{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T23:53:15Z","timestamp":1773964395267,"version":"3.50.1"},"reference-count":102,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T00:00:00Z","timestamp":1634256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41871305"],"award-info":[{"award-number":["No. 41871305"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National key R &amp; D program of China","award":["No.2017YFC0602204"],"award-info":[{"award-number":["No.2017YFC0602204"]}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["No. CUGQY1945"],"award-info":[{"award-number":["No. CUGQY1945"]}]},{"name":"Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education; and Fundamental Research Funds for the Central Universities","award":["No. GLAB2019ZR02"],"award-info":[{"award-number":["No. GLAB2019ZR02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7\/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models\u2019 sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area.<\/jats:p>","DOI":"10.3390\/rs13204129","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T23:25:15Z","timestamp":1634513115000},"page":"4129","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["PS-InSAR-Based Validated Landslide Susceptibility Mapping along Karakorum Highway, Pakistan"],"prefix":"10.3390","volume":"13","author":[{"given":"Muhammad Afaq","family":"Hussain","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6373-3162","authenticated-orcid":false,"given":"Zhanlong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5570-6391","authenticated-orcid":false,"given":"Run","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"given":"Muhammad","family":"Shoaib","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Hydraulic Engineering Simulation and Safety, School of Civil Engineering, Tianjin University, Tianjin 300101, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rehman, M.U., Zhang, Y., Meng, X., Su, X., Catani, F., Rehman, G., Yue, D., Khalid, Z., Ahmad, S., and Ahmad, I. (2020). Analysis of landslide movements using interferometric synthetic aperture radar: A case study in Hunza-Nagar Valley, Pakistan. Remote Sens., 12.","DOI":"10.3390\/rs12122054"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"272","DOI":"10.5721\/EuJRS20134615","article-title":"First steps towards a landslide inventory map of the Central Karakoram National Park","volume":"46","author":"Calligaris","year":"2013","journal-title":"Eur. J. Remote Sens."},{"key":"ref_4","unstructured":"Karim, E. (2006). Hazard and Vulnerability Assessment of Sherqila Village District Ghizer NAs Pakistan. [Ph.D. Thesis, University of Geneva]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1002\/(SICI)1099-1417(199611\/12)11:6<461::AID-JQS282>3.0.CO;2-G","article-title":"Quaternary lacustrine deposits in a high-energy semi-arid mountain environment, Karakoram Mountains, northern Pakistan","volume":"11","author":"Owen","year":"1996","journal-title":"J. Quat. Sci."},{"key":"ref_6","first-page":"18","article-title":"Landslide Inventory and Landslide Susceptibility Mapping for China Pakistan Economic Corridor (CPEC)\u2019s main route (Karakorum Highway)","volume":"11","author":"Abbas","year":"2021","journal-title":"J. Appl. Emerg. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"999","DOI":"10.5194\/nhess-19-999-2019","article-title":"Landslide susceptibility mapping by using a geographic information system (GIS) along the China\u2013Pakistan Economic Corridor (Karakoram Highway), Pakistan","volume":"19","author":"Ali","year":"2019","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Marsala, V., Galli, A., Paglia, G., and Miccadei, E. (2019). Landslide susceptibility assessment of Mauritius Island (Indian ocean). Geosciences, 9.","DOI":"10.3390\/geosciences9120493"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1007\/s11629-016-4220-z","article-title":"Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: A combined approach of weights of evidence and spatial multi-criteria","volume":"14","author":"Rahman","year":"2017","journal-title":"J. Mt. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.1007\/s11629-018-5195-8","article-title":"Landslide susceptibility analysis of Karakoram highway using analytical hierarchy process and scoops 3D","volume":"17","author":"Rashid","year":"2020","journal-title":"J. Mt. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s12665-017-6471-6","article-title":"Rock fall susceptibility assessment along a mountainous road: An evaluation of bivariate statistic, analytical hierarchy process and frequency ratio","volume":"76","author":"Shirzadi","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s12145-015-0220-8","article-title":"Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS","volume":"8","author":"Razandi","year":"2015","journal-title":"Earth Sci. