The eastern Himalayan syntaxis (EHS) region is characterized by steep topography, strong tectonic activity, and strong disaster activity, and the disaster activity has a temporal correlation with the change of seasonal climate factors. Different from other disaster-prone areas, the disasters in the EHS area are more destructive and more susceptible to changes in climate factors. However, previous studies on disaster susceptibility in the region mainly focused on geohazard samples, regional topography, and geological conditions and seldom considered the characteristics of disaster activity and the characteristics of seasonal temperature and rainfall changes, which often led to an underestimation of disaster susceptibility in this region. Therefore, in this study, SBAS-InSAR technology and the GEE (Google Earth Engine) platform were used to identify 317 geological disasters and construct the spatial–temporal motion field and spatial-temporal background information of disasters in the last 5 years. Based on the random forest and optimized evaluation data model, the disaster susceptibility in the EHS area was quantitatively evaluated. The AUC value of the model was 0.89, indicating that the results have high reliability. The results showed that the high-susceptibility areas were mainly distributed in the riverbank and high-altitude areas. The SHAP model revealed that the factors of temperature, precipitation, slope, and elevation in the EHS area had a great negative influence on the disaster. The quantitative results of the influence of seasonal temperature and precipitation on disaster susceptibility showed that the area of susceptibility in summer was significantly higher than that in winter, and the area of high and very high susceptibility increased by about 13

The Jiali-Chayu fault, situated on the eastern side of the eastern Himalayan syntaxis, is the southeastern margin of the large strike-slip fault zone of the Jiali Fault. The study of the distribution and activity within this fault zone is imperative for a comprehensive understanding of the tectonic movement patterns in the southeastern Tibetan Plateau. Previous studies have established that the kinematic characteristic of the Jiali-Chayu fault diverges significantly from that of other segments within the Jiali fault. Nonetheless, the current tectonic characteristics, including the slip sense, slip rate, and geometric deformation of this fault, are still not well resolved, leading to divergent interpretations regarding its contemporary activity intensity. This paper introduced an optimized time-series InSAR method with phase compensation designed for regions characterized by low coherence and exhibiting slow deformation. Using Sentinel-1 SAR data from both ascending and descending orbits spanning the period between 2017 and 2022, we successfully derived deformation rates for the middle part of the Jiali-Chayu fault at a spatial resolution of 150 m. The slip and dip rates of active faults are determined by considering the fault movement rates from two different observation angles, in conjunction with strike angle and the assumed dip angle of the fault. The results show that the deformation rates of the three branches are very different, with F2-1 and F2-2 exhibiting notable activity, while other areas exhibit relatively weaker activity. The strike-slip rates for F2-1 and F2-2 faults range between 3.6 and 5.3 mm/a and 3.05 to 5.13 mm/a, respectively, while their respective dip-slip rates fall within the range of 1.1–2.7 mm/a and 2.99–5.02 mm/a. In accordance with the fault slip directions, we classify the F2-1 fault as a sinistral (left-lateral) strike-slip fault and the F2-2 fault as a dextral (right-lateral) strike-slip fault. This study addresses a gap in remote sensing methods for detecting active fault activity in this region, providing a systematic foundation for identifying weak activity characteristics within the fault zone. Graphical Abstract

The difficult, time-consuming, and imbalanced acquisition of landslide inventories in complex and heterogeneous large areas often results in limited predictive performance for most statistical landslide spatial prediction methods. Although partial transfer learning methods have produced reliable predictive results and successfully implemented knowledge transfer between data-rich and data-scarce areas, most of these methods generally only extract inadequate environmental features and lack the ability to interpret when, what, and how to effectively transfer knowledge to other regions with limited data. In this paper, a heterogeneous transfer learning method considering feature representations and environmental consistency, which features robustness, similarity, and transferability, is proposed for landslide spatial prediction. Specifically, we trained a stacked autoencoder (SAE) to extract more nonlinear features among environmental factors, and added an environmental similarity criterion to the transfer adaptation boosting (TrAdaBoost) algorithm to minimize feature dissimilarities and avoid negative transfer in different scenarios. To evaluate the robustness of the proposed method, we first selected two target areas (Lushan County and Luding County, China) and a source area (Wenchuan County, China) as case study areas. Then, we directly combined the source area and target area as an additional dataset without considering transfer learning to validate the significance and necessity of the proposed method. The area under the receiver operating characteristic curve (AUC) of the two target regions for the proposed method were 0.920 and 0.972, respectively, which were greater than those of the traditional TrAdaBoost (0.909 and 0.969, respectively), SAE (0.790 and 0.937, respectively), and random forest (0.915 and 0.966, respectively) methods. Furthermore, the AUC values of the SAE (0.851 and 0.900) and random forest (0.890 and 0.935) models based on the expanded datasets were also lower than those of the proposed method. Therefore, the experimental results show that the proposed method can be generalized well due to its efficient utilization and high adaptability. Moreover, the proposed method can not only be applied to emergency rescue and disaster prevention but can also offer a promising way to improve landslide predictions of models with incomplete landslide inventories.

