Early identification of potential landslides for the Sichuan-Chongqing power grid based on optical remote sensing and SBAS-InSAR

doi:10.6046/zrzyyg.2022321

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Abstract

Power grid projects in mountainous regions have encountered numerous landslides in recent years, leading to collapsed transmission towers and power outages. Hence, early identification of potential landslides is crucial for ensuring the safety of power engineering. For this purpose, this study conducted early identification of potential landslides along the Sichuan-Chongqing power grid based on optical remote sensing and the small baseline subset (SBAS) - interferometric synthetic aperture radar (InSAR) technology. The interpretation of high-resolution optical remote sensing images revealed 28 potential landslide sites near the transmission towers along the power grid. Based on this, this study detected the study area’s surface deformation using the SBAS-InSAR technology, identifying 27 potential landslide sites. Except for 15 repeated results, the above two methods identified a total of 40 potential landslide sites. Finally, through field check and the qualitative analysis of deformation signs and stability, this study determined that seven potential landslide sites threaten the safety of transmission towers, with two of them presenting higher risks. These findings provide valuable guidance and references for the prevention and control of landslides along the Sichuan-Chongqing power grid.

Keywords landslide early identification optical remote sensing SBAS-InSAR Sichuan-Chongqing power grid

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P237

Issue Date: 21 December 2023

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	Huawei ZHAO
	Lin ZHOU
	Minglun TAN
	Minggao TANG
	Qinggang TONG
	Jiajun QIN
	Yuhui PENG

Cite this article:

Huawei ZHAO,Lin ZHOU,Minglun TAN, et al. Early identification of potential landslides for the Sichuan-Chongqing power grid based on optical remote sensing and SBAS-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(4): 264-272.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022321 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/264

Fig.1 Map of location of study area and Radar image coverage

Fig.2 Image of typical old landslide

Fig.3 Optical remote image of H37 landslide hidden danger point

Tab.1 Basic parameters of Sentinel-1A image

Fig.4 Distribution map of landslide hidden danger based on manual interpretation and identification of optical remote sensing

Fig.5 Maps of annual deformation rate of Sentinel-1A Ascent and distribution of landslide hidden danger dentification

Tab.2 Landslide hidden danger affecting normal operation of power grid

Fig.6 Optical satellite images of H08 landslide hidden danger point

Fig.7 Locally broken bedrock

Fig.8 Leading edge road crack

Fig.9 Map of deformation rate of H08 landslide hidden danger point

Fig.10 Time series deformation curve of characteristic points of H08 landslide hidden danger

Fig.11 Optical satellite images of H10 landslide hidden danger point

Fig.12 Deformation rate Map of H10 landslide hidden danger point

Fig.13 Time series deformation curves of characteristic points of H10 landslide hidden danger point in zone Ⅰ area

Fig.14 Time series deformation curves of characteristic points of H10 landslide hidden danger point in zone Ⅱ area