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/s12517-012-0807-z","article-title":"Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya","volume":"7","author":"Regmi","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"909","DOI":"10.1007\/s13762-013-0464-0","article-title":"GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran","volume":"11","author":"Jaafari","year":"2014","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.gsf.2019.10.001","article-title":"How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?","volume":"11","author":"Achour","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s12594-016-0395-8","article-title":"Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand","volume":"87","author":"Kumar","year":"2016","journal-title":"J. Geol. Soc. India"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"974638","DOI":"10.1155\/2012\/974638","article-title":"Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes Models","volume":"2012","author":"Pradhan","year":"2012","journal-title":"Math. Probl. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1007\/s12665-012-1842-5","article-title":"Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea","volume":"68","author":"Park","year":"2013","journal-title":"Environ. Earth Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s40808-018-0426-0","article-title":"Modeling and mapping landslide susceptibility zones using GIS based multivariate binary logistic regression (LR) model in the Rorachu river basin of eastern Sikkim Himalaya, India","volume":"4","author":"Mandal","year":"2018","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s10346-015-0614-1","article-title":"Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia","volume":"13","author":"Youssef","year":"2016","journal-title":"Landslides"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.geomorph.2009.06.020","article-title":"Optimal landslide susceptibility zonation based on multiple forecasts","volume":"114","author":"Rossi","year":"2010","journal-title":"Geomorphology"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Park, S., and Kim, J. (2019). Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl. Sci., 9.","DOI":"10.3390\/app9050942"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/20964471.2018.1472392","article-title":"Mapping landslide susceptibility and types using Random Forest","volume":"2","author":"Taalab","year":"2018","journal-title":"Big Earth Data"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10346-012-0320-1","article-title":"Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study","volume":"10","author":"Cuartero","year":"2013","journal-title":"Landslides"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.geomorph.2014.09.020","article-title":"Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy)","volume":"242","author":"Conoscenti","year":"2015","journal-title":"Geomorphology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.ecolmodel.2011.12.007","article-title":"How can statistical models help to determine driving factors of landslides?","volume":"239","author":"Vorpahl","year":"2012","journal-title":"Ecol. Model."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1080\/19475705.2017.1407368","article-title":"Assessment of the effects of training data selection on the landslide susceptibility mapping: A comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)","volume":"9","author":"Kalantar","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.jafrearsci.2016.02.019","article-title":"Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression","volume":"118","author":"Colkesen","year":"2016","journal-title":"J. Afr. Earth Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.envsoft.2016.07.005","article-title":"A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)","volume":"84","author":"Pham","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00704-015-1702-9","article-title":"Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: A comparison study of prediction capability of na\u00efve bayes, multilayer perceptron neural networks, and functional trees methods","volume":"128","author":"Pham","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s12665-015-5233-6","article-title":"Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran)","volume":"75","author":"Aghdam","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.catena.2015.07.020","article-title":"A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran","volume":"135","author":"Dehnavi","year":"2015","journal-title":"Catena"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/s12040-015-0536-2","article-title":"Landslide susceptibility zonation in part of Tehri reservoir region using frequency ratio, fuzzy logic and GIS","volume":"124","author":"Kumar","year":"2015","journal-title":"J. Earth Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.geomorph.2018.06.006","article-title":"Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia","volume":"318","author":"Aditian","year":"2018","journal-title":"Geomorphology"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.envsoft.2016.07.016","article-title":"Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping","volume":"84","author":"Arnone","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s12665-014-3442-z","article-title":"Using maximum