Many debris-covered glaciers are widely distributed on the Qinghai–Tibet Plateau. Glaciers are important freshwater resources and cause disasters such as glacier collapse and landslides. Therefore, it is of great significance to monitor the movement characteristics of large active glaciers and analyze the process of mass migration, which may cause serious threats and damage to roads and people living in surrounding areas. In this study, we chose a glacier with strong activity in Lulang County, Tibet, as the study area. The complete 4-year time series deformation of the glacier was estimated by using an improved small-baseline subset InSAR (SBAS-InSAR) technique based on the ascending and descending Sentinel-1 datasets. Then, the three-dimensional time series deformation field of the glacier was obtained by using the 3D decomposition technique. Furthermore, the three-dimensional movement of the glacier and its material migration process were analyzed. The results showed that the velocities of the Lulang glacier in horizontal and vertical directions were up to 8.0 m/year and 0.45 m/year, and these were basically consistent with the movement rate calculated from the historical optical images. Debris on both sides of the slope accumulated in the channel after slipping, and the material loss of the three provenances reached 6–9 × 103 m3/year, while the volume of the glacier also decreased by about 76 × 103 m3/year due to snow melting and evaporation. The correlation between the precipitation, temperature, and surface velocity suggests that glacier velocity has a clear association with them, and the activity of glaciers is linked to climate change. Therefore, in the context of global warming, the glacier movement speed will gradually increase with the annual increase in temperature, resulting in debris flow disasters in the future summer high-temperature period.

Landslide susceptibility mapping (LSM) comprehensively evaluates the spatial probability of landslide occurrence by using different environmental factors. However, most of the evaluation methods ignore the dynamic characteristic factors of landslides, which makes it difficult to obtain reliable prediction results. Taking the upper reaches of the Jinsha River as the study area, this article introduces the deformation data into the landslide characteristic model and proposes an improved landslide susceptibility evaluation method. Four kinds of landslide susceptibility machine learning models were constructed by collecting 20 landslide related factors. The prediction accuracy of machine learning models is compared, and the performance of different models and the improvement of model performance by deformation information are evaluated. The results show that the performance of Random Forest and XGBoost model is better than SVM and logistic regression model. The prediction accuracy of Random Forest and XGBoost model is improved obviously after InSAR deformation is introduced. 96.9 and 93.19% of landslide areas were reasonably classified as high or very high risk levels. Compared with the calculation result of traditional model, the proportion of high and very high risk pixels in landslide area is increased by 2.97 and 1.13%, respectively. In addition, the percentage of high and very high risk areas in the susceptibility evaluation area increased from 15.45 to 16.23% and 18.73 to 21.89%, respectively. The accuracy of Random Forest and XGBoost models increased from 0.793 to 0.878 and 0.776 to 0.812, respectively, and the AUC increased by 0.9 and 1.7%, respectively. The SHAP and traditional feature importance analysis reveals that rainfall, aspect, temperature and NDVI are the main influencing factors of landslide in the upper reaches of the Jinsha River.

