{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T21:52:13Z","timestamp":1770328333177,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T00:00:00Z","timestamp":1623888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and technology innovation Commission","doi-asserted-by":"publisher","award":["KQTD20180410161218820"],"award-info":[{"award-number":["KQTD20180410161218820"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development (R&amp;D) Project","award":["2016YFC0800105-01"],"award-info":[{"award-number":["2016YFC0800105-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spaceborne interferometric synthetic aperture radar (InSAR) methodology has been widely successfully applied to measure urban surface micro slow subsidence. However, the accuracy is still limited by the spatial resolution of currently operating SAR systems and the lacking precision of geolocation of the respective scatters. In this context, high-precision urban models, as provided by the active laser point cloud methodology through light detection and ranging (LiDAR) techniques, can assist in improving the geolocation quality of InSAR-derived permanent scatters (PS) and provide the precise contour of buildings for hazard analysis. This paper proposes to integrate InSAR and LiDAR technologies for an improved detailed analysis of subsidence levels and a hazard assessment for buildings in the urban environment. By the use of LiDAR data, most building contours in the main subsidence area were extracted and SAR positioning of buildings via PS points was refined more precisely. The workflow for the proposed method includes the monitoring of land subsidence by the TS-InSAR technique, the geolocation improvement of InSAR-derived PS, and building contour extraction by LiDAR data. Furthermore, a reasonable hazard assessment system of land subsidence was developed. Significant vertical subsidence of \u221240 to 12 mm per year was detected by the analysis of multisensor SAR images. The land subsidence rates in the Shenzhen District obviously follow certain spatial patterns. Most stable areas are located in the middle and northeast of Shenzhen except for some areas in Houhai, the Qianhai Bay, and the Wankeyuncheng. An additional hazard assessment of land subsidence reveals that the subsidence of buildings is mainly caused by the construction of new buildings and some by underground activities. The research results of this paper can provide a useful synoptic reference for urban planning and help reducing land subsidence in Shenzhen.<\/jats:p>","DOI":"10.3390\/rs13122366","type":"journal-article","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T11:20:26Z","timestamp":1623928826000},"page":"2366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Integration of InSAR and LiDAR Technologies for a Detailed Urban Subsidence and Hazard Assessment in Shenzhen, China"],"prefix":"10.3390","volume":"13","author":[{"given":"Yufang","family":"He","sequence":"first","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"},{"name":"Shenzhen Municipal Planning, & Land Realestate Information Center (Shenzhen GeoSpatial Information Center), Shenzhen 518034, China"}]},{"given":"Guochang","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}]},{"given":"Hermann","family":"Kaufmann","sequence":"additional","affiliation":[{"name":"School of Space Science and Physics, Shandong University at Weihai, Weihai 264209, China"}]},{"given":"Jingtao","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhen Municipal Planning, & Land Realestate Information Center (Shenzhen GeoSpatial Information Center), Shenzhen 518034, China"}]},{"given":"Hua","family":"Ma","sequence":"additional","affiliation":[{"name":"Shenzhen Lijian Skyeye-Laser Technology Limited Company, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5300-8060","authenticated-orcid":false,"given":"Tong","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Space Science and Applied Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hanssen, R.F. (2001). Radar Interferometry: Data Interpretation and Error Analysis, Kluwer Academic Publishers.","DOI":"10.1007\/0-306-47633-9"},{"key":"ref_2","unstructured":"Tantianuparp, P., Shi, X., Liao, M., Zhang, L., and Balz, T. (2012, January 25\u201329). Landslide monitoring in the Three Gorges area using D-InSAR and PS-InSAR. Proceedings of the Dragon-2&3 Symposium, Beijing, China."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9542","DOI":"10.3390\/rs70809542","article-title":"Extracting vertical displacement rates in Shanghai (China) with multi-platform SAR images","volume":"7","author":"Dai","year":"2015","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.enggeo.2021.106033","article-title":"Integration of Sentinel-1 and ALOS\/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China","volume":"284","author":"Liu","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112400","DOI":"10.1016\/j.rse.2021.112400","article-title":"InSAR monitoring of creeping landslides in mountainous regions: A case study in Eldorado National Forest, California","volume":"258","author":"Kang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Shao, X., Ma, S., Xu, C., Zhang, P., Wen, B., Tian, Y., Zhou, Q., and Cui, Y. (2019). Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake. Remote Sens., 11.","DOI":"10.3390\/rs11080978"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lazecky, M., Perissin, D., Sousa, J., Bakon, M., Hlavacova, I., and Real, N. (April, January 30). Potential of Satellite InSAR Techniques for Monitoring Bridge Deformations. Proceedings of the 2015 Joint Urban Remote Sensing Event (JURSE), Lausanne, Switzerland.","DOI":"10.1109\/JURSE.2015.7120506"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yang, K., Yan, L., Huang, G., Chen, C., and Wu, Z. (2016). Monitoring Building Deformation with InSAR: Experiments and Validation. Sensors, 16.","DOI":"10.3390\/s16122182"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2006.01.023","article-title":"A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data","volume":"102","author":"Casu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.tecto.2011.10.013","article-title":"Recent advances in SAR interferometry time series analysis for measuring crustal deformation","volume":"514\u2013517","author":"Hooper","year":"2012","journal-title":"Tectonophysics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8610","DOI":"10.3390\/rs70708610","article-title":"Land subsidence, ground fissures and buried faults: InSAR monitoring of Ciudad Guzm\u00e1n (Jalisco, Mexico)","volume":"7","author":"Brunori","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, G., Zhang, Y., Zeng, R., Yang, Z., Chen, X., Zhao, F., and Meng, X. (2018). Detection of land subsidence associated with land creation and rapid urbanization in the chinese loess plateau using time series InSAR: A case study of Lanzhou new district. Remote Sens., 10.","DOI":"10.3390\/rs10020270"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent scatterers in SAR interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s00190-015-0883-4","article-title":"High-precision positioning of radar scatterers","volume":"90","author":"Dheenathayalan","year":"2016","journal-title":"J. Geod."},{"key":"ref_15","unstructured":"Haala, N., and Brenner, C. (1997, January 17\u201319). Generation of 3D City Models from Airborne Laser Scanning Data. Proceedings of the EARSEL Workshop on LIDAR Remote Sensing of Land and Sea, Tailinn, Estonia."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xu, H., Cheng, L., Li, M., Yajun, W., Xia, N., Chen, Y., and Tang, Y. (2016). Three-dimensional reconstruction of building roofs from airborne LiDAR data based on a layer connection and smoothness strategy. Remote Sens., 8.","DOI":"10.3390\/rs8050415"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"8107","DOI":"10.3390\/rs6098107","article-title":"3D building roof modeling by optimizing primitive\u2019s parameters using constraints from LiDAR Data and aerial imagery","volume":"6","author":"Wuming","year":"2014","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/TGRS.2010.2050487","article-title":"Very high resolution spaceborne SAR tomography in urban environment","volume":"48","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1109\/TGRS.2014.2348859","article-title":"Precise three-dimensional stereo localization of corner reflectors and persistent scatterers with TerraSAR-X","volume":"53","author":"Gisinger","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TGRS.2010.2060264","article-title":"Imaging geodesy\u2014Toward centimeter-level ranging accuracy with TerraSAR-X","volume":"49","author":"Eineder","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hu, F., Van Leijen, F., Chang, L., Wu, J., and Hanssen, R. (2019). Monitoring deformation along railway systems combining multi-temporal InSAR and LiDAR data. Remote Sens., 11.","DOI":"10.3390\/rs11192298"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chang, L., Sakpal, N., Oude Elberink, S., and Wang, H. (2020). Railway infrastructure classification and instability identification using Sentinel-1 SAR and laser scanning data. Sensors, 20.","DOI":"10.3390\/s20247108"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105317","DOI":"10.1016\/j.catena.2021.105317","article-title":"Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques","volume":"203","author":"Novellino","year":"2021","journal-title":"CATENA"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Miele, P., Di Napoli, M., Guerriero, L., Ramondini, M., Sellers, C., Annibali Corona, M., and Di Martire, D. (2021). Landslide Awareness System (LAwS) to Increase the Resilience and Safety of Transport Infrastructure: The Case Study of Pan-American Highway (Cuenca\u2013Ecuador). Remote Sens., 13.","DOI":"10.3390\/rs13081564"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"132","DOI":"10.4236\/ars.2017.62010","article-title":"Risk assessment of land subsidence in Kathmandu Valley, Nepal, using Remote Sensing and GIS","volume":"6","author":"Bhattarai","year":"2017","journal-title":"Adv. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/s12665-009-0024-6","article-title":"Risk assessment of land subsidence at Tianjin coastal area in China","volume":"59","author":"Hu","year":"2009","journal-title":"Environ. Earth Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1007\/s00024-009-0491-4","article-title":"Analysis of ground deformation detected using the SBAS-DInSAR technique in Umbria, Central Italy","volume":"166","author":"Guzzetti","year":"2009","journal-title":"Pure Appl. Geophys."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gee, D., Bateson, L., Sowter, A., Grebby, S., Novellino, A., Cigna, F., Marsh, S., Banton, C., and Wyatt, L. (2017). Ground motion in areas of abandoned mining: Application of the intermittent SBAS (ISBAS) to the Northumberland and Durham Coalfield, UK. Geosciences, 7.","DOI":"10.3390\/geosciences7030085"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1080\/01431161.2015.1134846","article-title":"Advanced InSAR techniques for deformation studies and for simulating the PS-assisted calibration procedure of Sentinel-1 data: Case study from Thessaloniki (Greece), based on the Envisat\/ASAR archive","volume":"37","author":"Costantini","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"1","article-title":"Mexico City subsidence observed with persistent scatterer InSAR","volume":"13","author":"Dixon","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2202","DOI":"10.1109\/36.868878","article-title":"Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry","volume":"38","author":"Ferretti","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3209","DOI":"10.1109\/TGRS.2009.2028797","article-title":"Estimating Spatiotemporal Ground Deformation with Improved Persistent-Scatterer Radar Interferometry","volume":"47","author":"Liu","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2016.10.037","article-title":"Monitoring land subsidence in the southern part of the lower Liaohe plain, China with a multi-track PS-InSAR technique","volume":"188","author":"Sun","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/36.45752","article-title":"The generation of SAR layover and shadow maps from digital elevation models","volume":"28","author":"Kropatsch","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"1063","article-title":"A method for extracting the SAR shadow from InSAR coherence","volume":"30","author":"Wang","year":"2005","journal-title":"J. Wuhan Univ. (Inf. Sci. Ed.)"},{"key":"ref_36","first-page":"1137","article-title":"Method for layover regions detection based on interferometric synthetic aperture radar","volume":"XLII-2","author":"Natijne","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.5194\/isprs-archives-XLII-2-1137-2018","article-title":"Massive linking of PS-InSAR deformations to a national airborne laser point cloud","volume":"42","author":"Natijne","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_38","first-page":"302","article-title":"Analysis of TIN-structure parameter spaces in airborne laser scanner data for 3-D building model generation","volume":"35","author":"Hofmann","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xu, B., Feng, G., Li, Z., Wang, Q., Wang, C., and Xie, R. (2016). Coastal subsidence monitoring associated with land reclamation using the Point Target based SBAS-InSAR method: A case study of Shenzhen, China. Remote Sens., 8.","DOI":"10.3390\/rs8080652"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Hu, B., Chen, J., and Zhang, X. (2019). Monitoring the land subsidence area in a coastal urban area with InSAR and GNSS. Sensors, 19.","DOI":"10.3390\/s19143181"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lanari, R., Berardino, P., Bonano, M., Casu, F., Luca, C., Elefante, S., Fusco, A., Manunta, M., Manzo, M., and Ojha, C. (2015, January 26\u201331). Sentinel-1 results: SBAS-DInSAR processing chain developments and land subsidence analysis. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium IGASS, Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326405"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/S0013-7952(02)00195-3","article-title":"Monitoring landslides and tectonic motions with the Permanent Scatterers Technique","volume":"68","author":"Colesanti","year":"2003","journal-title":"Eng. Geol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1080\/01431161003698419","article-title":"Mapping changes in coastline geomorphic features using Landsat TM and ETM+\u2009imagery: Examples in southeastern Brazil","volume":"32","author":"Kawakubo","year":"2011","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2366\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:17:35Z","timestamp":1760163455000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/12\/2366"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,17]]},"references-count":43,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13122366"],"URL":"https:\/\/doi.org\/10.3390\/rs13122366","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,17]]}}}