Refined InSAR method for mapping and classification of active landslides in a high mountain region: Deqin County, southern Tibet Plateau, China
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Título: | Refined InSAR method for mapping and classification of active landslides in a high mountain region: Deqin County, southern Tibet Plateau, China |
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Autor/es: | Liu, Xiaojie | Zhao, Chaoying | Yin, Yueping | Tomás, Roberto | Zhang, Jing | Zhang, Qin | Wei, Yunjie | Wang, Meng | Lopez-Sanchez, Juan M. |
Grupo/s de investigación o GITE: | Ingeniería del Terreno y sus Estructuras (InTerEs) | Señales, Sistemas y Telecomunicación |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ingeniería Civil | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Landslides | Time series InSAR | Tropospheric delay correction | Tibetan plateau | Deqin County |
Fecha de publicación: | 8-feb-2024 |
Editor: | Elsevier |
Cita bibliográfica: | Remote Sensing of Environment. 2024, 304: 114030. https://doi.org/10.1016/j.rse.2024.114030 |
Resumen: | The mapping and classification of active landslides in high mountainous regions provide crucial information about the location and types of geohazards. Additionally, this process plays a vital role in ensuring the safety of the geological environments in mountainous towns. In this study, we presented a refined InSAR approach for mapping and classifying active landslide hazards in Deqin County, Tibetan Plateau, China. The study area is characterized by a high altitude and extremely rugged terrain. Consequently, conventional InSAR methods are limited in precisely estimating landslide deformation owing to severe atmospheric delays. To this end, we first propose a block-based linear model to correct tropospheric artifacts. This model considers the spatial variability of the atmosphere and provides an opportunity to accurately estimate heterogeneous atmospheric delays over high mountainous areas without any external data. Compared with the traditional global-window linear model and the GACOS approach, the new method demonstrated outstanding performance in reducing atmospheric artifacts. Second, based on the knowledge mapping of landslide types, we proposed a semi-automatic procedure to map and classify landslides using InSAR-derived displacements and auxiliary data (i.e., C-index and high resolution optical images). Our results obtained from ascending and escending Sentinel-1 images revealed, for the first time, that there were 317 active landslides in Deqin County between May 2017 and June 2021. Among these, 10.7% were associated with slide activity, 7.9% with fall deformation, and the majority (81.4%) with flow movement. These results were cross-verified and evaluated using an a priori inventory map obtained from the visual interpretation of optical images and geological field surveys. This study demonstrates that InSAR can accurately map and classify active landslides over difficult mountainous terrains, provided the associated phase errors are effectively restrained. |
Patrocinador/es: | This research was financially supported by the Natural Science Foundation of China (Grant No. 41929001) and National Key Research and Development Program of China No.2022YFC3004302). This research was also supported by a Chinese Scholarship Council student ship awarded to Xiaojie Liu (Ref. 202006560031), the Science Foun dation of Gansu Province (Nos. 23JRRA830 and 23ZDFA007), and the ESA-MOST China DRAGON-5 project (ref. 59339). |
URI: | http://hdl.handle.net/10045/140806 |
ISSN: | 0034-4257 (Print) | 1879-0704 (Online) |
DOI: | 10.1016/j.rse.2024.114030 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2024 Elsevier Inc. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.rse.2024.114030 |
Aparece en las colecciones: | INV - INTERES - Artículos de Revistas INV - SST - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Liu_etal_2024_RemoteSensEnviron_final.pdf | Versión final (acceso restringido) | 57,6 MB | Adobe PDF | Abrir Solicitar una copia |
Liu_etal_2024_RemoteSensEnviron_preprint.pdf | Preprint (acceso abierto) | 3,94 MB | Adobe PDF | Abrir Vista previa |
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