{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:46:14Z","timestamp":1773704774488,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T00:00:00Z","timestamp":1719619200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Scholarship Council","award":["202008050048"],"award-info":[{"award-number":["202008050048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong\u2013Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.<\/jats:p>","DOI":"10.3390\/rs16132394","type":"journal-article","created":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T08:17:29Z","timestamp":1719821849000},"page":"2394","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China"],"prefix":"10.3390","volume":"16","author":[{"given":"Ruiqi","family":"Zhang","sequence":"first","affiliation":[{"name":"Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8568, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8519-1421","authenticated-orcid":false,"given":"Lele","family":"Zhang","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zhice","family":"Fang","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8340-6994","authenticated-orcid":false,"given":"Takashi","family":"Oguchi","sequence":"additional","affiliation":[{"name":"Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8568, Japan"},{"name":"Center for Spatial Information Science, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa 277-8568, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1041-6865","authenticated-orcid":false,"given":"Abdelaziz","family":"Merghadi","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4269-5031","authenticated-orcid":false,"given":"Zijin","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, China"}]},{"given":"Aonan","family":"Dong","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5930-199X","authenticated-orcid":false,"given":"Jie","family":"Dou","sequence":"additional","affiliation":[{"name":"Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, Q., Guo, C., Dong, X., Li, W., Lu, H., Fu, H., and Liu, X. (2021). Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China. Remote Sens., 13.","DOI":"10.3390\/rs13214234"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107156","DOI":"10.1016\/j.enggeo.2023.107156","article-title":"Remote sensing for landslide investigations: A progress report from China","volume":"321","author":"Xu","year":"2023","journal-title":"Eng. Geol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"133146","DOI":"10.1016\/j.jclepro.2022.133146","article-title":"Refined landslide susceptibility analysis based on InSAR technology and UAV multi-source data","volume":"368","author":"Cao","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhang, Y., Liang, Y., Sun, P., Li, Y., Su, X., Wang, A., and Meng, X. (2022). Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest. Remote Sens., 14.","DOI":"10.3390\/rs14092131"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2256308","DOI":"10.1080\/10106049.2023.2256308","article-title":"Unraveling the evolution of landslide susceptibility: A systematic review of 30-years of strategic themes and trends","volume":"38","author":"Dong","year":"2023","journal-title":"Geocarto Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107109","DOI":"10.1016\/j.catena.2023.107109","article-title":"Exploring the uncertainty of landslide susceptibility assessment caused by the number of non\u2013landslides","volume":"227","author":"Liu","year":"2023","journal-title":"Catena"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106799","DOI":"10.1016\/j.catena.2022.106799","article-title":"Landslide susceptibility assessment through TrAdaBoost transfer learning models using two landslide inventories","volume":"222","author":"Zhiyong","year":"2023","journal-title":"Catena"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104188","DOI":"10.1016\/j.catena.2019.104188","article-title":"A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods","volume":"183","author":"Zhu","year":"2019","journal-title":"Catena"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"101621","DOI":"10.1016\/j.gsf.2023.101621","article-title":"A physics-informed data-driven model for landslide susceptibility assessment in the Three Gorges Reservoir area","volume":"14","author":"Liu","year":"2023","journal-title":"Geosci. Front."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.catena.2018.12.035","article-title":"Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping","volume":"176","author":"Hong","year":"2019","journal-title":"Catena"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1038\/s41598-023-28991-5","article-title":"An objective absence data sampling method for landslide susceptibility mapping","volume":"13","author":"Rabby","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fu, Z., Wang, F., Dou, J., Nam, K., and Ma, H. (2023). Enhanced Absence Sampling Technique for Data-Driven Landslide Susceptibility Mapping: A Case Study in Songyang County, China. Remote Sens., 15.","DOI":"10.3390\/rs15133345"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s11069-021-04844-0","article-title":"A hybrid framework integrating physical model and convolutional neural network for regional landslide susceptibility mapping","volume":"109","author":"Wei","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, Y., Zuo, X., Zhu, D., Wu, W., Yang, X., Guo, S., Shi, C., Huang, C., Li, F., and Liu, X. (2022). Identification and Analysis of Landslides in the Ahai Reservoir Area of the Jinsha River Basin Using a Combination of DS-InSAR, Optical Images, and Field Surveys. Remote Sens., 14.","DOI":"10.3390\/rs14246274"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Miao, F., Ruan, Q., Wu, Y., Qian, Z., Kong, Z., and Qin, Z. (2023). Landslide Dynamic Susceptibility Mapping Base on Machine Learning and the PS-InSAR Coupling Model. Remote Sens., 