The ancient landslide has endured long-term slope evolution which results in its complicated material and special rock-soil properties. The risk of ancient landslide reactivation is substantially increasing due to the increase of intensified human engineering activities and the frequency of extreme weather events. Many ancient landslides have been reactivated all over the world and led to serious fatalities and severe damage to many important engineering facilities such as transportation and hydropower engineering projects. On the basis of the analysis of the research situation about the ancient landslides at home and abroad, the main research advances were summarized including the regional developing laws and recognizing of the ancient landslides, the mechanics properties of ancient landslide body and related sliding zone, reactivation mechanism of ancient landslides, reactivating process and modeling analysis of ancient landslides, early recognization of ancient landslide reactivation, etc. To meet the demands of disaster prevention and reduction, three key scientific issues were put forward to be solved: ①automaticaly establishing the methodology and identification criterions for recognition of ancient landslide; ②revealing the reactivation mechanism of ancient landslide based on a new strength theory; ③establishing the early rapid recognition method and predictive model for ancient landslide reactivation. Solving the above mentioned scientific theory and methodology will facilitate the planning and site selection of major projects as well as the disaster prevention and reduction in ancient landslide developing areas. 相似文献
The three-dimensional ionospheric tomography (3DCIT) algorithm based on Global Navigation Satellite System (GNSS) observations have been developed into an effective tool for ionospheric monitoring in recent years. However, because the rays that come into or come out from the side of the inversion region cannot be used, the distribution of the rays in the edge and bottom part of the inversion region is scarce and the electron density cannot be effectively improved in the inversion process. We present a three-dimensional tomography algorithm with side rays (3DCIT-SR) applying the side rays to the inversion. The partial slant total electron content (STEC) of side rays in the inversion region is obtained based on the NeQuick2 model and GNSS-STEC. The simulation experiment results show that the algorithm can effectively improve the distribution of GNSS rays in the inversion region. Meanwhile, the iteration accuracy has also been significantly improved. After the same number of iterations, the iterative results of 3DCIT-SR are closer to the truth than 3DCIT, in particular, the inversion of the edge regions is improved noticeably. The GNSS data of the International GNSS Service (IGS) stations in Europe are used to perform real data experiments, and the inversion results show that the electron density profiles of 3DCIT-SR are closer to the ionosonde measurements. The accuracy improvement of 3DCIT-SR is up to 56.3% while the improvement is more obvious during the magnetic storm compared to the case of a calm ionospheric state . 相似文献
Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr–1. 相似文献
The accurate mapping of urban housing prices at a fine scale is essential to policymaking and urban studies, such as adjusting economic factors and determining reasonable levels of residential subsidies. Previous studies focus mainly on housing price analysis at a macro scale, without fine‐scale study due to a lack of available data and effective models. By integrating a convolutional neural network for united mining (UMCNN) and random forest (RF), this study proposes an effective deep‐learning‐based framework for fusing multi‐source geospatial data, including high spatial resolution (HSR) remotely sensed imagery and several types of social media data, and maps urban housing prices at a very fine scale. With the collected housing price data from China's biggest online real estate market, we produced the spatial distribution of housing prices at a spatial resolution of 5 m in Shenzhen, China. By comparing with eight other multi‐source data mining techniques, the UMCNN obtained the highest housing price simulation accuracy (Pearson R = 0.922, OA = 85.82%). The results also demonstrated a complex spatial heterogeneity inside Shenzhen's housing price distribution. In future studies, we will work continuously on housing price policymaking and residential issues by including additional sources of spatial data. 相似文献
The runoff and sediment load of the Loess Plateau have changed significantly due to the implementation of soil and water conservation measures since the 1970s. However, the effects of soil and water conservation measures on hydrological extremes have rarely been considered. In this study, we investigated the variations in hydrological extremes and flood processes during different periods in the Yanhe River Basin (a tributary of the Loess Plateau) based on the daily mean runoff and 117 flood event data from 1956 to 2013. The study periods were divided into reference period (1956–1969), engineering measures period (1970–1995), and biological control measures period (1996–2013) according to the change points of the annual streamflow and the actual human activity in the basin. The results of the hydrological high extremes (HF1max, HF3max, HF7max) exhibit a decreasing trend (P?<?0.01), whereas the hydrological low extremes (HBF1min, HBF3min, HBF7min) show an increasing trend during 1956–2013. Compared with the hydrological extremes during the reference period, the hydrological high extremes increased during the engineering measures period at low (<?15%) and high frequency (>?80%), whereas decreased during the biological control measures period at almost all frequencies. The hydrological low extremes generally increased during both the engineering measures and biological control measures periods, particularly during the latter period. At the flood event scale, most flood event indices in connection with the runoff and sediment during the engineering measures period were significantly higher than those during the biological control measures period. The above results indicate that the ability to withstand hydrological extremes for the biological control measures was greater than that for the engineering measures in the studied basin. This work reveals the effects of different soil and water conservation measures on hydrological extremes in a typical basin of the Loess Plateau and hence can provide a useful reference for regional soil erosion control and disaster prevention policy-making.