entropy modeling for landslide susceptibility mapping with multiple geoenvironmental data sets","volume":"73","author":"Park","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.catena.2017.01.010","article-title":"Landslide susceptibility assessment using maximum entropy model with two different data sampling methods","volume":"152","author":"Kornejady","year":"2017","journal-title":"Catena"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4725","DOI":"10.1007\/s12665-013-2863-4","article-title":"Landslide susceptibility assessment using object mapping units, decision tree, and support vector machine models in the Three Gorges of China","volume":"71","author":"Wu","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103225","DOI":"10.1016\/j.earscirev.2020.103225","article-title":"Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance","volume":"207","author":"Merghadi","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Pradhan, A.M.S., and Kim, Y.-T. (2020). Rainfall-Induced Shallow Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.20944\/preprints202008.0089.v1"},{"key":"ref_43","first-page":"1","article-title":"Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping","volume":"35","author":"Sahin","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Qing, F., Zhao, Y., Meng, X., Su, X., Qi, T., and Yue, D. (2020). Application of Machine Learning to Debris Flow Susceptibility Mapping along the China\u2013Pakistan Karakoram Highway. Remote Sens., 12.","DOI":"10.3390\/rs12182933"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.geomorph.2018.10.024","article-title":"A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model","volume":"327","author":"Yan","year":"2019","journal-title":"Geomorphology"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1016\/j.scitotenv.2019.07.203","article-title":"Multi-hazard probability assessment and mapping in Iran","volume":"692","author":"Pourghasemi","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Nohani, E., Moharrami, M., Sharafi, S., Khosravi, K., Pradhan, B., Pham, B.T., Lee, S., and Melesse, A.M. (2019). Landslide susceptibility mapping using different GIS-based bivariate models. Water, 11.","DOI":"10.3390\/w11071402"},{"key":"ref_48","first-page":"119","article-title":"Assessment of earthquake-triggered landslide susceptibility based on expert knowledge and information value methods: A case study of the 20 April 2013 Lushan, China Mw6. 6 earthquake","volume":"6","author":"Xu","year":"2013","journal-title":"Disaster Adv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11069-012-0347-6","article-title":"Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling\u2013Narayanghat road section in Nepal Himalaya","volume":"65","author":"Devkota","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1007\/s11069-012-0257-7","article-title":"Landslide hazard and risk assessment: A case study from the Hlohovec\u2013Sered\u2019landslide area in south-west Slovakia","volume":"64","author":"Bednarik","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s10346-004-0039-8","article-title":"An approach for GIS-based statistical landslide susceptibility zonation\u2014with a case study in the Himalayas","volume":"2","author":"Saha","year":"2005","journal-title":"Landslides"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote sensing for landslide investigations: An overview of recent achievements and perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"433","DOI":"10.5194\/nhess-9-433-2009","article-title":"Estimating mass-wasting processes in active earth slides\u2013earth flows with time-series of High-Resolution DEMs from photogrammetry and airborne LiDAR","volume":"9","author":"Corsini","year":"2009","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.geomorph.2014.11.031","article-title":"Landslide deformation monitoring with ALOS\/PALSAR imagery: A D-InSAR geomorphological interpretation method","volume":"231","author":"Doubre","year":"2015","journal-title":"Geomorphology"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.enggeo.2012.07.017","article-title":"Design and implementation of a landslide early warning system","volume":"147","author":"Intrieri","year":"2012","journal-title":"Eng. Geol."},{"key":"ref_56","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_57","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_58","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2011.2124465","article-title":"A new algorithm for processing interferometric data-stacks: SqueeSAR","volume":"49","author":"Ferretti","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"B07407","DOI":"10.1029\/2006JB004763","article-title":"Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis, with application to Volc\u00e1n Alcedo, Gal\u00e1pagos","volume":"112","author":"Hooper","year":"2007","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_60","unstructured":"Werner, C., Wegmuller, U., Strozzi, T., and Wiesmann, A. (2003, January 21\u201325). Interferometric point target analysis for deformation mapping. Proceedings of the IGARSS 2003. In Proceedings of the2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477), Toulouse, France."