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Objective Strong earthquake-induced landslides are characterized by large number, wide distribution and large scale, and seriously threaten people's lives and property. Landslide susceptibility mapping (LSM) can quickly predict the spatial distribution of prone areas, which is highly important for reducing the risk of post-earthquake disasters. However, in the studies of coseismic landslide LSMs, how to select negative landslide samples and integrate machine learning models to improve the evaluation accuracy still needs further investigation. Methods In this study, the landslides induced by the Wenchuan earthquake in mountainous areas are selected as a case study. First, 10 landslide influencing factors, such as topography, geological environment, and seismic parameters, are selected to analyse the spatial distribution of landslides. Then, collinearity analysis is used to test data redundancy, nonnegative sample points from the sampling strategies are randomly selected in the extremely low susceptibility regions by the frequency ratio (FR) method. Finally, gradient boosting decision tree (GBDT), random forest (RF), and their optimal models are used to predict coseismic landslide susceptibility, conduct a comparative study of the models and carry out an accuracy assessment. Results The results show that ① the spatial distribution of landslides is controlled by multiple factors, and ② the accuracy of the models is FR-RF(AUC=0.943)>FR-GBDT(AUC=0.926)>RF(AUC=0.901)>GBDT(AUC=0.856). ③ Selecting negative landslide samples in low susceptibility areas could significantly improve the accuracy of LSMs. Conclusion The research results can provide a reference for selecting negative landslide samples and constructing evaluation models, as well as for providing theoretical support for post-earthquake disaster prevention and mitigation.

The 2018, Mw 6.6 Hokkaido Eastern Iburi earthquake in Japan triggered over 10,000 landsliding in an area spanning about 500 km2, altering the local topography and leading to the accumulation of loose deposits on hillslopes and in valleys. However, a comprehensive post-seismic landslide inventory and an assessment of topographic changes are lacking, hindering a quantitative hazard assessment. Additionally, the extent of vegetation recovery in areas affected by coseismic landslides, a key indicator of post-seismic debris flow hazard, has not been evaluated. Here, we utilize high-resolution digital elevation models and multi-temporal satellite imagery to analyze topographic changes and vegetation dynamics in the earthquake's epicentral area (seismic intensity >5.5). We observe that the event roughened the overall gentle topography of the region and made the slopes steeper. Owing to the absence of significant rainstorms and snowmelt post 2018, only a few debris remobilizations (60) and new landslides (80) have occurred in the affected region. Moreover, we noticed a slow vegetation recovery in the post-seismic phase, suggesting that the likelihood of debris flows and gully erosion remains elevated, highlighting the need for continued monitoring and assessment. [ABSTRACT FROM AUTHOR]

Landslide disasters cause serious property losses and casualties every year. Landslide displacement prediction is fundamental for mitigating landslide disasters. Several approaches have been used to predict landslide displacement, yet a more accurate and reliable displacement prediction still has a poor understanding of landslide early warning systems for landslide mitigation, due to limited data and mutational displacements. To boost the robustness and accuracy of landslide displacement prediction, this paper assembled a new hybrid model containing the local mean decomposition (LMD), innovations state space models for exponential smoothing (ETS), and the temporal convolutional network (TCN). The proposed model, which is based on over 10 years of long-term time series monitoring GPS data, was tested on the selected case—stepwise Baijiabao landslide in the Three Gorges Reservoir area of China (TGRA) was tested by the proposed model. The results presented that the LMD–ETS–TCN model has the best performance in comparison with other benchmark models. Compared with autoregressive integrated moving average (ARIMA), support vector regression (SVR), and long short-term memory neural network (LSTM), the accuracy was noticeably improved by an average of 40.9%, 46.2%, and 22.1%, respectively. The robustness and effectiveness of the presented approach are attested, and it has discernible improvements for landslide displacement prediction.

Paddy rice cropping systems play a vital role in food security, water use, gas emission estimates, and grain yield prediction. Due to alterations in the labor structure and the high cost of paddy rice planting, the paddy rice cropping systems (single or double paddy rice) have drastically changed in China in recent years; many double-cropping paddy rice fields have been converted to single-cropping paddy rice or other crops, especially in southern China. Few maps detect single and double paddy rice and cropping intensity for paddy rice (CIPR) in China with a 30 m resolution. The Landsat-based and effective flooding signal-based phenology (EFSP) method, which distinguishes CIPR with the frequency of the effective flooding signal (EFe), was proposed and tested in China. The cloud/ice/shadow was excluded by bit arithmetic, generating a good observation map, and several non-paddy rice masks were established to improve the classification accuracy. Threshold values for single and double paddy rice were calculated through the mapped data and agricultural census data. Image processing (more than 684,000 scenes) and algorithm implementation were accomplished by a cloud computing approach with the Google Earth Engine (GEE) platform. The resultant maps of paddy rice from 2014 to 2019 were evaluated with data from statistical yearbooks and high-resolution images, with producer (user) accuracy and kappa coefficients ranging from 0.92 to 0.96 (0.76–0.87) and 0.67–0.80, respectively. Additionally, the determination coefficients for mapped and statistical data were higher than 0.88 from 2014 to 2019. Maps derived from EFSP illustrate that the single and double paddy rice systems are mainly concentrated in the Cfa (warm, fully humid, and hot summer, 49% vs. 56%) climate zone in China and show a slightly decreasing trend. The trend of double paddy rice is more pronounced than that of single paddy rice due to the high cost and shortages of rural household labor. However, single paddy rice fields expanded in Dwa (cold, dry winter, and hot summer, 11%) and Dwb (cold, dry winter, and warm summer, 9%) climate zones. The regional cropping intensity for paddy rice coincides with the paddy rice planting area but shows a significant decrease in south China, especially in Hunan Province, from 2014 to 2019. The results demonstrate that EFSP can effectively support the mapping of single and double paddy rice fields and CIPR in China, and the combinations of Landsat 7 and 8 provide enough good observations for EFSP to monitor paddy rice agriculture.