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.

Tree species recognition is important for remote–sensing mapping and dynamic monitoring of forest resource. However, the complex phenological cycle poses a challenge to remote–sensing recognition of deciduous tree species in mountainous areas, and the selection of temporal phase is particularly important to improve recognition accuracy. Multispectral images of Ziyuan–1 02C (ZY–1 02C) and Ziyuan–3 (ZY–3) at three phenological phases of spring, autumn and winter (12 May, 29 September and 7 December, recorded as T_5–12, T_9–29 and T_12–7) are selected to optimize sensitive spectral indices. Support vector machine (SVM) and maximum likelihood model (MLE) are constructed to explore the optimum phase of recognizing on Quercus acutissima (O. acutissima ) and Robinia pseudoacacia (R. pseudoacacia) in Mount Tai. The results showed the average spectral reflection intensity of O. acutissima was higher than that of R. pseudoacacia Compared to other phenological periods, the most significant spectral differences between O. acutissima and R. pseudoacacia were found in the spring (12 May), which was identified as the optimum phenological phase. Band 4 is the most sensitive band in all the three phases for the tree species recognition. Moreover, the overall recognition accuracy of deciduous tree species on 12 May reached 89.25%, which was significantly higher than the other two phases. On 12 May, the recognition accuracies of SVM based on sensitive spectral indices of up to 93.59% for O. acutissima and 85.44% for R. pseudoacacia, were higher overall than that of the MLE. Sensitive spectral indices introduced were shown to significantly improve the recognition accuracy for tree species over a single sensitive band. The study is expected to facilitate the precise recognition and forestry management on Mount Tai.

With the implementation of the large-scale “Grain for Green” program, vegetation in the Chinese Loess Plateau (CLP) has been remarkably restored, which is believed to in turn have significantly mitigated soil erosion in the Yellow River basin (YRB). However, various local-scale evidences suggest that plants may also promote slope failures; whether the large-scale ecological restoration in the YRB has posed negative effects on slope stability, remains an open question. Here, we inspect the effects of ecological restoration on slope stability in the YRB by quantitatively correlating the distribution of landslides with the variation of vegetation cover. We pick out those landslides located in areas with increasing vegetation continuous fields (VCF), and group them into eight major spatial clusters. We find that, landslide clusters with higher increasing gradients of VCF generally have higher proportions of active landslides, revealing that ecological restoration has posed negative effects on slope stability in the YRB. We then clarify that, soil moisture is "U-shaped" correlated with the negative effect intense index (NEII) of ecological restoration on slope stability, and a specific threshold of soil moisture correlate a minimum NEII. Finally, we demonstrate that, the ecological restoration of more than 20 years in the YRB has caused widespread increasing but fluctuation of soil moisture and evaporative stress factor and induced negative effects on loess strength and slope stability in the CLP. We show that, in areas with limited water-source, excessive revegetation will damage soil strength and reduce soil resistance because its water-consumption will exceed its water-conservation. This contribution suggests that, ecological restoration should be restrained to a sensible extent for effective soil-water conservations and landslide hazard mitigations, especially in arid and semi-arid areas like the CLP.

Estimating forest aboveground biomass (AGB) in mountainous forest ecosystems remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing-based AGB estimation framework integrating intelligent optimization and machine learning was developed for Mount Tai in eastern China. Sentinel-2 multispectral data were selected to derive 105 candidate variables, including spectral bands, vegetation indices, texture features, and topographic factors, from which 17 key variables were selected using Pearson correlation analysis for model construction. A Support Vector Machine (SVM) optimized by the Pigeon-inspired optimization (PIO) algorithm was developed to adaptively determine optimal hyperparameters, and its performance was compared with that of Random Forest (RF) and standard SVM models. Among the three models, PIO-SVM produced the highest numerical accuracy. For the training dataset, it obtained an R² of 0.85 and an RMSE of 46.12 t/hm². For the testing dataset, it achieved an R² of 0.73 and an RMSE of 62.19 t/hm², compared with 0.72 and 66.25 t/hm² for the standard SVM model and 0.70 and 65.19 t/hm² for the RF model. The spatial distribution of AGB derived from the optimal model shows higher AGB values in the central and northern regions characterized by dense forest cover, in close agreement with field observations. Overall, the results suggest that PIO-based parameter optimization can improve SVM performance for AGB estimation in mountainous forests. This study provides a reliable and efficient framework for regional-scale monitoring of forest biomass and carbon sink dynamics.

Accurate estimation of aboveground biomass (AGB) in mountainous forest ecosystem remains a significant challenge due to complex terrain, the high cost and limited applicability of traditional field-based methods. To address this issue, a remote sensing–based AGB estimation framework integrating intelligent optimization and machine learning was developed for Mount Tai in eastern China. Sentinel-2 multispectral data were selected to derive 105 candidate variables, including spectral bands, vegetation indices, texture features, and topographic factors, from which 17 key variables were selected using Pearson correlation analysis for model construction. A Support Vector Machine (SVM) optimized by the Pigeon-inspired optimization (PIO) algorithm was developed to adaptively determine optimal hyperparameters, and its performance was compared with that of Random Forest (RF) and standard SVM models. The results demonstrate that the PIO-SVM model achieved the best overall performance. For the training dataset, the model obtained an R² of 0.85. For the test dataset, the R² reached 0.73, outperforming RF (0.70) and standard SVM (0.72). The spatial distribution of AGB derived from the optimal model shows higher AGB values in the central and northern regions characterized by dense forest cover, in close agreement with field observations. These findings indicate that the PIO algorithm effectively enhances SVM hyperparameter optimization in complex parameter spaces, significantly improving the accuracy and stability of AGB estimation in mountainous forest. This study provides a reliable and efficient framework for regional-scale monitoring of forest biomass and carbon sink dynamics.