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Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using the image classification method and semantic segmentation method of deep learning. However, there is a lack of research on the automatic recognition of old loess landslides, which are difficult to distinguish from the environment. Therefore, this study uses the object detection method of deep learning to identify old loess landslides with Google Earth images. At first, a database of loess historical landslide samples was established for deep learning based on Google Earth images. A total of 6111 landslides were interpreted in three landslide areas in Gansu Province, China. Second, three object detection algorithms including the one-stage algorithm RetinaNet and YOLO v3 and the two-stage algorithm Mask R-CNN, were chosen for automatic landslide identification. Mask R-CNN achieved the greatest accuracy, with an AP of 18.9% and F1-score of 55.31%. Among the three landslide areas, the order of identification accuracy from high to low was Site 1, Site 2, and Site 3, with the F1-scores of 62.05%, 61.04% and 50.88%, respectively, which were positively related to their recognition difficulty. The research results proved that the object detection method can be employed for the automatic identification of loess landslides based on Google Earth images. [ABSTRACT FROM AUTHOR]

Flagship species' conservation strategies hold significant prominence in biodiversity preservation. The giant panda, a globally recognized species, has drawn attention to its benefits and constraints as a flagship species. This study aimed to assess the potential benefits of a dual flagship strategy using both the giant panda and snow leopard, compared to an approach solely using the giant panda. We identified the number of potential beneficiary species based on their habitat overlap with the giant panda and snow leopard in Sichuan and Gansu, China. Subsequently, we examined public preferences for these two flagships and their influencing factors through questionnaire surveys within and outside China. The dual flagship strategy covered the habitats of more species and amplified existing protection for those species already benefiting from giant panda conservation efforts. The giant panda was commonly perceived as "Adorable", "Innocent", and "Rare", while perceptions of the snow leopard leaned towards "Mighty", "Mysterious", and "Rare". Though the giant panda is widely favored, the survey indicates a notable preference for snow leopards among a proportion of respondents. The dual flagship strategy offers expanded wildlife habitat coverage and benefits a broader range of species. Moreover, the combined appeal of the snow leopard and giant panda, each possessing unique charm and symbolism, holds the potential to garner broader societal interest and support. This study may serve as a reference for policy decisions in the Giant Panda National Park and other similar protected areas, optimizing conservation management and outreach initiatives for flagship species strategies. It may also benefit conservation strategies centered on other flagship species. [ABSTRACT FROM AUTHOR]

Loess landslides pose significant physical and environmental threats to communities of the Heifangtai Terrace in Gansu Province, China. Estimating potentially affected areas using dynamic numerical simulations provides a foundation for risk assessment and management. However, current studies often use rheological input parameters derived from the back analysis of an individual historical landslide, which requires that the potential landslide is quite similar to the historical landslide to guarantee the accuracy of predictions. We collected data from 20 loess flowslides that occurred from 2015 to 2019 in the Heifangtai Terrace. A friction model embedded in MassFlow was used to simulate run-out behavior. Accumulation depths at multiple control points were used as observation information. A multi-objective optimization back analysis was used to calibrate optimal rheological parameters for each flowslide, yielding a region-specific database of 20 datasets for statistical analyses. Finally, two flowslides that occurred in 2019 were employed as potential landslides to conduct probabilistic run-out predictions based on the database. These predictions agreed well with the observed accumulation areas in both cases, thus validating our method. This database will be useful for future landslide run-out predictions in this area. [ABSTRACT FROM AUTHOR]

