Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images
On 8 August 2017, Jiuzhaigou earthquake, magnitude 7.0, hit northern Sichuan, China. As the earthquake-stricken area is located in the mountainous region with forest and low residential density, the main damage is to vegetation and roads by earthquake-triggered landslides. In this study, the core ar...
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2020-01-01
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Series: | Advances in Civil Engineering |
Online Access: | http://dx.doi.org/10.1155/2020/8828385 |
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author | Xiao Fu Qing Zhu Chao Liu Naiwen Li Wenhua Zhuang Zhengli Yang Heng Lu Min Tang |
author_facet | Xiao Fu Qing Zhu Chao Liu Naiwen Li Wenhua Zhuang Zhengli Yang Heng Lu Min Tang |
author_sort | Xiao Fu |
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description | On 8 August 2017, Jiuzhaigou earthquake, magnitude 7.0, hit northern Sichuan, China. As the earthquake-stricken area is located in the mountainous region with forest and low residential density, the main damage is to vegetation and roads by earthquake-triggered landslides. In this study, the core area of Jiuzhaigou natural reserve, one of the highest seismic intensity zones, is selected. The landslides are extracted by examining vegetation changes from the preearthquake and postearthquake images using the Normalized Difference Vegetation Index (NDVI) and are verified by slope. As most road damage in the mountainous region could be attributed to the landslides nearby, the impacts of landslide on road are studied based on spatial analysis and are used to infer occluded road damage. Then, a knowledge-based method for postearthquake road detection and road capacity assessment from preearthquake road data and postearthquake high-resolution remote sensing imagery is proposed, as well as the quantitative road capacity assessment indicators to classify the road grades. This method is evaluated using the Beijing-2 (BJ-2) satellite images over the study area acquired on 28 April and 9 August. Compared with visual interpretation results, the extraction accuracy reached 90% for landslides and 85% for postearthquake roads, indicating that the approaches are effective and promising for quick response to devastating earthquake in similar circumstances. |
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institution | Kabale University |
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series | Advances in Civil Engineering |
spelling | doaj-art-3150836887eb4a4eb220e58649f1616b2025-02-03T01:04:28ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/88283858828385Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing ImagesXiao Fu0Qing Zhu1Chao Liu2Naiwen Li3Wenhua Zhuang4Zhengli Yang5Heng Lu6Min Tang7Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaFaculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Hydraulic and Hydroelectric Engineering, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, ChinaChina Railway Eryuan Engineering Group Co., Ltd., Chengdu 610031, ChinaOn 8 August 2017, Jiuzhaigou earthquake, magnitude 7.0, hit northern Sichuan, China. As the earthquake-stricken area is located in the mountainous region with forest and low residential density, the main damage is to vegetation and roads by earthquake-triggered landslides. In this study, the core area of Jiuzhaigou natural reserve, one of the highest seismic intensity zones, is selected. The landslides are extracted by examining vegetation changes from the preearthquake and postearthquake images using the Normalized Difference Vegetation Index (NDVI) and are verified by slope. As most road damage in the mountainous region could be attributed to the landslides nearby, the impacts of landslide on road are studied based on spatial analysis and are used to infer occluded road damage. Then, a knowledge-based method for postearthquake road detection and road capacity assessment from preearthquake road data and postearthquake high-resolution remote sensing imagery is proposed, as well as the quantitative road capacity assessment indicators to classify the road grades. This method is evaluated using the Beijing-2 (BJ-2) satellite images over the study area acquired on 28 April and 9 August. Compared with visual interpretation results, the extraction accuracy reached 90% for landslides and 85% for postearthquake roads, indicating that the approaches are effective and promising for quick response to devastating earthquake in similar circumstances.http://dx.doi.org/10.1155/2020/8828385 |
spellingShingle | Xiao Fu Qing Zhu Chao Liu Naiwen Li Wenhua Zhuang Zhengli Yang Heng Lu Min Tang Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images Advances in Civil Engineering |
title | Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images |
title_full | Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images |
title_fullStr | Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images |
title_full_unstemmed | Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images |
title_short | Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images |
title_sort | estimation of landslides and road capacity after august 8 2017 ms7 0 jiuzhaigou earthquake using high resolution remote sensing images |
url | http://dx.doi.org/10.1155/2020/8828385 |
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