<p>Large paleolandslides are developed in the upper reaches of Jinsha River, which seriously threaten the safety of nearby residents and engineering facilities. It is important to study the movement characteristics of these landslides. In this work, we inspect the deformation characteristics of a rapid landsliding area along the Jinsha River by using multi-temporal remote sensing, and analyzed its future development. Surface deformations and damage features between January 2016 and October 2020 were obtained using multi-temporal InSAR and multi-temporal correlations of optical images, respectively. Deformation and failure signs obtained from the field investigation were highly consistent. Results showed that cumulative deformation of the landsliding area is more than 50 cm, and the landsliding area is undergoing an accelerated deformation stage. The external rainfall condition is an important factor controlling the deformation. The increase of rainfall will accelerate the deformation of slope. The geological conditions of the slope itself affect the deformation of landslide. Due to fault development and groundwater enrichment, slopes are more likely to slide along weak structural plane. The Jinsha River continuously scours the concave bank of the slope, causing local collapses and forming local free surfaces. Numerical simulation results show that once the landsliding area fails, the landslide body may form a 4 km long dammed lake, and the water level could rise about 200 m.</p>

河流对岸坡的侵蚀作用是滑坡失稳的重要因素之一,特别是在中国黄土地区.因此,探讨河流水位的季节性变化对黄土边坡稳定性的影响规律,对早期黄土滑坡灾害预警具有重要作用.论文以甘肃省天水市清泉村滑坡为例,分析5年内沿岸边坡形变速率与季节性水位变化之间的关系.基于SBAS-InSAR技术获取滑坡时序形变信息,借助MNDWI与DEM获取边坡底部时序水位信息,并结合GPM降雨数据进行相关性分析,研究发现随着降雨增多、河流水位的上涨,清泉村滑坡的形变速率明显增大,在夏季时滑坡形变量增长较快,并且边坡形变相较于水位季节变化具有一定的滞后性;经相关性分析,得出水位与滑坡形变的相关性系数为0.46,降雨与滑坡形变的相关性系数为0.39,表明清泉村滑坡的形变速率与水位变化更相关.因此,河流的季节性变化对滑坡形变速率具有一定影响,进而造成河流对沿岸滑坡的侵蚀作用呈现出季节性变化.研究揭示的黄土滑坡与河流水位的季节性变化关系,对揭示河岸边坡的演化过程、滑坡识别与灾害防治具有重要意义.

Traditional case-based reasoning methods overlook non-stationary spatial drivers of geographical events such as heterogeneity, dependence, and accumulation in case representation, and directly obtain the solution of the most similar cases in case reuse instead of considering the interference of fake similar cases to eliminate the contin-gency of reasoning, which leads to poor interpretations and low efficiency decisions in complex and heteroge-neous geographical environments. This study proposes an improved spatial case-based reasoning (SCBR) considering multiple spatial drivers to overcome above problems and uses landslide susceptibility mapping as an example. Specifically, these spatial drivers were captured, extracted, and integrated into case representation by using geographic self-organizing mapping algorithm, spatial statistic, and spatial adjacent matrix, respectively. Additionally, the K-nearest neighbor method as case retrieval was introduced to retrieve the K similar cases based on the local and global similarity reasoning. Finally, the Gaussian process regression as case reuse method was generated to landslide susceptibility index under the assumption that K similar cases follows Gaussian distri-bution. Our experimental results show that the precision, F1, recall, and kappa of the proposed SCBR method are 0.974, 0.976, 0.979, and 0.953 which are higher than those of the traditional case-based reasoning (0.931, 0.941, 0.953, and 0.881), long short-term memory (0.951, 0.933, 0.915, and 0.870), and extreme gradient boosting decision tree (0.963, 0.967, 0.972, and 0.945), respectively. In general, the novel approach with better predictive performance can help decision makers to develop policies that reduce the loess of landslides and apply to similar geological events.

热诱导裂纹在石窟表面岩体的损伤破坏过程中扮演着极其重要的角色.石窟岩体表面温度变化导致的热诱导裂纹扩展是研究石窟岩体损伤的关键,而目前尚未有针对石窟岩体热诱导裂纹损伤问题的定量评价方法.为了厘清石窟温度变化与热诱导裂纹扩展的联系,采用红外热成像监测与常规监测并行的方法,分析甘肃庆阳北石窟寺的岩体表面温度变化特征,建立基于矿物分布改进的热诱导裂纹评价方法,定量评价石窟岩体热诱导裂纹扩展损伤特征,结合地基SAR监测技术对评价结果进行反演验证.发现红外热成像技术识别出的北石窟寺三处高热诱导裂纹扩展区域与地基SAR监测下的显著形变区域高度重合.红外热成像技术与地基SAR监测技术的创新性结合,可应用于评估长时间序列下石窟崖壁表面热诱导裂纹损伤的时空特征,对石窟保护及风化病害防治具有一定的实际意义.

