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Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight “conditional-probability-based” bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a “classification-free” modification, in which factor classifications are avoided and the result is dominated by a single parameter, “bin width”. Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis.

Loess slopes with steep gradients are particularly prone to vertical tension cracks at the crest, resulting from unloading and other factors. These cracks significantly affect the spatiotemporal distribution of moisture infiltration during rainfall, potentially leading to slope instability. This study investigates the impact of crest-tension cracks on moisture infiltration in loess slopes under extreme rainfall conditions, focusing on crack position, depth, and width. Soil moisture content and the dynamics of wetting fronts were monitored to assess how these tension cracks influence infiltration patterns. The results indicate that tension cracks at the slope crest act as preferential infiltration pathways, causing water retention within the cracks and forming a “U-shaped” preferential infiltration zone. The extent of this “U-shaped” wetting front is influenced by the crack’s width, depth, and proximity to the slope shoulder; wider, deeper cracks closer to the shoulder result in a more pronounced wetting front. Over time, as rainfall persists, the influence of preferential infiltration decreases, and the infiltration patterns of slopes with crest cracks begin to resemble those of homogeneous slopes. In both cases, wetting fronts exhibit intersecting patterns: one parallel to the slope crest and the other parallel to the slope surface. During the initial stages of rainfall, the migration speed of wetting fronts in slopes with crest-tension cracks was significantly higher than in homogeneous slopes. However, after prolonged rainfall, the migration speeds of wetting fronts in both scenarios converged. A strong linear correlation was observed between the average migration depth of the horizontal wetting front at the slope crest and the parallel wetting front on the slope surface, for both slope types. These findings deepen our understanding of moisture migration dynamics in loess slopes with crest-tension cracks, providing insights for developing effective slope hazard mitigation strategies.

Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.

What are the main findings? Multi-temporal landslide susceptibility assessment using a 30-year inventory and interpretable random forest models reveals persistent and period-specific high-susceptibility zones. Tree-SHAP identifies dominant environmental and geomorphic factors controlling landslide susceptibility and their nonlinear responses over time. What are the implications of the main findings? Provides a framework to understand the temporal evolution of landslide susceptibility in tectonically active alpine valleys. Supports long-term hazard assessment, spatial planning, and targeted risk mitigation in high-relief mountain regions. The Eastern Himalayan Syntaxis in the southeastern margin of the Tibetan Plateau is a tectonically active, deeply incised, high-relief region with frequent landslides. However, the long-term evolution of landslide susceptibility and the temporal behavior of its dominant conditioning factors remain insufficiently understood. This study compiled a 30-year inventory of 1350 landslides from multi-source remote-sensing data and divided it into three periods: P1 (1991–2000), P2 (2001–2010), and P3 (2011–2020). Period-specific random forest models were developed for susceptibility mapping, and Tree-SHAP was used to interpret temporal changes in dominant factors and their nonlinear responses. The models showed reliable performance, with AUC values of 0.887, 0.848, and 0.900, respectively. Susceptibility patterns showed broad temporal stability with localized reorganization, with unchanged areas accounting for 55.62%, 51.62%, and 58.51% of the P1–P2, P2–P3, and P1–P3 transitions, respectively. High and very high susceptibility zones were persistently concentrated along the Yarlung Tsangpo–Parlung Tsangpo–Yigong Tsangpo river system and major tributary junctions. SHAP results identified elevation, slope gradient, terrain curvature, NDVI, and annual precipitation as the persistent core factor group, whereas drainage proximity, the seismic disturbance proxy, and road proximity showed stronger period-dependent effects. Nonlinear SHAP responses revealed threshold-saturation, overall decreasing or distance-decay, threshold-transition, and inverted U-shaped patterns. These findings indicate that susceptibility evolution reflects the coupling between persistent geomorphic predisposition and stage-dependent environmental and disturbance-related modifiers, providing a basis for identifying persistent and stage-specific high-susceptibility zones in high-relief valley regions.