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/LGRS.2005.855072","article-title":"Analysis of the terrain displacement along a funicular by SAR interferometry","volume":"3","author":"Strozzi","year":"2006","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1109\/TGRS.2003.814657","article-title":"Linear and nonlinear terrain deformation maps from a reduced set of interferometric SAR images","volume":"41","author":"Mora","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"443","DOI":"10.14358\/PERS.74.4.443","article-title":"Generation of advanced ERS and Envisat interferometric SAR products using the stable point network technique","volume":"74","author":"Crosetto","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s10346-010-0239-3","article-title":"Analysis with C-and X-band satellite SAR data of the Portalet landslide area","volume":"8","author":"Herrera","year":"2011","journal-title":"Landslides"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/LGRS.2010.2101045","article-title":"Object-oriented change detection for landslide rapid mapping","volume":"8","author":"Lu","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1144\/qjegh2013-028","article-title":"A new appraisal of the Ancona landslide based on geotechnical investigations and stability modelling","volume":"47","author":"Agostini","year":"2014","journal-title":"Q. J. Eng. Geol. Hydrogeol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1080\/01431161.2011.605087","article-title":"Updating landslide inventory maps using Persistent Scatterer Interferometry (PSI)","volume":"33","author":"Righini","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1007\/s11629-017-4697-0","article-title":"Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan","volume":"15","author":"Bacha","year":"2018","journal-title":"J. Mt. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"343","DOI":"10.5721\/EuJRS20144721","article-title":"A regional level preliminary landslide susceptibility study of the upper Indus river basin","volume":"47","author":"Ahmed","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1007\/s12517-016-2308-y","article-title":"Landslide susceptibility mapping using GIS and weighted overlay method: A case study from NW Himalayas, Pakistan","volume":"9","author":"Basharat","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1029\/1999TC900042","article-title":"The tectonic evolution of the Kohistan-Karakoram collision belt along the Karakoram Highway transect, north Pakistan","volume":"18","author":"Searle","year":"1999","journal-title":"Tectonics"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1029\/TC004i001p00127","article-title":"Cooling history of the NW Himalaya, Pakistan","volume":"4","author":"Zeitler","year":"1985","journal-title":"Tectonics"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1130\/B25357.1","article-title":"GPS measurements from the Ladakh Himalaya, India: Preliminary tests of plate-like or continuous deformation in Tibet","volume":"116","author":"Jade","year":"2004","journal-title":"Geol. Soc. Am. Bull."},{"key":"ref_75","unstructured":"Goudie, A., Brundsden, D., Whalley, W., Collins, D., and Derbyshire, E. (1984). The geomorphology of the Hunza valley, Karakoram mountains, Pakistan. International Karakoram Project, Cambridge University Press."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"DiPietro, J.A., and Pogue, K.R. (2004). Tectonostratigraphic subdivisions of the Himalaya: A view from the west. Tectonics, 23.","DOI":"10.1029\/2003TC001554"},{"key":"ref_77","first-page":"1","article-title":"Landslide susceptibility mapping along the China Pakistan Economic Corridor (CPEC) route using multi-criteria decision-making method","volume":"7","author":"Maqsoom","year":"2021","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"49","DOI":"10.3112\/erdkunde.2001.01.04","article-title":"Geomorphological hazards along the Karakoram highway: Khunjerab pass to the Gilgit River, northernmost Pakistan (Geomorphologische hazards entlang des Karakorum highway: Khunjerab Pa\u00df bis zum Gilgit River, n\u00f6rdlichstes Pakistan)","volume":"55","author":"Derbyshire","year":"2001","journal-title":"Erdkunde"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"104211","DOI":"10.1016\/j.catena.2019.104211","article-title":"GIS-based landslide susceptibility mapping for a part of the North Anatolian Fault Zone between Re\u015fadiye and Koyulhisar (Turkey)","volume":"183","author":"Demir","year":"2019","journal-title":"Catena"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Roy, J., Saha, S., Arabameri, A., Blaschke, T., and Bui, D.T. (2019). A novel ensemble approach for landslide susceptibility mapping (LSM) in Darjeeling and Kalimpong districts, West Bengal, India. Remote Sens., 11.","DOI":"10.3390\/rs11232866"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1002\/widm.8","article-title":"Classification and regression trees","volume":"1","author":"Loh","year":"2011","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-018-7808-5","article-title":"GIS-based