针对天水市大型或严重破坏的黄土滑坡调查与分析较少,潜在活动蠕变型滑坡的空间分布与未来发展趋势不明的问题,该文基于InSAR遥感技术对天水市活动性滑坡进行了解译识别、成因分析与调查核实,利用SBAS-InSAR计算的时序形变信息判断滑坡未来的发展趋势.研究区内发现了103处活动性灾害,其中69处为黄土滑坡、34处地面沉降区.结合地质条件和地貌特征分析表明,地质条件、人类活动、河流沟谷以及降雨是诱发滑坡产生的主要因素,而且滑坡的分布与斜坡坡度、坡向以及坡体与水系距离有很强的相关性.实验结果证明,InSAR技术可以有效地识别和监测黄土滑坡,可为区域灾害防治提供科学依据.

Many ancient landslides in the upper reaches of the Jinsha River seriously threaten the safety of residents on both sides of the river. The river erosion and groundwater infiltration have greatly reduced the stability of the ancient landslides along the Jinsha River and revived many large landslides. Studying their deformation characteristics and mechanisms and predicting possible failure processes are significant to the safety of residents and hydropower projects. We used SBAS-InSAR and three-dimensional decomposition techniques in our study. Our results showed that the trailing edge and middle part of the landslide have rapidly deformed. The maximum vertical annual displacement rate was 12 cm/a period from July 2017 to July 2019. Correlation analysis showed that creep deformation is closely related to the river damming of the Baige landslide events and that the rising river level was an important factor in the resurrection and accelerated destruction of the Xiaomojiu landslide. As a result, we predicted the possible failure process of the Xiaomojiu landslide, which might have lasted 80 s and eventually formed a landslide deposit with a height of about 150 m, a length of approximately 1500 m, and an average width of 450 m. Our results provide data references for displacement monitoring and instability risk simulation of large landslides along the Jinsha River.

The rock mass along the Jinsha River is relatively broken under complex geological action. Many ancient landslides were distributed along the Jinsha River in Gongjue County, which is very dangerous under the action of gravity, tectonic stress and river erosion. Efficient and accurate identification and monitoring of landslides is important for disaster monitoring and early warning. Interferometric synthetic aperture radar (InSAR) technology has been proved to be an effective technology for landslide hazard identification and mapping. However, great uncertainty inevitably exists due to the single deformation observation method, resulting in wrong judgment during the process of landslide detection. Therefore, to address the uncertainties arising from single observations, a cross-comparison method is put forward using SBAS-InSAR (small baseline subset InSAR) and PS-InSAR (permanent scatterers InSAR) technology. Comparative analysis of the spatial complementarity of interference points and temporal deformation refined the deformation characteristics and verified the reliability of the InSAR results, aiding in the comprehensive identification and further mapping of landslides. Landslides along the Jinsha River in Gongjue County were studied in this paper. Firstly, 14 landslides with a total area of 20 km(2) were identified by using two time-series InSAR methods. Then, the deformation characteristics of these landslides were validated by UAV (unmanned aerial vehicle) images, multiresource remote sensing data and field investigation. Further, the precipitation data were introduced to analyze the temporal deformation characteristics of two large landslides. Lastly, the influence of fault activity on landslide formation is further discussed. Our results demonstrate that the cross-comparison of the time-series InSAR method can effectively verify the accuracy of landslide identification.

Land reclamation in the Yan'an New District (YND) on the Chinese Loess Plateau is one of the largest earthworks projects in the world, involving the excavation of loess from ridges and the deposition of the material in adjacent valleys, flattening an area of more than 78 km2. It can take multiple years for the landscape to adjust to the new topography after the earthworks are completed, with subsidence in the fill areas and uplift in the areas of excavation. Understanding the pattern and extent of this differential vertical movement has great importance for ensuring the structural integrity of any infrastructure built on the site. We therefore studied the spatial deformation field of the YND from January 2015 to December 2018 using satellite synthetic aperture radar interferometry (InSAR) technology. Persistent scatterers (PS) and distributed scatterers (DS) were combined in a twotier network to overcome problems with temporal decorrelation of the InSAR phase signals. The derived deformation field was validated with in-situ ground leveling and GNSS measurements, and interpreted using a forward geophysical model. The results indicate that our approach provides a more detailed understanding of the deformation than conventional PSI and SBAS methods. Subsidence of up to 87 mm/yr occurred over about 4.4 km2 of the fill region, and uplift of up to 26 mm/yr in the excavated areas. There is a strong linear relationship between these displacements and prior topographic elevation change. The primary cause of subsidence in fill areas is compaction of remoulded loess. Instantaneous elastic and gradual poroelastic deformation are the main causes of uplift in excavated areas.

