy of InSAR technology. Therefore, optimizing the interferometric baseline is crucial for enhancing InSAR’s monitoring accuracy. Surface vegetation changes can disrupt the coherence between SAR images, introducing incoherent noise into interferograms and reducing InSAR’s monitoring accuracy. To address this issue, we propose and validate an optimization method for the InSAR baseline that considers changes in vegetation coverage (OM-InSAR-BCCVC) in the Yuanmou dry-hot valley. Initially, based on the imaging times of SAR image pairs, we categorize all interferometric image pairs into those captured during months of high vegetation coverage and those from months of low vegetation coverage. We then remove the image pairs with coherence coefficients below the category average. Using the Small Baseline Subset InSAR (SBAS-InSAR) technique, we retrieve surface deformation information in the Yuanmou dry-hot valley. Landslide identification is subsequently verified using optical remote sensing images. The results show that significant seasonal changes in vegetation coverage in the Yuanmou dry-hot valley lead to noticeable seasonal variations in InSAR coherence, with the lowest coherence in July, August, and September, and the highest in January, February, and December. The average coherence threshold method is limited in this context, resulting in discontinuities in the interferometric baseline network. Compared with methods without baseline optimization, the interferometric map ratio improved by 17.5% overall after applying the OM-InSAR-BCCVC method, and the overall inversion error RMSE decreased by 0.5 rad. From January 2021 to May 2023, the radar line of sight (LOS) surface deformation rate in the Yuanmou dry-hot valley, obtained after atmospheric correction by GACOS, baseline optimization, and geometric distortion region masking, ranged from −73.87 mm/year to 127.35 mm/year. We identified fifteen landslides and potential landslide sites, primarily located in the northern part of the Yuanmou dry-hot valley, with maximum subsidence exceeding 100 mm at two notable points. The OM-InSAR-BCCVC method effectively reduces incoherent noise caused by vegetation coverage changes, thereby improving the monitoring accuracy of InSAR.

The Yuanmou dry-hot valley has been confirmed as a typical area subjected to severe soil erosion in southwestern China. The research on the soil erosion deformation exhibited by the dry-red soil that is extensively distributed in this region takes on critical significance in deepening the investigation of soil and water loss control efforts in the Yuanmou dry-hot valley. In this study, a time series of soil erosion deformation was established at different altitudinal gradients from March 2018 to October 2022 using Small Baseline Subset InSAR (SBAS-InSAR) technology to explore the deformation patterns exhibited by soil erosion in the dry-red soil of the Yuanmou dry-hot valley. Next, the time series of fractional vegetation cover (FVC) and monthly average rainfall in the identical period were analyzed comprehensively. The result of this study are presented as follows: 1) The dry-red soil regions in the Yuanmou dry-hot valley, which were observed in the line of sight (LOS) direction, attained the deformation rates ranging from −101.683 mm/yr to 30.57 mm/yr (Ascending),-79.658 mm/yr to 41.942 mm/yr (Descending). In general, areas with significant surface erosion were concentrated in the Longchuan River basin flowing through the north and south of Yuanmou County as well as in the river confluence zones. Uplifted areas have been more widely reported in the central and northern regions of Yuanmou (e.g., the Wudongde hydroelectric power station reservoir area). 2) A significant altitudinal gradient effect was exerted by soil erosion in the dry-red soil of the Yuanmou dry-hot valley. The valley-dam area and the medium and low mountain areas were subjected to the most severe soil erosion, and the maximum erosion reached over 80 mm. Erosion was mitigated in the low mountain areas around the dam and the medium and high mountain areas, and the maximum erosion reached 60 mm and 30 mm, respectively. At an altitude of 1,350 m, soil erosion in the dry-red soil was more significantly affected by rainfall. Nevertheless, at an altitude over 1,350 m, variations in FVC become the primary factor for soil erosion in the dry-red soil. The results of this study can scientifically support soil and water loss control efforts in the Yuanmou dry-hot valley.

Landslides represent a widespread global geological hazard, presenting significant risks to both human populations and critical infrastructure. The accuracy of landslide susceptibility evaluation models serves as a critical prerequisite for landslide hazard prediction and risk management, while insufficient landslide sample data may constrain the reliability of susceptibility modeling and evaluation results. To address the challenge of limited landslide samples in complex mountainous areas, this study proposes a novel landslide susceptibility evaluation method integrating environmental similarity theory with a backpropagation neural network (Environmental Similarity Model–BP Neural Network, ESM-BP). Taking the Baihetan reservoir area as the study region, the environmental similarity degrees between potential prediction points and historical landslide samples were calculated using eight environmental factors. A normal distribution approach was employed to classify similarity thresholds, thereby constructing an enhanced landslide sample dataset. The BP neural network model was subsequently applied for susceptibility assessment, with comparative validation against support vector machine (SVM) and random forest (RF) models. The experimental results demonstrate that (1) the integration of environmental similarity theory effectively expanded the dataset by 4398 samples with distinct susceptibility levels, resolving data scarcity issues and significantly enhancing the model’s generalization capabilities. (2) Among the three models tested with supplemented samples, the BP neural network achieved optimal performance, showing improvements in the accuracy values by 0.02 and 0.14 compared to SVM and RF, respectively, Kappa coefficient enhancements of 0.02 and 0.18, and RMSE reductions of 0.04 and 0.21. This methodology enhances the applicability and reliability of landslide susceptibility evaluation models in complex mountainous environments, providing innovative insights for related research in landslide susceptibility assessment.