The Sichuan-Tibet railway goes across the Upper Jinsha River, along which a large number of large historical landslides have occurred and dammed the river. Therefore, it is of great significance to investigate large potential landslides along the Jinsha River. In this paper, we inspect the deformation characteristics of a rapid landsliding area along the Jinsha River by using multi-temporal remote sensing, and analyzed its future development and risk to the Sichuan-Tibet railway. Surface deformations and damage features between January 2016 and October 2020 were obtained using multi-temporal InSAR and multi-temporal correlations of optical images, respectively. Deformation and failure signs obtained from the field investigation were highly consistent. Results showed that cumulative deformation of the landsliding area is more than 50 cm, and the landsliding area is undergoing an accelerated deformation stage. The external rainfall condition, water level, and water flow rate are important factors controlling the deformation. The increase of rainfall, the rise of water level, and faster flow rate will accelerate the deformation of slope. The geological conditions of the slope itself affect the deformation of landslide. Due to the enrichment of gently dipping gneiss and groundwater, the slope is more likely to slide along the slope. The Jinsha River continuously scours the concave bank of the slope, causing local collapses and forming local free surfaces. Numerical simulation results show that once the landsliding area fails, the landslide body may form a 4-km-long dammed lake, and the water level could rise about 200 m; the historic data shows that landslide dam may burst in 2–8 days after sliding. Therefore, strategies of landslide hazard mitigation in the study area should be particularly made for the coming rainy seasons to mitigate risks from the landsliding area.

Loess covers approximately 6.6% of China and forms thick extensive deposits in the northern and northwestern parts of the country. Natural erosional processes and human modification of thick loess deposits have produced abundant, potentially unstable steep slopes in this region. Slope deformation monitoring aimed at evaluating the mechanical behavior of a loess slope has shown a cyclic pattern of contraction and expansion. Such cyclic strain change on the slope materials can damage the loess and contribute to slope instability. The site showing this behavior is a 70-m high loess slope near Yan’an city in Shanxi Province, northwest China. A Ground-Based Synthetic Aperture Radar (GB-SAR) sensor and a displacement meter were used to monitor this cyclic deformation of the slope over a one-year period from September 2018 to August 2019. It is postulated that this cyclic behavior corresponds to thermal and moisture fluctuations, following energy conservation laws. To investigate the validity of this mechanism, physical models of soil temperature and moisture measured by hygrothermographs were used to simulate the observed cyclic deformations. We found good correlations between the models based on the proposed mechanism and the exhibited daily and annual cyclic contraction and expansion. The slope absorbed energy from the time of maximum contraction to the time of maximum expansion, and released energy from the time of maximum expansion to the time of maximum contraction. Recoverable cyclic deformations suggest stresses in the loess are within the elastic range, and non-recoverable cyclic deformations suggest damage of the loess material (breakage of bonds between soil grains), which could lead to instability. Based on these observations and the models, we developed a quantitative relationship between weather cycles and thermal deformation of the slope. Given the current climate change projections of temperature increases of up to 3.5 °C by 2100, the model estimates the loess slope to expand about 0.35 mm in average, which would be in addition to the current cyclic “breathing” behavior experienced by the slope.

Based on mathematical models, in-depth analysis about the interrelationship between agricultural CO2 emission and economic development has increasingly become a hotly debated topic. By applying two mathematical models including logarithmic mean divisia index (LMDI) and Tapio decoupling, this work aims to study the driving factor and decoupling trend for Chinese agricultural CO2 emission from 1996 to 2020. Firstly, the intergovernmental panel on climate change (IPCC) method is selected to estimate the agricultural CO2 emission from 1996 to 2020, and the LMDI model is adopted to decompose the driving factors of agricultural CO2 emission into four agricultural factors including economic development, carbon emission intensity, structure, and labor effect. Then, the Tapio decoupling model is applied to analyze the decoupling state and development trend between the development of agricultural economy and CO2 emission. Finally, this paper puts forward some policies to formulate a feasible agricultural CO2 emission reduction strategy. The main research conclusions are summarized as follows: 1) During the period from 1996 to 2020, China's agricultural CO2 emission showed two stages, a rapid growth stage (1996–2015) and a rapid decline stage (2016–2020). 2) Agricultural economic development is the first driving factor for the increase of agricultural CO2 emission, while agricultural labor factor and agricultural production efficiency factor play two key inhibitory roles. 3) From 1996 to 2020, on the whole, China's agricultural sector CO2 emission and economic development showed a weak decoupling (WD) state. The decoupling states corresponding to each time period are strong negative decoupling (SND) (1996–2000), expansive negative decoupling (END) (2001–2005), WD (2006–2015) and strong decoupling (SD) (2016–2020), respectively.