Active landslides have real direct threat to human settlement safety, engineering facilities and ecological environment. Due to the lack of measured deformation information, the general susceptibility mapping of active landslides could not be accurate enough to identify potential landslides, and effective landslide susceptibility mapping requires time series deformation characteristics. Therefore, a new evaluation method, combined with deformation characteristics acquired from SBAS-InSAR technique, is proposed to provide a susceptibility mapping of potential landslides along a section of Sichuan-Tibet railway and Jinsha River. In addition, the spatial characteristics of active landslides and their difference in geomorphic characteristics from inactive landslides are analyzed quantitatively. Results showed that compared with the original method, the mean value of susceptibility combined with deformation information in landslide areas is increased by 0.028, which increased the identification of high-risk areas. In this way, active landslides can be easily distinguished from inactive landslides, the mean difference of their susceptibility was 0.61, and the former needs more attention. Spatial characteristics of landslides indicate that they are closely related to topographic factors, Jinsha River and fault, which mostly belongs to medium-sized landslide. Quantitative statistical analysis indicated that compared with inactive landslides, the deformation characteristics of active landslides are more obvious, such as larger erosion area, crack length and continuous deformation. Moreover, the river curvature at the foot of active landslides is larger, which means that river scour affects the occurrence of landslides.

Underground mining in coal mining areas will induce large-scale, large-gradient surface deformation, threatening the safety of people’s lives and property in nearby areas. Due to mining-related subsidence is characterized by fast displacement and high nonlinearity, monitoring this process by using traditional and single interferometric synthetic aperture radar (InSAR) technology is very challenging, and it cannot accurately and quantitatively calculate the deformation of the mining area. In this paper, we proposed a new method that combines both multitemporal consecutive D-InSAR and offset tracking technology to construct a complete deformation field of the coal mining area. Taking into account the accuracy of multitemporal consecutive D-InSAR in calculating small deformation areas and the ability of offset tracking to measure large deformation areas, we utilized their respective advantages to extract the surface influence range and applied an adaptive spatial filtering method to integrate their respective results for inversion of the deformation field. 12 ascending high-resolution TerraSAR-X images (2 m) from September 3, 2018, to October 26, 2019, and 39 descending Sentinel-1 TOPS SAR images from August 5, 2018, to November 4, 2019, in the Ordos Coalfield located at Inner Mongolia, China, were utilized to obtain the whole subsidence field of the working faces F6211 and F6207 during the 454-day mining period. The GPS monitoring station located in the direction of the mining surface is used to verify the accuracy of the above method; at the same time, to a certain extent, the difference between the unmanned aerial vehicle’s DSM data acquired after coal mining and the Shuttle Radar Topography Mission (STRM) DEM can qualitatively verify the accuracy of the results. Our results show that the results of TerraSAR are basically consistent with the deformation trend of GPS data, and that of Sentinel-1 have large errors compared with GPS. The maximum central subsidence reaches ~12 m in the working face F6211 and ~4 m in the working face F6207. In the working face F6207, the good agreement between GPS and TerraSAR results indicated that the method above using high-resolution SAR data could be reliable for monitoring the large deformation area in the mining field.