gully erosion susceptibility mapping: A comparison among three data-driven models and AHP knowledge-based technique","volume":"77","author":"Arabameri","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1080\/10106049.2017.1323964","article-title":"Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea","volume":"33","author":"Kim","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Nelson, T.A., Nijland, W., Bourbonnais, M.L., and Wulder, M.A. (2017). Regression tree modeling of spatial pattern and process interactions. Mapping Forest Landscape Patterns, Springer.","DOI":"10.1007\/978-1-4939-7331-6_5"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s12583-012-0236-7","article-title":"Landslide hazard mapping using GIS and weight of evidence model in Qingshui river watershed of 2008 Wenchuan earthquake struck region","volume":"23","author":"Xu","year":"2012","journal-title":"J. Earth Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1007\/978-3-319-09048-1_144","article-title":"Scenarios of land cover change and landslide susceptibility: An example from the buzau subcarpathians, romania","volume":"Volume 5","author":"Malek","year":"2015","journal-title":"Engineering Geology for Society and Territory"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"720","DOI":"10.1016\/j.jseaes.2012.11.025","article-title":"Debris-flow hazards on tributary junction fans, Chitral, Hindu Kush Range, northern Pakistan","volume":"62","author":"Khan","year":"2013","journal-title":"J. Asian Earth Sci."},{"key":"ref_90","first-page":"34","article-title":"GIS Based landslide susceptibility mapping with application of analytical hierarchy process in District Ghizer, Gilgit Baltistan Pakistan","volume":"6","author":"Rahim","year":"2018","journal-title":"J. Geosci. Environ. Prot."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Song, Y., Niu, R., Xu, S., Ye, R., Peng, L., Guo, T., Li, S., and Chen, T. (2019). Landslide susceptibility mapping based on weighted gradient boosting decision tree in Wanzhou section of the Three Gorges Reservoir Area (China). ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010004"},{"key":"ref_92","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_93","doi-asserted-by":"crossref","unstructured":"Aslan, G., Foumelis, M., Raucoules, D., De Michele, M., Bernardie, S., and Cakir, Z. (2020). Landslide mapping and monitoring using Persistent Scatterer Interferometry (PSI) technique in the French Alps. Remote Sens., 12.","DOI":"10.3390\/rs12081305"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s10346-014-0522-9","article-title":"The contribution of PSInSAR interferometry to landslide hazard in weak rock-dominated areas","volume":"12","author":"Oliveira","year":"2015","journal-title":"Landslides"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s11069-020-03927-8","article-title":"Multi-geohazards susceptibility mapping based on machine learning\u2014a case study in Jiuzhaigou, China","volume":"102","author":"Cao","year":"2020","journal-title":"Nat. Hazards"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1080\/19475705.2021.1880977","article-title":"Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms","volume":"12","author":"Arabameri","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ge, T., Tian, W., and Liou, Y.-A. (2019). Debris flow susceptibility mapping using machine-learning techniques in Shigatse area, China. Remote Sens., 11.","DOI":"10.3390\/rs11232801"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s11069-015-1740-8","article-title":"Landslide susceptibility modeling assisted by Persistent Scatterers Interferometry (PSI): An example from the northwestern coast of Malta","volume":"78","author":"Piacentini","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Hakim, W.L., Achmad, A.R., and Lee, C.-W. (2020). Land subsidence susceptibility mapping in jakarta using functional and meta-ensemble machine learning algorithm based on time-series InSAR data. Remote Sens., 12.","DOI":"10.3390\/rs12213627"},{"key":"ref_100","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_101","doi-asserted-by":"crossref","first-page":"176","DOI":"10.3390\/geosciences4030176","article-title":"GIS-based landslide susceptibility mapping on the Peloponnese Peninsula, Greece","volume":"4","author":"Chalkias","year":"2014","journal-title":"Geosciences"},{"key":"ref_102","first-page":"1","article-title":"Optimized landslide susceptibility mapping and modelling using PS-InSAR technique: A case study of Chitral valley, Northern Pakistan","volume":"36","author":"Hussain","year":"2021","journal-title":"Geocarto Int."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4129\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:15:06Z","timestamp":1760166906000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/20\/4129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,15]]},"references-count":102,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13204129"],"URL":"https:\/\/doi.org\/10.3390\/rs13204129","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,15]]}}}