[1]	高霞霞, 李素敏, 陈朋弟, 等. 时序InSAR边坡滑移监测下输电杆塔失稳机理研究[J]. 贵州大学学报(自然科学版), 2022, 39(1):43-50.
[1]	Gao X X, Li S M, Chen P D, et al. Research on the instability mechanism of transmission towers under time-series InSAR slope slip monitoring[J]. Journal of Guizhou University(Natural Sciences), 2022, 39(1):43-50.
[2]	董继红, 马志刚, 梁京涛, 等. 基于时序InSAR技术的滑坡隐患识别对比研究[J]. 自然资源遥感, 2022, 34(3):73-81.doi:10.6046/zrzyyg.2021333.
[2]	Dong J H, Ma Z G, Liang J T, et al. A comparative study of the identification of hidden landslide hazards based on time series InSAR techniques[J]. Remote Sensing for Natural Resources, 2022, 34(3):73-81.doi:10.6046/zrzyyg.2021333.
[3]	丁华, 丁辉. 遥感技术在滑坡灾害解译中的应用——以陕西省延安市子长县为例[J]. 自然灾害学报, 2013, 22(2):229-233.
[3]	Ding H, Ding H. Application of remote sensing technology to interpretation of landslide disaster:A case study of Zichang County,Shanxi Province[J]. Journal of Natural Disasters, 2013, 22(2):229-233.
[4]	董秀军, 王栋, 冯涛. 无人机数字摄影测量技术在滑坡灾害调查中的应用研究[J]. 地质灾害与环境保护, 2019, 30(3):77-84.
[4]	Dong X J, Wang D, Feng T. Research on the application of unmanned aerial vehicle digital photogrammetry in landslide disaster investigation[J]. Journal of Geological Hazards and Environment Preservation, 2019, 30(3):77-84.
[5]	许强, 李为乐, 董秀军, 等. 四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究[J]. 岩石力学与工程学报, 2017, 36(11):2612-2628.
[5]	Xu Q, Li W L, Dong X J, et al. The Xinmocun landslide on June 24,2017 in Maoxian,Sichuan:characteristics and failure mechanism[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(11):2612-2628.
[6]	张文君. 滑坡灾害遥感动态特征监测及其预测分析研究[D]. 成都: 西南交通大学, 2007.
[6]	Zhang W J. Research on the remote sensing dynamic character monitoring and prediction analysis of landslide disaster[D]. Chengdu: Southwest Jiaotong University, 2007.
[7]	朱怡飞, 姚鑫, 姚磊华, 等. 基于InSAR和光学遥感的贵州鬃岭采煤滑坡识别与危险性评价[J]. 地质力学学报, 2022, 28(2):268-280.
[7]	Zhu Y F, Yao X, Yao L H, et al. Identification and risk assessment of coal mining-induced landslides in Guizhou Province by InSAR and optical remote sensing[J]. Journal of Geomechanics, 2022, 28(2):268-280.
[8]	Guo J M, Hu J Y, Li B, et al. Land Subsidence in Tianjin for 2015 to 2016 Revealed by the Analysis of Sentinel-1A with SBAS-InSAR[J]. Journal of Applied Remote Sensing, 2022, 11(2):026024. doi: 10.1117/1.JRS.11.026024 url: http://remotesensing.spiedigitallibrary.org/article.aspx?doi=10.1117/1.JRS.11.026024
[9]	陆会燕, 李为乐, 许强, 等. 光学遥感与InSAR结合的金沙江白格滑坡上下游滑坡隐患早期识别[J]. 武汉大学学报(信息科学版), 2019, 44(9):1342-1354.
[9]	Lu H Y, Li W L, Xu Q, et al. Early detection of landslides in the upstream and downstream areas of the baige landslide,the Jinsha River based on optical remote sensing and InSAR technologies[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9):1342-1354.
[10]	张路, 廖明生, 董杰, 等. 基于时间序列InSAR分析的西部山区滑坡灾害隐患早期识别——以四川丹巴为例[J]. 武汉大学学报(信息科学版), 2018, 43(12):2039-2049.
[10]	Zhang L, Liao M S, Dong J, et al. Early detection of landslide hazards in mountainous areas of West China using time series SAR interferometry:A case study of Danba, Sichuan[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):2039-2049.
[11]	廖明生, 张路, 史绪国, 等. 滑坡变形雷达遥感监测方法与实践[M]. 北京: 科学出版社, 2017.
[11]	Liao M S, Zhang L, Shi X G, et al. Remote sensing monitoring method and practice of landslide deformation radar[M]. Beijing: Science Press, 2017.
