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China","award":["42130709"],"award-info":[{"award-number":["42130709"]}]},{"name":"Geological Survey Project of China","award":["20JR5RA223"],"award-info":[{"award-number":["20JR5RA223"]}]},{"name":"Geological Survey Project of China","award":["18JR2JA006"],"award-info":[{"award-number":["18JR2JA006"]}]},{"name":"Geological Survey Project of China","award":["lzujbky-2021-ey05"],"award-info":[{"award-number":["lzujbky-2021-ey05"]}]},{"name":"Geological Survey Project of China","award":["DD20189270"],"award-info":[{"award-number":["DD20189270"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide risk assessment is important for risk management and loss\u2013damage reduction. Herein, we assessed landslide susceptibility, hazard, and risk in the urban area of Yan\u2019an City, which is located on the Loess Plateau of China and affected by many loess landslides. Based on 1841 slope units mapped in the study area, a random forest machine learning classifier and eight environmental factors influencing landslides were used for a landslide susceptibility assessment. In addition, differential synthetic aperture radar interferometry (DInSAR) technology was used for a hazard assessment. The accuracy of the random forest is 0.903 and the area under the receiver operating characteristics (ROC) curve is 0.96. The results show that 16% and 22% of the slope units were classified as being at very high and high-susceptibility levels for landslides, respectively, whereas 16% and 24% of the slope units were at very high and high-hazard levels for landslides, respectively. The landslide risk was obtained based on the susceptibility map and hazard map of landslides. The results show that only 26% of the slope units were located at very high and high-risk levels for landslides and these are mainly concentrated in urban centers. Such risk zones should be taken seriously and their dynamics must be monitored. Our landslide risk map is expected to provide information for planners to help them choose appropriate locations for development schemes and improve integrated geohazard mitigation in Yan\u2019an City.<\/jats:p>","DOI":"10.3390\/rs14092131","type":"journal-article","created":{"date-parts":[[2022,5,2]],"date-time":"2022-05-02T07:08:58Z","timestamp":1651475338000},"page":"2131","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5345-9398","authenticated-orcid":false,"given":"Wangcai","family":"Liu","sequence":"first","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiwen","family":"Liang","sequence":"additional","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pingping","family":"Sun","sequence":"additional","affiliation":[{"name":"Key Laboratory for Geo-Hazards in Loess Area, Ministry of Natural Resources, Xi\u2019an Center of Geological\r\nSurvey, China Geological Survey, Xi\u2019an 710054, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanxi","family":"Li","sequence":"additional","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4895-9782","authenticated-orcid":false,"given":"Xiaojun","family":"Su","sequence":"additional","affiliation":[{"name":"College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3102-6193","authenticated-orcid":false,"given":"Xingmin","family":"Meng","sequence":"additional","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.5194\/nhess-18-2161-2018","article-title":"Global fatal landslide occurrence from 2004 to 2016","volume":"18","author":"Froude","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1007\/s10346-020-01521-4","article-title":"Probabilistic evaluation of loess landslide impact using multivariate model","volume":"18","author":"Xu","year":"2021","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.enggeo.2014.08.015","article-title":"Heavy rainfall triggered loess\u2013mudstone landslide and subsequent debris flow in Tianshui, China","volume":"186","author":"Peng","year":"2015","journal-title":"Eng. Geol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1007\/s11069-020-04264-6","article-title":"Influence of human activity on landslide susceptibility development in the Three Gorges area","volume":"104","author":"Li","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.enggeo.2008.03.022","article-title":"Guidelines for landslide susceptibility, hazard and risk zoning for land use planning","volume":"102","author":"Fell","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0013-7952(01)00093-X","article-title":"Landslide risk assessment and management: An overview","volume":"64","author":"Dai","year":"2002","journal-title":"Eng. Geol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.compgeo.2013.04.009","article-title":"Quantitative risk assessment of landslide by limit analysis and random fields","volume":"53","author":"Huang","year":"2013","journal-title":"Comput. Geotech."