Territory of Three-parallel-rivers composing by Nu River, Lantsang River and Jinsha River in Tibet Plateau is a key zone for the study of the geological environment and nature resource development of Tibet Plateau. The investigation of active landslides in full coverage, the study of their development and distribution rule not only play an important role in slope disaster reduction and prevention, but also in geological science research. Take the 179 000 km2 study area of the Pan-three-river parallel catchment as an example. It is demonstrated the workflow and key technologies of the whole process: InSAR big data processing → landslide interpretation → development law analysis → key sections analysis, obtained active landslide data under 4 000 asl of medium-small scale in study area. The research results showed that, 1) The existing SAR data sources, InSAR technology and interpretation methods could meet the needs of small and medium-sized large-area landslide identification, and the time was ripe for complete InSAR recognitions of landslides in China; 2) The "phase resonance enhanced InSAR" (PRE-InSAR) technology designed for the rapid calculation of large redundant SAR observations could eliminate most of the adverse interference conditions, highlight the location, range and activity intensity of landslides, and effectively completed the InSAR data processing suitable for large-area landslide identification, which had the potential of popularization in technology; 3) Geomorphology and optical remote sensing images, and considering the formation and lithology, geohazard mechanism, triggering factors, combing 10 classes geomorphic development locations; 4) A total of 904 active landslides under 4 000 asl were interpreted, and they were mainly distributed in eight sections of three-river, namely, Wangbudui—Paoluo, Songxiong, Zhongzunong of Jinsha River; Basu of Nu River; Chaya, Chaya—Deqin and Huangdeng Reservoir of Lantsang River; 5) Active landslides are mainly the revival of ancient (old) landslides. It was revealed that the main environmental factors that had great influences on active landslides were river erosion, topographic slope, stratigraphic lithology, rainfall and temperature in order, while the spatial distribution of active structure and earthquake was not significant. The mutual feed between landslides and human activity was a major characteristic of the development of landslides in this region; 6) InSAR identification results more comprehensive and objective in reflecting the rules of the natural development of landslide, deepened to the understanding of the three-river region geohazard, could provide important support for the active landslide prevention & control of Tibet Plateau, but InSAR observation and interpretation had multiple solutions of active landslides. The next step is to develop a standardized InSAR identification workflow and conduct multi-source data fusion observation.

贵州贞丰县某煤矿开采煤层以向斜缓倾的三叠系上统火把冲组(T3h)为主,与贵州省大部分煤矿开采的背斜反倾煤层不同,其采矿活动诱发的地面沉降和滑坡风险亦表现出不同的变形破坏模式(背斜反倾煤层易诱发倾倒崩塌、顺层缓倾煤层易诱发地面塌陷与滑坡).论文利用升、降轨观测的共15期3 m空间分辨率L波段PALSAR-2 SAR为数据源,开展了多期地表变形D-InSAR测量,确定出变形发生的位置、范围与滞后时间.经实地调查验证,InSAR解算结果较好地吻合了矿区开采范围和地表破坏情况,证实了InSAR在煤矿区识别时序性地表形变的准确性.进而分解计算了地表三维变形,并通过与地下开采范围和过程的相关性分析,深化了对该地区缓倾煤层地下开采诱发的顺层滑坡变形模式的认识:(1)InSAR可以识别计算出采矿区地表变形的范围与沉降量,矿区变形在干涉影像中表现为以采空区地表为中心向四周扩散的圆环状变形条纹;(2)地表变形区域覆盖地下采空区上方及附近地表区域,根据地表变形情况与地下采空区范围计算出该地区上山边界角约70°、下山边界角约58°;(3)地下采空与地表沉降变形存在约30 d的时间滞后;(4)顺层地下采空引发的地表水平移动方向受地层产状、地表坡向共同作用,水平向为沿层面的顺层滑移与向沉降中心汇聚的合成运动结果;(5)沿层面的顺层滑移与地表坡度因素叠加造成采空区地表上山侧岩石受拉产生拉裂缝,下山侧则易产生塌陷坑及裂缝.

