remote sensing Article Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods Jinmin Zhang 1,2, Wu Zhu 1,2,3,* , Yiqing Cheng 1,2 and Zhenhong Li 1,2,3 1 School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, China; [email protected] (J.Z.); [email protected] (Y.C.); [email protected] (Z.L.) 2 Big Data Center for Geosciences and Satellites, Chang’an University, Xi’an 710054, China 3 Key Laboratory of Western China’s Mineral Resources and Geological Engineering, Ministry of Education, Xi’an 710054, China * Correspondence: [email protected]; Tel.: +86-29-82339261 Abstract: Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan–Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan–Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi–Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014–2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along Citation: Zhang, J.; Zhu, W.; the Linzhi–Ya’an route were detected by integrating observed deformation, Google Earth optical Cheng, Y.; Li, Z. Landslide Detection images, and external geological data. The main factors controlling the spatial landslide distribution in the Linzhi–Ya’an Section along the were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in Sichuan–Tibet Railway Based on the elevation range of 3000–5000 m, the slope range of 10–40◦, and the aspect of northeast and east. InSAR and Hot Spot Analysis In the horizontal direction, landslides were concentrated near rivers, and were also closely related Methods. Remote Sens. 2021, 13, 3566. to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw https://doi.org/10.3390/rs13183566 weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. Academic Editor: This research provides scientific support for the construction and operation of the Linzhi–Ya’an Massimiliano Bordoni section of the Sichuan–Tibet Railway. Received: 31 July 2021 Keywords: landslide detection; Sichuan–Tibet Railway; interferometric synthetic aperture radar Accepted: 3 September 2021 (InSAR); hot spot analysis Published: 8 September 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in 1. Introduction published maps and institutional affil- The Sichuan–Tibet Railway, China, travels from eastern Chengdu, Sichuan to Lhasa, iations. Xizang, passing through the Dadu River, Yalong River, Jinsha River, Lancang River, and Nujiang River, and, finally, travelling along the Yarlung Zangbo River to Lhasa, with a total length of 1533.64 km. It is a bridge connecting Tibet and the central region of China, which demonstrates immense strategic significance for the development of the Tibetan Copyright: © 2021 by the authors. economy and maintenance of border stability [1]. Railway construction of the Chengdu– Licensee MDPI, Basel, Switzerland. Ya’an section and Linzhi–Lhasa section have been completed, while the Linzhi–Ya’an This article is an open access article section is under the project implementation stage. The planned Linzhi–Ya’an section is distributed under the terms and approximately 998.64 km long and passes through 21 tunnels, accounting for approximately conditions of the Creative Commons 80.4% of the total length. There are many active fault zones along the route with extremely Attribution (CC BY) license (https:// strong geological tectonic activities [2]. Moreover, the stratum lithology is complex, and creativecommons.org/licenses/by/ there exists considerable variation in the elevation of the terrain, which mostly belongs 4.0/). Remote Sens. 2021, 13, 3566. https://doi.org/10.3390/rs13183566 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 3566 2 of 20 to the alpine canyon landform. The large-scale and frequent occurrence of geological disasters, such as landslides and debris flows, have posed considerable challenges to the construction of the Sichuan–Tibet Railway [3]. Many researchers have studied the adverse geological problems in areas of the Sichuan–Tibet Railway and the challenges to railway construction from a geological perspective. For example, Guo et al. [4] analyzed the adverse geological conditions along the Sichuan–Tibet Railway and found that earthquakes, debris flows, landslides, and other geological disasters posed a potential threat to railway safety. Li et al. [5] studied the types, development characteristics, and spatial distribution of collapses and landslides along the entire Sichuan–Tibet Railway. Based on this study, the landslides along this area were divided into five types: seismic landslides, rainfall- triggered landslides, freeze–thaw landslides, structure and foot erosion landslides, as well as engineering landslides. They also concluded that the Baiyu–Jiangda and Bangda– Basu sections presented with highly developed landslides. Peng et al. [1] summarized 10 engineering geological problems encountered in the construction of the Sichuan–Tibet Railway, including complex structural deformation, fault activity, earthquakes, high-level and long-distance landslides, extra-large debris flows, and ice lake outbursts. However, current research mainly focuses on summarizing the types of geological disasters along the railway and on analyzing the influencing factors [1–5]. Few studies have focused on the inventory of landslides along the entire line, especially the distribution characteristics and triggering factors of landslides based on inventory maps. Such studies are of substantial significance not only for the construction of the Sichuan–Tibet Railway, but also for the future safety of residents and property along the line. Owing to the wide range, adverse environment, complex and changeable terrain, as well as the high level of some disaster points over the study area, the implementation of traditional disaster identification methods based on artificial and ground deployment is challenging. In recent years, remote sensing technologies have been developed rapidly and emerged as effective tools for surveying large-scale geological disasters. Among them, interferometric synthetic aperture radar (InSAR) is widely used for the detection of large-scale geological disasters, such as landslides, debris flows, and collapses, owing to its advantages of exhibiting all-weather, all-day, and high-precision properties [6–8]. Particularly, various synthetic aperture radar (SAR) satellites in different bands have been launched globally, including Cosmo/SkyMed and TerraSAR-X in the X-band, Sentinel- 1A/B and Radarsat-1/2 in the C-band, ALOS/PALSAR-1/2 in the L-band, and BIOMASS plan in the P-band, and the application of this technology has been further promoted in geological disaster detection and monitoring [9]. Some researchers have used the InSAR method to detect geological disasters along the Sichuan–Tibet Railway and adjacent areas; for example, Zhang et al. [10] successfully detected landslide hazards in the Dadu River Basin using the InSAR method. Liu et al. [11] combined InSAR and optical remote sensing to identify and analyze landslides in the Jinsha River Basin. Zhang et al. [12] conducted detection of landslide hazards in the Lancang River section of the Sichuan–Tibet Railway based on the small baseline subsets (SBAS)–InSAR method. However, most existing studies have only focused on the detection and monitoring of some typical landslides in partial segments of the Sichuan–Tibet Railway, and few studies have focused on the identification, inventory, and fine monitoring of geological disasters along the entire line. Particularly, large-scale landslide detection and spatial distribution characteristics have been explored using L-band SAR data with strong vegetation penetration ability [13]. Additionally, interpretation of large-scale deformation results observed from the InSAR method mostly relies on manual interpretation, which is time-consuming and laborious [14]. It is also easily affected by subjective factors, which lead to misjudgment and omitted judgement, and ultimately reduce the accuracy of the identification results. Based on the above-mentioned information, this study involved the collection of 164 ALOS/PALSAR-2 ascending SAR data covering the Linzhi–Ya’an section of the Sichuan– Tibet Railway from September 2014 to May 2020. We performed identification, cataloguing, monitoring, and determination of spatial distribution of potential geological disasters using Remote Sens. 2021, 13, 3566 3 of 20 the InSAR method. First, we explored and used targeted methods to correct errors in InSAR data processing, such as atmospheric error, geometric distortion, unwrapping error, and digital elevation model (DEM) error, and the accuracy of the InSAR method for surface deformation detection was effectively improved. Then, the hot spot analysis method was used to realize automatic,