[12]	聂成顺. 基于InSAR和光学遥感的会东县滑坡隐患识别研究[D]. 北京: 中国地质大学(北京), 2021.
[12]	Nie C S. Recognition for potential landslides in Huidong County based on InSAR and optical remote sensing[D]. Beijing: China University of Geosciences(Beijing), 2021.
[13]	许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报(信息科学版), 2019, 44(7):957-966.
[13]	Xu Q, Dong X J, Li W L. Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7):957-966.
[14]	许强. 对地质灾害隐患早期识别相关问题的认识与思考[J]. 武汉大学学报(信息科学版), 2020, 45(11):1651-1659.
[14]	Xu Q. Understanding and consideration of related issues in early identification of potential geohazards[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11):1651-1659.
[15]	葛大庆. 地质灾害早期识别与监测预警中的综合遥感应用[J]. 城市与减灾, 2018(6):53-60.
[15]	Ge D Q. Comprehensive application of remote sensing application in early identification,monitoring and early warning of geological disasters[J]. City and Disaster Reduction, 2018(6):53-60.
[16]	葛大庆, 戴可人, 郭兆成, 等. 重大地质灾害隐患早期识别中综合遥感应用的思考与建议[J]. 武汉大学学报(信息科学版), 2019, 44(7):949-956.
[16]	Ge D Q, Dai K R, Guo Z C, et al. Early identification of serious geological hazards with integrated remote sensing technologies: Thoughts and recommendations[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7):949-956.
[17]	葛大庆, 郭兆成. 重大地质灾害隐患早期识别中综合遥感应用的思考[J]. 中国应急救援, 2019(1):10-14.
[17]	Ge D Q, Guo Z C. Thoughts on the application of comprehensive remote sensing in the early identification of hidden dangers of major geological disasters[J]. China Emergency Rescue, 2019(1):10-14.
[18]	沙永莲, 王晓文, 刘国祥, 等. 基于SBAS-InSAR的新疆哈密砂墩子煤田开采沉陷监测与反演[J]. 自然资源遥感, 2021, 33(3):194-201.doi:10.6046/zrzyyg.2020026.
[18]	Sha Y L, Wang X W, Liu G X, et al. SBAS-InSAR based monitoring and inversion of surface subsidence of the Shadunzi Coal Mine in Hami City,Xinjiang[J]. Remote Sensing for Natural Resources, 2021, 33(3):194-201.doi:10.6046/zrzyyg.2020026.
[19]	刘沛源, 常鸣, 武彬彬, 等. 基于SBAS-InSAR,技术的成汶高速汶川段滑坡易发区选线研究[J]. 地球科学, 2022, 47(6):2048-2057.
[19]	Liu P Y, Chang M, Wu B B, et al. Route selection of landslide prone area in Wenchuan section of Chengdu Wenchuan expressway based on SBAS-InSAR[J]. Earth Science, 2022, 47(6):2048-2057.
[20]	张家勇, 邹银先, 刘黔云, 等. 利用时序InSAR进行毕节市潜在滑坡识别与形变监测[J]. 测绘通报, 2022(6):121-124. doi: 10.13474/j.cnki.11-2246.2022.0183.
[20]	Zhang J Y, Zou Y X, Liu Q Y, et al. Potential landslides deformation monitoring in Bijie City with InSAR time series[J]. Bulletin of Surveying and Mapping, 2022(6):121-124. doi: 10.13474/j.cnki.11-2246.2022.0183.
[21]	卓宝熙. 工程地质遥感判释与应用[M]. 北京: 中国铁道出版社, 2011.
[21]	Zhuo B X. Remote sensing interpretation & application of geology engineering[M]. Beijing: China Railway Publishing House, 2011.
[22]	徐子兴, 季民, 张过, 等. 基于SBAS-InSAR技术和Logistic模型的矿区沉降动态预测方法[J]. 自然资源遥感, 2022, 34(2):20-29.doi:10.6046/zrzyyg.2021354.
[22]	Xu Z X, Ji M, Zhang G, et al. Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model[J]. Remote Sensing for Natural Resources, 2022, 34(2):20-29.doi:10.6046/zrzyyg.2021354.
[23]	Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11):2375-2383. doi: 10.1109/TGRS.2002.803792 url: http://ieeexplore.ieee.org/document/1166596/

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