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108370","DOI":"10.1016\/j.measurement.2020.108370","article-title":"Geological hazard risk assessment of line landslide based on remotely sensed data and GIS","volume":"169","author":"Tan","year":"2021","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ssci.2016.03.016","article-title":"Regional risk assessment for urban major hazards based on GIS geoprocessing to improve public safety","volume":"87","author":"Zhao","year":"2016","journal-title":"Saf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geomorph.2015.06.001","article-title":"Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)","volume":"249","author":"Trigila","year":"2015","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.catena.2017.11.022","article-title":"Prediction of the landslide susceptibility: Which algorithm, which precision?","volume":"162","author":"Pourghasemi","year":"2018","journal-title":"Catena"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enggeo.2017.04.013","article-title":"Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine","volume":"223","author":"Huang","year":"2017","journal-title":"Eng. Geol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.enggeo.2008.03.019","article-title":"Transient deterministic shallow landslide modeling: Requirements for susceptibility and hazard assessments in a GIS framework","volume":"102","author":"Godt","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.enggeo.2004.01.009","article-title":"Shallow landslides in pyroclastic soils: A distributed modelling approach for hazard assessment","volume":"73","author":"Frattini","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.catena.2016.01.022","article-title":"Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping","volume":"140","author":"Pradhan","year":"2016","journal-title":"Catena"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104580","DOI":"10.1016\/j.catena.2020.104580","article-title":"Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping","volume":"191","author":"Huang","year":"2020","journal-title":"Catena"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.catena.2007.01.003","article-title":"GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): Comparisons of results and confirmations","volume":"72","author":"Yalcin","year":"2008","journal-title":"Catena"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.catena.2018.04.003","article-title":"A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping","volume":"166","author":"Zhu","year":"2018","journal-title":"Catena"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"133557","DOI":"10.1016\/j.scitotenv.2019.07.363","article-title":"Relation between land cover and landslide susceptibility in Val d\u2019Aran, Pyrenees (Spain): Historical aspects, present situation and forward prediction","volume":"693","author":"Shu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth Sci. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s12517-018-3531-5","article-title":"Analysis and evaluation of landslide susceptibility: A review on articles published during 2005\u20132016 (periods of 2005\u20132012 and 2013\u20132016)","volume":"11","author":"Pourghasemi","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s11629-011-2157-9","article-title":"GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis","volume":"8","author":"Ramani","year":"2011","journal-title":"J. Mt. Sci. Engl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1007\/s10346-014-0550-5","article-title":"A systematic review of landslide probability mapping using logistic regression","volume":"12","author":"Budimir","year":"2015","journal-title":"Landslides"},{"key":"ref_24","first-page":"11","article-title":"Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan","volume":"22","author":"Khan","year":"2019","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rabby, Y.W., and Li, Y. (2020). Landslide Susceptibility Mapping Using Integrated Methods: A Case Study in the Chittagong Hilly Areas, Bangladesh. Geosciences, 10.","DOI":"10.3390\/geosciences10120483"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1007\/s10346-011-0305-5","article-title":"GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model","volume":"9","author":"Damm","year":"2012","journal-title":"Landslides"},{"key":"ref_27","unstructured":"Pradhan, S.P., Vishal, V., and Singh, T.N. (2019). Machine Learning Techniques in Landslide Susceptibility Mapping: A Survey and a Case Study. Landslides: Theory, Practice and Modelling, Springer International Publishing."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10881","DOI":"10.1007\/s00521-020-05529-8","article-title":"Machine learning for landslides prevention: A survey","volume":"33","author":"Ma","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1016\/j.catena.2020.104833","article-title":"GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods","volume":"196","author":"Chen","year":"2021","journal-title":"Catena"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s11069-017-3104-z","article-title":"Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada","volume":"90","author":"Behnia","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.catena.2018.01.005","article-title":"Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)","volume":"163","author":"Hong","year":"2018","journal-title":"Catena"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cageo.2015.04.007","article-title":"Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling","volume":"81","author":"Goetz","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s12665-017-6640-7","article-title":"A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China","volume":"76","author":"Xie","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.catena.2016.11.032","article-title":"A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility","volume":"151","author":"Chen","year":"2017","journal-title":"Catena"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"105250","DOI":"10.1016\/j.catena.2021.105250","article-title":"Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models","volume":"202","author":"Huang","year":"2021","journal-title":"Catena"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1007\/s12665-011-1297-0","article-title":"Landslide