Tree species recognition accuracy greatly affects forest remote sensing mapping and forestry resource monitoring. The multispectral and texture features of the remote sensing images from the ZiYuan-3 (ZY-3) satellite at two phenological phases of autumn and winter (September 29th and December 7th) were selected for constructing and optimizing sensitive spectral indices and texture indices. Multidimensional cloud model and support vector machine (SVM) model were constructed by the screened spectral and texture indices for remote sensing recognition of Quercus acutissima ( Q. acutissima ) and Robinia pseudoacacia ( R. pseudoacacia ) on Mount Tai. The results showed that, the correlation intensities of the constructed spectral indices with tree species were preferable in winter than in autumn. The spectral indices constructed by band 4 showed the superior correlation compared with other bands, both in the autumn and winter time phases. The optimal sensitive texture indices for both phases were mean, homogeneity and contrast for Q. acutissima , and contrast, dissimilarity and second moment for R. pseudoacacia. Spectral features were found to have a higher recognition accuracy than textural features for recognizing on both Q. acutissima and R. pseudoacacia, and winter showing superior recognition accuracy than autumn, especially for Q. acutissima. The recognition accuracy of the multidimensional cloud model (89.98%) does not show a superior advantage over the one-dimensional cloud model (90.57%). The highest recognition accuracy derived from a three-dimensional SVM was 84.86%, which was lower than the cloud model (89.98%) in the same dimension. This study is expected to provide technical support for the precise recognition and forestry management on Mount Tai.

This study addresses the challenge of quantifying spatially differential vertical surface deformation (SDVSD). Traditional approaches using persistent scatterer interferometry (PSI) data often focus on bulk vertical surface deformation (VSD) but overlook directional variability and struggle with irregularly distributed persistent scatterer (PS) points, limiting comprehensive SDVSD analysis. This study proposes a regular triangle network (RTN)-based framework that tessellates the study area into uniform triangular units, enabling the systematic quantification of the SDVSD direction, magnitude and rate while mitigating spatial biases from uneven PS distributions. Applied to the Shixi area in China’s Northwest Xuzhou Coalfield, the RTN-based framework revealed that (1) the SDVSD directionality aligned with the coal strata dip and working face distribution, contrasting with VSD’s focus on the magnitude and rate alone; (2) SDVSD exhibited seasonal rate fluctuations suggesting environmental influences, and, unlike VSD, it has a non-additivity property in temporal evolution; (3) there was spatial divergence between SDVSD and VSD, i.e., high VSD rates did not necessarily correlate with high SDVSD rates, emphasizing the need for an independent spatial gradient analysis. This study demonstrates that the RTN-based framework effectively disentangles the directional and magnitude (rate) components of SDVSD, offering a robust tool for the identification of deformation hotspots and linking surface dynamics to subsurface processes. By formalizing the quantification of PSI-derived SDVSD, this study advances InSAR deformation monitoring, providing actionable insights for infrastructure risk mitigation and sustainable land management in mining regions and beyond.

The upper Jinsha River basin (UJRB) located in the southeastern Qinghai-Tibet Plateau has been undergoing large-scale landslides and subsequent dam floods. However, the absence of a comprehensive inventory has impeded thorough analysis of large landslides in the UJRB. In this study, the first comprehensive inventory of large landslides in the UJRB was compiled, which includes 122 cases, distinguished from previous ones by following the entire UJRB scope, differentiating landslide feature zones, and characterizing path-dependent landslide geometric properties. Landslide geometric properties, spatial distribution, and dam flood hazard were interpreted based on this inventory. The results showed that large landslides having widening longitudinally shaped transition-deposition zones do not have significant numerical advantages compared those with narrowing shaped zones even they are frontally confined. Generally, in determining landslide spatial distribution, topographic-geomorphic factors (e.g. relief and geomorphic unit) are more important than lithologic-tectonic factors (e.g. lithologic unit and distance to fault), and coarser scale determinants (i.e. major rivers and active faults) are more decisive than finer scale determinants (i.e. minor rivers and inactive faults). Mixing up headscarp and transition-deposition zones overestimated the constraint of topographic confinement on landslide geometry, while significantly underestimated the importance of topographic slope and relief in determining landslide spatial distribution, confirming the essentiality of differentiating landslide feature zones. The empirical probability that a large landslide produces a dam lake in valleys with permanent streams in the UJRB is 25%, and this probability increases to 70% if landslide scale exceeds 106 m2 (1 km2). The data and results presented in this study provide elementary information for risk analysis of large landslides and subsequent dam floods in the UJRB.