The Batang—Mangkang section of the G4218 highway is located in the transition zone from the Jinsha River Valley to the plateau, and facing with strong neotectonic activities, broken rock masses and frequent geological disasters. Therefore, the construction and maintenance of the highway in this region are of huge difficulties. Traditional ground geological survey methods face many difficulties in highway route selection, disaster assessment and stability analysis, etc. Combining the optical remote sensing interpretation of geological hazard points with deformation observation by InSAR technology, it is expected that the geological hazard points in this region can be investigated quickly, accurately and efficiently, and reveal their development and distribution rules of geological disaster points. Under the special geological conditions of the high mountains and valleys of the Qinghai–Tibet Plateau, the common types of geological disasters in the region were summarized, and the method of integrated highway remote sensing identification was put forward based on the study of the characteristics of regional disasters and remote sensing technology. Using this method, we carried out disaster surveys on the Batang—Mangkang section, with full knowledge of optical remote sensing visual interpretation technology and InSAR technology, supplemented by field geological survey, GIS spatial analysis, engineering geological analogy, etc. The conclusions of this article are as follows:1) A total of 670 geological disasters were interpreted by optical visual remote sensing in the study area, and InSAR technology combined with four kinds of SAR data interpreted 220 active geological disasters; 2) The development rules of different types of geological disasters in the study area varied greatly with the change of topographic features, geological conditions and geological disasters and other influencing factors. There were differences in spatial distribution and formation lithology of geologic hazard using the optical remote sensing or InSAR technology; 3) According to the comparative analysis based on the field work, it was concluded that the interpretation results of optical remote sensing and InSAR had a certain relationship with the interpretation methods, imaging conditions of the images and the activity of landslide. The two methods cannot be used for mutual inspection directly; 4) The use of comprehensive remote sensing technology was universal in the highway construction of the high mountain valley of the Qinghai–Tibet Plateau. It made full use of the complementarity of optical remote sensing interpretation technology and InSAR deformation observation technology. On the basis of saving time and cost, this method can have a more comprehensive and accurate understanding of the development of regional geological disasters.

Coal mined-out area, formed in mine production, usually induces a certain degree of ground subsidence and collapse. In many areas, the background information of coal seam mining is unclear, which brings trouble to the ecological restoration of the mining area and the reuse of land resources. Therefore, the calculation of the underground mining range is particularly important and can play a monitoring role in the cross-border mining of coal mines. This paper uses Japan's PALSAR-2 satellite data with a data resolution of about 3 m, and a total of 16 scenes in two observation directions of ascending and descending. Using InSAR technology, the multi-phase surface deformation monitoring is carried out in the mine goaf of Zhenfeng County, Guizhou Province. Based on the geological conditions, underground mining conditions, and field investigations in the study area, this paper calculates the relationship between mining subsidence parameters and time lags in the study area. The calculation combines the surface deformation of the known coal mines and the underground mining information. Then using the above-mentioned parameters and surface deformation information, this paper inverts an abandoned coal mine near the area and calculates the underground goaf range and mining time. The results are as follows. The study area is dominated by slow-sloping sandstone and mudstone. The coal mining strike influence angle is 83°, the uphill influence angle is 75°and the downhill influence angle is 80°. Inversion calculation gives the mining range of 380 m×150 m underground and the mining time of each area. FLAC3D numerical simulation calculation of the mined area of the abandoned coal mine is used to verify the accuracy of the range of the mined area. Compared with the calculation results of InSAR, the surface deformation trend of the mining area obtained by FLAC3D is more consistent, and the root meant square error of the trend and tendency is about 6 cm.

The stability of reservoir bank slopes is always of great concern in hydropower construction. After impounding, some unstable slopes may fail, leading to landslides with significant displacements. This work investigated the vertical and horizontal displacements of the Yizicun landslide before and after impoundment of the Xiluodu Reservoir on the Jinshajiang River, southwestern China using InSAR technique and data from ALOS PALSAR, ENVISAT–ASAR, TerraSAR-X and Sentinel-1. The purpose was to explore the relationships between the ground surface deformation and the landslide movement mode, and to make further analysis on its deformation characteristics. Combining with field investigations, the analysis of InSAR reveals that the Yizicun landslide was a push type before impoundment and is a pull type after impoundment. This landslide is largely a whole slump with multiple slip surfaces. After the impoundment of the reservoir, the landslide boundary had a tendency to spread, and its rear edge has extended backward about 20 m. More fissures and secondary landslides appeared on the southern boundary than those in the north. The horizontal movement changed from southwestward to westward, and the vertical deformation increased. The current stability of the landslide might be subjected to a combined action of ground water levels and precipitation intensity. The study also indicates that InSAR has many inimitable advantages in the study of reservoir landslides, such as backtracking for stability of bank slopes before impounding, three-dimensional monitoring of active areas, landslide movement mode analysis and so on. Thus, it is a suitable method to efficiently analyze landslide deformation characteristics before and after reservoir impoundment, which is of great significant for detecting and monitoring reservoir landslides.