{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T04:19:28Z","timestamp":1784693968873,"version":"3.55.0"},"reference-count":19,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,11]],"date-time":"2018-02-11T00:00:00Z","timestamp":1518307200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Triggered by earthquakes, rainfall, or anthropogenic activities, landslides represent widespread and problematic geohazards worldwide. In recent years, multiple remote sensing techniques, including synthetic aperture radar, optical, and light detection and ranging measurements from spaceborne, airborne, and ground-based platforms, have been widely applied for the analysis of landslide processes. Current techniques include landslide detection, inventory mapping, surface deformation monitoring, trigger factor analysis and mechanism inversion. In addition, landslide susceptibility modelling, hazard assessment, and risk evaluation can be further analyzed using a synergic fusion of multiple remote sensing data and other factors affecting landslides. We summarize the 19 articles collected in this special issue of Remote Sensing of Landslide, in the terms of data, methods and applications used in the papers.<\/jats:p>","DOI":"10.3390\/rs10020279","type":"journal-article","created":{"date-parts":[[2018,2,12]],"date-time":"2018-02-12T10:50:38Z","timestamp":1518432638000},"page":"279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":204,"title":["Remote Sensing of Landslides\u2014A Review"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5730-9602","authenticated-orcid":false,"given":"Chaoying","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Geology Engineering and Geomatics, Chang\u2019an University, No. 126, Yanta Road, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9181-1818","authenticated-orcid":false,"given":"Zhong","family":"Lu","sequence":"additional","affiliation":[{"name":"Huffington Department of Earth Sciences, Southern Methodist University, P.O. Box 750395, Dallas, TX 75275, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bozzano, F., Mazzanti, P., Perissin, D., Rocca, A., Pari, P., and Discenza, M. (2017). Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis. Remote Sens., 9.","DOI":"10.3390\/rs9030267"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kang, Y., Zhao, C., Zhang, Q., Lu, Z., and Li, B. (2017). Application of InSAR Techniques to an Analysis of the Guanling Landslide. Remote Sens., 9.","DOI":"10.3390\/rs9101046"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Qi, S., Zou, Y., Wu, F., Yan, C., Fan, J., Zang, M., Zhang, S., and Wang, R. (2017). A Recognition and Geological Model of a Deep-Seated Ancient Landslide at a Reservoir under Construction. Remote Sens., 9.","DOI":"10.3390\/rs9040383"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Schl\u00f6gel, R., Thiebes, B., Mulas, M., Cuozzo, G., Notarnicola, C., Schneiderbauer, S., Crespi, M., Mazzoni, A., Mair, V., and Corsini, A. (2017). Multi-Temporal X-Band Radar Interferometry Using Corner Reflectors: Application and Validation at the Corvara Landslide (Dolomites, Italy). Remote Sens., 9.","DOI":"10.3390\/rs9070739"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yang, Z., Li, Z., Zhu, J., Preusse, A., Yi, H., Hu, J., Feng, G., and Papst, M. (2017). Retrieving 3-D Large Displacements of Mining Areas from a Single Amplitude Pair of SAR Using Offset Tracking. Remote Sens., 9.","DOI":"10.3390\/rs9040338"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Du, Y., Xu, Q., Zhang, L., Feng, G., Li, Z., Chen, R., and Lin, C. (2017). Recent landslide movement in Tsaoling, Taiwan tracked by TerraSAR-X\/TanDEM-X DEM time series. Remote Sens., 9.","DOI":"10.3390\/rs9040353"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mondini, A. (2017). Measures of Spatial Autocorrelation Changes in Multitemporal SAR Images for Event Landslides Detection. Remote Sens., 9.","DOI":"10.3390\/rs9060554"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bru, G., Gonz\u00e1lez, P.J., Mateos, R.M., Rold\u00e1n, F., Herrera, G., B\u00e9jar-Pizarro, M., and Fern\u00e1ndez, J. (2017). A-DInSAR Monitoring of Landslide and Subsidence Activity: A Case of Urban Damage in Arcos de la Frontera, Spain. Remote Sens., 9.","DOI":"10.3390\/rs9080787"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bardi, F., Raspini, F., Frodella, W., Lombardi, L., Nocentini, M., Gigli, G., Morelli, S., Corsini, A., and Casagli, N. (2017). Monitoring the Rapid-Moving Reactivation of Earth Flows by Means of GB-InSAR: The April 2013 Capriglio Landslide (Northern Appennines, Italy). Remote Sens., 9.","DOI":"10.3390\/rs9020165"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Golovko, D., Roessner, S., Behling, R., Wetzel, H., and Kleinschmit, B. (2017). Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan. Remote Sens., 9.","DOI":"10.3390\/rs9090943"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Chen, T., Trinder, J.C., and Niu, R. (2017). Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China. Remote Sens., 9.","DOI":"10.3390\/rs9040333"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sun, W., Tian, Y., Mu, X., Zhai, J., Gao, P., and Zhao, G. (2017). Loess Landslide Inventory Map Based on GF-1 Satellite Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040314"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bivic, R.L., Allemand, P., Quiquerez, A., and Delacourt, C. (2017). Potential and Limitation of SPOT-5 Ortho-Image Correlation to Investigate the Cinematics of Landslides: The Example of \u201cMare \u00e0 Poule d\u2019Eau\u201d (R\u00e9union, France). Remote Sens., 9.","DOI":"10.3390\/rs9020106"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kamps, M., Bouten, W., and Seijmonsbergen, A. (2017). LiDAR and Orthophoto Synergy to optimize Object-Based Landscape Change: Analysis of an Active Landslide. Remote Sens., 9.","DOI":"10.3390\/rs9080805"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Luo, L., Ma, W., Zhang, Z., Zhuang, Y., Zhang, Y., Yang, J., Cao, X., Liang, S., and Mu, Y. (2017). Freeze\/Thaw-Induced Deformation Monitoring and Assessment of the Slope in Permafrost Based on Terrestrial Laser Scanner and GNSS. Remote Sens., 9.","DOI":"10.3390\/rs9030198"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ren, Z., Zhang, Z., and Yin, J. (2017). Erosion Associated with Seismically-Induced Landslides in the Middle Longmen Shan Region, Eastern Tibetan Plateau, China. Remote Sens., 9.","DOI":"10.3390\/rs9080864"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ahmed, B., and Dewan, A. (2017). Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh. Remote Sens., 9.","DOI":"10.3390\/rs9040304"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wang, Y., Niu, R., and Peng, L. (2017). Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China. Remote Sens., 9.","DOI":"10.3390\/rs9090938"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Park, H.J., Jang, J.Y., and Lee, J.H. (2017). Physically Based Susceptibility Assessment of Rainfall-Induced Shallow Landslides Using a Fuzzy Point Estimate Method. Remote Sens., 9.","DOI":"10.3390\/rs9050487"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:54:39Z","timestamp":1760194479000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/2\/279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,11]]},"references-count":19,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["rs10020279"],"URL":"https:\/\/doi.org\/10.3390\/rs10020279","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,11]]}}}