susceptibility assessment: What are the effects of mapping unit and mapping method?","volume":"66","author":"Erener","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_38","unstructured":"Jacobs, L., Kervyn, M., Poesen, J., Reichenbach, P., Rossi, M., Marchesini, I., Alvioli, M., and Dewitte, O. (2017, January 23\u201328). Dealing with heterogeneous landslide information for landslide susceptibility assessment: Comparing a pixel-based and slope unit-based approach. Proceedings of the EGU General Assembly Conference, Vienna, Austria."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s12145-018-0335-9","article-title":"A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment","volume":"11","author":"Ba","year":"2018","journal-title":"Earth Sci. Inform."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107084","DOI":"10.1016\/j.geomorph.2020.107084","article-title":"Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach","volume":"356","author":"Jacobs","year":"2020","journal-title":"Geomorphology"},{"key":"ref_41","first-page":"714","article-title":"Dynamic formation mechanism of landslide disaster on the Loess Plateau","volume":"26","author":"Peng","year":"2020","journal-title":"J. Geomech."},{"key":"ref_42","first-page":"118","article-title":"Loess landslide susceptibility evaluation based on slope unit and information value method in Baota District, Yan\u2019 an","volume":"34","author":"Xue","year":"2015","journal-title":"Geol. Bull. China"},{"key":"ref_43","first-page":"211","article-title":"Risk Zoning of Landslide Based on SINMAP Model in Yan\u2019an City","volume":"39","author":"Gao","year":"2019","journal-title":"Bull. Soil Water Conserv."},{"key":"ref_44","first-page":"679","article-title":"Risk assessment of geological hazards in Baota District, Yan\u2019 an City, Shanxi, China","volume":"38","author":"Yang","year":"2020","journal-title":"Mt. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.rse.2016.07.018","article-title":"Landslide susceptibility map refinement using PSInSAR data","volume":"184","author":"Ciampalini","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_46","first-page":"95","article-title":"Analysis of trend and mutation characteristics of precipitation in yan\u2019an city during past 45 years","volume":"35","author":"Ma","year":"2016","journal-title":"J. Yanan Univ. Nat. Sci. Ed."},{"key":"ref_47","first-page":"236","article-title":"Distribution Regularity and Development Characteristics of Landslides in Yan\u2019an","volume":"36","author":"Zhu","year":"2017","journal-title":"Geol. Sci. Technol. Inf."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.enggeo.2008.03.010","article-title":"Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview","volume":"102","author":"Castellanos","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1080\/08120099.2013.762942","article-title":"Evaluating the effect of slope curvature on slope stability by a numerical analysis","volume":"60","author":"Sharma","year":"2013","journal-title":"Aust. J. Earth Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"124159","DOI":"10.1016\/j.jclepro.2020.124159","article-title":"Integrating principal component analysis with statistically-based models for analysis of causal factors and landslide susceptibility mapping: A comparative study from the loess plateau area in Shanxi (China)","volume":"277","author":"Tang","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.enggeo.2011.09.013","article-title":"Influences of spatial distribution of soil thickness on shallow landslide prediction","volume":"124","author":"Ho","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_52","first-page":"1409","article-title":"Initial analysis on environmental effect of cutting hills to backfill ditch project on Loess Plateau\u2014Take Yan\u2019 an New District as an example","volume":"65","author":"Zhang","year":"2019","journal-title":"Geol. Rev."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"170","DOI":"10.21449\/ijate.479404","article-title":"The Effect of the Normalization Method Used in Different Sample Sizes on the Success of Artificial Neural Network Model","volume":"6","author":"Aksu","year":"2019","journal-title":"Int. J. Assess. Tools Educ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.enggeo.2006.09.013","article-title":"Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry","volume":"88","author":"Colesanti","year":"2006","journal-title":"Eng. Geol."},{"key":"ref_55","unstructured":"Wegm\u00fcller, U., Strozzi, T., and Tosi, L. (2000, January 24\u201329). Differential SAR interferometry for land subsidence monitoring: Methodology and examples. Proceedings of the Sixth International Symposium on Land Subsidence, Ravenna, Italy."