Using Persistent Scatterer Interferometry for Post-Earthquake Landslide Susceptibility Mapping in Jiuzhaigou  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China University of Chinese Academy of Sciences, Beijing 100049, China University of Target Microwave Properties (LAMP), Zhongke Academy of Satellite Application in Deqing (DASA), Deqing, Huzhou 313200, China China 12334  
[PERSON]  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China University of Chinese Academy of Sciences, Beijing 100049, China University of Target Microwave Properties (LAMP), Zhongke Academy  
there caused even more damage than in other mountainous areas [5]. [PERSON] et al. analyzed co-seismic landslides and updated the geologic hidden trouble database of landslides in Jiuzhaigou [6]. [PERSON] et al. investigated landslides using remote sensing data after the earthquake [7]. These landslide databases made it possible for the analysis of landslide susceptibility. Landslide susceptibility maps (LSMs) are important tools for earthquake prevention and disaster reduction, and they can help decision-makers with infrastructure plans [8; 9; 10]. Therefore, assessing landslide susceptibility in this area and updating the distribution map of landslide susceptibility in earthquake areas are critical for developing earthquake relief and related policies. LSMs use geomorphology, environmental factors, and landslide investigation to predict landslide occurrence. LSM models can be constructed using several methodologies, including machine learning and statistical methods, which are frequently performed using Geographic Information Systems (GIS) [11; 12]. In recent years, many machine learning and statistical methods had been developed and applied to LSM, including the weights of evidence, support vector machine algorithms, decision trees, and convolutional neural networks [13]. These LSM models are usually considered time-invariant because of the absence of multitemporal data and the invariance of the influencing factors [14]. However, landslides are uncertain and their susceptibility shows dynamic changes with natural conditions, especially after earthquakes and during the rainy season [15]. The increase in earthquake-induced landslide susceptibility cannot currently be updated over time, which reduces the effectiveness and reliability of the LSM. Relevant scholars had taken the following measures to maintain the timeliness of landslide susceptibility. Some studies used physically based models to update landslide susceptibility, while others upgraded the process with the rainfall model [16; 17]. Such methods, however, were often limited to specific landslides. In addition, these updating methods must continue to obtain detailed ground data, making it difficult to complete a wide range of landslide susceptibility monitoring [18]. Remote sensing data have the advantages of high accuracy, wide range, and repeatable monitoring. The use of remote sensing data for regional landslide susceptibility studies could address the limitations of physical models [19; 20]. Spatial synthetic aperture radar (SAR) has been utilized extensively for geohazard investigations including landslides, debris flows, and ground subsidence [21; 22; 23]. In recent years, many studies have applied persistent scatterer InSAR (PSInSAR) and Small baselines subset InSAR (SBAS) to monitor various landslides all over the world, including in Mao County, the Italian mountains, and Yellowstone National Park [24; 25; 26]. PS-InSAR has been proven to monitor landslide deformation, which could help to improve landslide monitoring and landslide susceptibility assessments. As mentioned above, InSAR technology allows for the rapid monitoring of surface deformation in post-earthquake areas, and the resulting changes in surface deformation before and after the earthquake can be used to rapidly update seismic landslide susceptibility maps.