Main Processes for OVS-1A &OVS-1B: From Manufacturer to User

Commercial remote sensing has boosted a new revolution in traditional processing chain. During the development of OVS-1A and OVS-1B, we construct the main processing pipeline for ground and calibration system. Since these two satellites utilize colorful video imaging pattern, the underlying video stabilization and color adjustment is vital for end user. Besides that, a full explanation is given for researchers to shed light on how to promote the imagery quality from manufacturing satellite camera to generate video products. From processing system, the demo cases demonstrate its potential to satisfy end user. Our team also releases the possible improvement for video imaging satellite in the coming future.

Landslides Triggered by the 2020 Qiaojia M_(w)5.1 Earthquake, Yunnan, China: Distribution, Influence Factors and Tectonic Significance

On May 18, 2020, an M_(w)5.1 earthquake occurred in Qiaojia County, Yunnan Province, China. This moderate-sized event triggered massive coseismic landslides, resulting in some damage. In this work, through visual interpretation of high-resolution(0.8–2 m) Gaofen satellite images before and after the earthquake, 167 landslides were delineated, 18 of which were inspected in the field. Using the landslide number density(LND) and landslide area percentage(LAP), we characterized the spatial distribution of these landslides, and analyzed their possible influence factors and tectonic significance. The results show that these landslides are distributed mostly in the NW-SE direction, roughly parallel to the long axis of seismic intensity zones and the strike of the Xiaohe-Baogunao fault(XBF). The LND and LAP decrease with increasing distances to the fault and from the epicenter to fault ends of the XBF. These permit to suggest that the seismogenic fault of the Qiaojia earthquake is likely a hidden branch of the XBF. All of the landslides induced by this event occurred in the region with the seismic intensity of six degrees or greater of the 2014 M_(w)6.2 Ludian earthquake. Therefore, it was inferred that the 2020 Qiaojia earthquake was probably the subsequent release of accumulated elastic strain after the 2014 Ludian earthquake in a same tectonic stress regime.

A Heterogeneous Sampling Strategy to Model Earthquake‑Triggered Landslides

Regional modeling of landslide hazards is an essential tool for the assessment and management of risk in mountain environments.Previous studies that have focused on modeling earthquake-triggered landslides report high prediction accuracies.However,it is common to use a validation strategy with an equal number of landslide and non-landslide samples,scattered homogeneously across the study area.Consequently,there are overestimations in the epicenter area,and the spatial pattern of modeled locations does not agree well with real events.In order to improve landslide hazard mapping,we proposed a spatially heterogeneous non-landslide sampling strategy by considering local ratios of landslide to non-landslide area.Coseismic landslides triggered by the 2008 Wenchuan Earthquake on the eastern Tibetan Plateau were used as an example.To assess the performance of the new strategy,we trained two random forest models that shared the same hyperparameters.The frst was trained using samples from the new heterogeneous strategy,and the second used the traditional approach.In each case the spatial match between modeled and measured(interpreted)landslides was examined by scatterplot,with a 2 km-by-2 km fshnet.Although the traditional approach achieved higher AUC_(ROC)(0.95)accuracy than the proposed one(0.85),the coefcient of determination(R^(2))for the new strategy(0.88)was much higher than for the traditional strategy(0.55).Our results indicate that the proposed strategy outperforms the traditional one when comparing against landslide inventory data.Our work demonstrates that higher prediction accuracies in landslide hazard modeling may be deceptive,and validation of the modeled spatial pattern should be prioritized.The proposed method may also be used to improve the mapping of precipitation-induced landslides.Application of the proposed strategy could beneft precise assessment of landslide risks in mountain environments.