},{"key":"ref_56","unstructured":"Pasquali, P., Pellegrini, R., Prati, C., and Rocca, F. (1994, January 8\u201312). Combination of interferograms from ascending and descending orbits. Proceedings of the IGARSS \u201894\u20141994 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA."},{"key":"ref_57","first-page":"926","article-title":"Analysis of Influence of Vegetation Coverage and Slope on SAR Interferometric Coherence","volume":"38","author":"Yu","year":"2020","journal-title":"Mt. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wu, Q., Jia, C., Chen, S., and Li, H. (2019). SBAS-InSAR Based Deformation Detection of Urban Land, Created from Mega-Scale Mountain Excavating and Valley Filling in the Loess Plateau: The Case Study of Yan\u2019an City. Remote Sens., 11.","DOI":"10.3390\/rs11141673"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liao, M., Zhang, R., Lv, J., Yu, B., Pang, J., Li, R., Xiang, W., and Tao, W. (2021). Subsidence Monitoring of Fill Area in Yan\u2019an New District Based on Sentinel-1A Time Series Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13153044"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geomorph.2017.09.007","article-title":"Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques","volume":"297","author":"Chen","year":"2017","journal-title":"Geomorphology"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zhao, F., Meng, X., Zhang, Y., Chen, G., Su, X., and Yue, D. (2019). Landslide Susceptibility Mapping of Karakorum Highway Combined with the Application of SBAS-InSAR Technology. Sensors, 19.","DOI":"10.3390\/s19122685"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s10346-013-0432-2","article-title":"Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry","volume":"11","author":"Lu","year":"2014","journal-title":"Landslides"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"27","DOI":"10.5194\/isprs-annals-VI-3-W1-2020-27-2020","article-title":"Integration of gis and advanced remote sensing techniques for landslide hazard assessment: A case study of northwest syria","volume":"VI-3\/W1-2020","author":"Hammad","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_65","first-page":"1","article-title":"Multicriteria decision making: The analytic hierarchy process: Planning, priority setting resource allocation","volume":"2","author":"Saaty","year":"1990","journal-title":"Resour. Alloc."},{"key":"ref_66","unstructured":"Fr\u00e9d\u00e9ric, L., Ast\u00e9, J., and Leroi, E. (1996). Vulnerability assessment of elements exposed to mass-movement: Working toward a better risk perception. Landslides-Glissements de Terrain, Balkema."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"5453","DOI":"10.1007\/s10661-011-2352-8","article-title":"Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey)","volume":"184","author":"Akgun","year":"2012","journal-title":"Environ. Monit. Assess."},{"key":"ref_68","first-page":"378","article-title":"Slope classification system for loess collapse risk assessment","volume":"20","author":"Tang","year":"2012","journal-title":"J. Eng. Geol."},{"key":"ref_69","first-page":"166","article-title":"Landslide risk assessment methods and flow on a large scale\u2014A case study of loess landslides risk assessment in Yan\u2019an urban districts, Shaanxi, China","volume":"30","author":"Tang","year":"2011","journal-title":"Geol. Bull. China"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.geomorph.2006.09.023","article-title":"Comparing landslide inventory maps","volume":"94","author":"Galli","year":"2008","journal-title":"Geomorphology"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/j.gsf.2020.05.010","article-title":"Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia","volume":"12","author":"Youssef","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_72","first-page":"35","article-title":"Characterization of pre-failure deformation and evolution of a large earthflow using InSAR monitoring and optical image interpretation","volume":"19","author":"Zhang","year":"2021","journal-title":"Landslides"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Confuorto, P., Medici, C., Bianchini, S., Del Soldato, M., Rosi, A., Segoni, S., and Casagli, N. (2022). Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence. Remote Sens., 14.","DOI":"10.3390\/rs14071748"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.enggeo.2018.02.020","article-title":"Landslide hazard assessment in the Himalayas (Nepal and Bhutan) based on Earth-Observation data","volume":"237","author":"Ambrosi","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"103574","DOI":"10.1016\/j.earscirev.2021.103574","article-title":"Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future","volume":"216","author":"Mondini","year":"2021","journal-title":"Earth Sci. Rev."},{"key":"ref_76","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2131\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:03:50Z","timestamp":1760137430000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2131"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,29]]},"references-count":76,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092131"],"URL":"https:\/\/doi.org\/10.3390\/rs14092131","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,29]]}}}