Slope surface deformation monitoring plays an important role in landslide risk assessment and early warning. Currently, the mainstream GNSS, as a point-measurement technique, is expensive to deploy, resulting in information on only a few points of displacement being obtained on a target slope in practical applications. In contrast, optical images can contain more information on slope displacement at a much lower cost. Therefore, a low-cost, high-spatial-resolution and easy-to-implement landslide surface deformation monitoring system based on close-range photogrammetry is developed in this paper. The proposed system leverages multiple image processing methods and monocular visual localization, combined with machine learning, to ensure accurate monitoring under time series. The results of several laboratory landslide experiments show that the proposed system achieved millimeter-level monitoring accuracy in laboratory landslide experiments. Moreover, the proposed system could capture slow displacement precursors of 5 mm to 10 mm before significant landslide failure occurred, which provides favorable surface deformation evidence for landslide monitoring and early warning. In addition, the system was deployed on a natural slope in Lanzhou, yielding preliminary effective monitoring results. The laboratory experimental results demonstrated the system’s effectiveness and high accuracy in monitoring landslide surface deformation, particularly its significant application value in early warning. The field deployment results indicated that the system could also effectively provide data support in natural environments, offering practical evidence for landslide monitoring and warning.

Analyzing the propagation dynamics from meteorological drought (MD) to hydrological drought (HD) is essential for sustainable water resource management, particularly under climate change. This study analyzed the multidimensional propagation characteristics and their driving factors from MD to HD in the Jialing River Basin from 1993 to 2020. The temporal characteristics of drought propagation were analyzed using monthly and daily drought indices, with a focus on variations in initiation lag times across seasons and drought grades. The attenuation and amplification effects during drought propagation were quantified using event propagation ratios, while examining the differential propagation patterns across different drought grades. Additionally, the Geographical Detector Model was employed to identify the main drivers of spatial heterogeneity in hydrological drought response rates. The main findings are as follows: (1) at the daily scale, the initiation stage had the shortest lag, while peak and termination stages showed longer lags. Seasonal and drought grade variations were observed in the initiation lag, with shorter lags in summer and autumn. (2) Drought propagation from MD to HD resulted in an attenuation of maximum intensity, while duration and severity were amplified. (3) Spatial heterogeneity in HD response rate was mainly influenced by evaporative conditions, vegetation cover, and topography.