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

Due to the huge losses caused by landslides, landslide deformation monitoring has become essential. The change of the tilting angle of the slope surface can reflect the instability process of the landslide. However, there are few studies on the relationship between the instability process of landslides and the tilting angle of the slope surface on the time scale. Therefore, there are even fewer cases in using the inclination angle of the landslide as an early warning method in practice. On August 24, 2018, a wireless tilt sensor network was deployed in the Heifangtai area of Gansu Province in China, to monitor the inclination state of loess landslides to study the relationship between the tilting angle of the slope surface and the landslide instability process. At 4 o'clock on October 5, 2019, the wireless tilt sensor network mentioned above detected a new loess fall landslide. Through the analysis of the monitoring data from the tilting sensor nodes named T3 and T4, we found that the tilting angle of the landslide surface in the pre-sliding stage has significant change compared with the stable stage, which directly verifies that the inclination data can be used for landslide warning. At the same time, we inputted the monitored inclination data into the inclination warning model proposed by Xie et al. (Landslides 17:301–312, 2020) for verification and found that the trend of inclination changes matches the model well. However, the forecast time error is large. Therefore, this paper optimizes the inclination early warning model proposed by Xie. Using the optimized early warning model, the predicted landslide occurrence time based on the data of the T3 node was 0.517 days, which was about 8 h later than the actual landslide that occurred on October 4, 2019, which is an improvement of forecasting. This case serves as an on-site direct evidence to further verify the feasibility of using the landslide body inclination state as a landslide monitoring and early warning parameter which can be promoted and used in practice. [ABSTRACT FROM AUTHOR]

The quantitative risk assessment of landslides requires the spatial impact probability of their run-out processes to conduct vulnerability assessments and prioritize mitigation measures. Hence, a general framework for three-dimensional (3D) probabilistic landslide run-out hazard evaluation is presented in this paper. In the proposed framework, a continuum mechanics concept-based dynamic numerical model, called Massflow, is used to conduct run-out analyses. A multi-observation Bayesian back analysis method with Markov chain Monte Carlo simulation is used to calibrate the uncertainties of the input parameters based on a past landslide event, which integrates the prior information, observation information, and model bias factor to yield posterior distributions of the input parameters. Then, the posterior distributions are used as inputs for a future event to estimate the exceedance probabilities of run-out intensities (i.e., maximum run-out height and velocity) on a 3D terrain and produce run-out hazard maps for quantitative landslide risk assessments purposes. To improve the computational efficiency of the proposed framework without losing accuracy, a multi-response Kriging-based surrogate model with a sequential adaptive sampling strategy is developed for both the back analysis and forward prediction. Finally, two sequential slides of landslide CJ#8 located in the Heifangtai terrain, Gansu, China are employed to test the performance of the proposed framework, with its advantages and potential drawbacks discussed in detail. Results show that the proposed framework can effectively calibrate the model input parameters to evaluate the run-out hazard of similar landslides, and the predicted results can be employed in landslide risk assessments and mitigations. • A 3D probabilistic landslide run-out hazard evaluation framework is proposed. • A multi-observation-based Bayesian framework is employed to calibrate input parameters. • Run-out intensity exceedance probability curves are calculated to produce probabilistic run-out hazard maps. • Multi-response Kriging-based surrogate models are used to significantly improve computational efficient. • Results can be employed for quantitative landslide risk assessment. [ABSTRACT FROM AUTHOR]

Zhouqu County in Gansu Province, Northwest China, is typically highly prone to landslides. On July 12, 2018, a landslide blocked the Bailong River near Zhouqu County, posing a serious threat to the life and property of local residents and the safety of infrastructure. Small baseline subset interferometry synthetic aperture radar technology (SBAS-InSAR) was adopted to identify the potential active landslides in the surrounding area of Zhouqu County, using ascending and descending orbit Sentinel-1 satellite images taken from October 2017 to December 2018. The surface deformation areas detected by SBAS-InSAR were verified by optical remote sensing image interpretation and field investigation, and a total of 23 active landslides were identified finally. The deformation characteristics of four typical landslides are analysed in detail using deformation velocity and rainfall data. It is found that the deformation velocity of landslides in this area is mainly affected by rainfall and there is a lag effect. The results can provide a reference for the prevention and control of landslide risk in Zhouqu County. [ABSTRACT FROM AUTHOR]