15.","DOI":"10.3390\/rs15225427"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"2185120","DOI":"10.1080\/19475705.2023.2185120","article-title":"An identification method of potential landslide zones using InSAR data and landslide susceptibility","volume":"14","author":"He","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_18","first-page":"1","article-title":"Landslide hazard analysis based on SBAS-InSAR and MCE-CNN model: A case study of Kongtong, Pingliang","volume":"38","author":"Zhang","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1007\/s10346-021-01796-1","article-title":"Enhanced dynamic landslide hazard mapping using MT-InSAR method in the Three Gorges Reservoir Area","volume":"19","author":"Zhou","year":"2022","journal-title":"Landslides"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"137320","DOI":"10.1016\/j.scitotenv.2020.137320","article-title":"Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning","volume":"720","author":"Dou","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, Y., Pei, J., Wang, Z., Zhang, Y., and Yuan, H. (2021). Analysis on the Characteristics of Crustal Structure and Seismotectonic Environment in Zigui Basin, Three Gorges. Front. Earth Sci., 9.","DOI":"10.3389\/feart.2021.780209"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.geomorph.2013.08.013","article-title":"Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China","volume":"204","author":"Peng","year":"2014","journal-title":"Geomorphology"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yu, X., and Gao, H. (2020). A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0229818"},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s10346-020-01444-0","article-title":"Dynamic development of landslide susceptibility based on slope unit and deep neural networks","volume":"18","author":"Hua","year":"2021","journal-title":"Landslides"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Dun, J., Feng, W., Yi, X., Zhang, G., and Wu, M. (2021). Detection and Mapping of Active Landslides before Impoundment in the Baihetan Reservoir Area (China) Based on the Time-Series InSAR Method. Remote Sens., 13.","DOI":"10.3390\/rs13163213"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, K., Deng, J., Ge, D., Liang, R., Li, W., and Xu, Q. (2021). Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR. Remote Sens., 13.","DOI":"10.3390\/rs13183662"},{"key":"ref_30","first-page":"103157","article-title":"Dynamic landslides susceptibility evaluation in Baihetan Dam area during extensive impoundment by integrating geological model and InSAR observations","volume":"116","author":"Dai","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yao, J., Yao, X., and Liu, X. (2022). Landslide Detection and Mapping Based on SBAS-InSAR and PS-InSAR: A Case Study in Gongjue County, Tibet, China. Remote Sens., 14.","DOI":"10.3390\/rs14194728"},{"key":"ref_32","first-page":"36","article-title":"Landslide Types and Processes","volume":"Volume 247","author":"Turner","year":"1996","journal-title":"Landslides, Investigation and Mitigation; Transportation Research Board Special Report"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, C., Liang, J., Zhang, S., Dong, J., Yan, S., Yang, L., Liu, B., Ma, X., and Li, W. (2022). Integration of Sentinel-1A, ALOS-2 and GF-1 Datasets for Identifying Landslides in the Three Parallel Rivers Region, China. Remote Sens., 14.","DOI":"10.3390\/rs14195031"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, C., Cao, Y., Yin, K., Wang, Y., Shi, X., Catani, F., and Ahmed, B. (2020). Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China. Remote Sens., 12.","DOI":"10.3390\/rs12203385"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Y., Meng, X., Liu, W., Wang, A., Liang, Y., Su, X., Zeng, R., and Chen, X. (2023). Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry. Remote Sens., 15.","DOI":"10.3390\/rs15204951"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jia, H., Wang, Y., Ge, D., Deng, Y., and Wang, R. (2022). InSAR Study of Landslides: Early Detection, Three-Dimensional, and Long-Term Surface Displacement Estimation\u2014A Case of Xiaojiang River Basin, China. Remote Sens., 14.","DOI":"10.3390\/rs14071759"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4453","DOI":"10.1007\/s11440-023-01841-4","article-title":"Comparison of hybrid data-driven and physical models for landslide susceptibility mapping at regional scales","volume":"18","author":"Wei","year":"2023","journal-title":"Acta Geotech."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s10346-019-01286-5","article-title":"Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan","volume":"17","author":"Dou","year":"2020","journal-title":"Landslides"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.gr.2022.08.004","article-title":"A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset","volume":"123","author":"Pradhan","year":"2023","journal-title":"Gondwana Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s12665-017-6731-5","article-title":"The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China","volume":"76","author":"Zhang","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108236","DOI":"10.1016\/j.geomorph.2022.108236","article-title":"Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold","volume":"408","author":"Huang","year":"2022","journal-title":"Geomorphology"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1007\/s10064-022-02836-3","article-title":"A frequency ratio-based sampling strategy for landslide susceptibility assessment","volume":"81","author":"Liu","year":"2022","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"05023007","DOI":"10.1061\/NHREFO.NHENG-1499","article-title":"Landslide Susceptibility Mapping for Road Corridors Using Coupled InSAR and GIS Statistical Analysis","volume":"24","author":"Arsyad","year":"2023","journal-title":"Nat. Hazards Rev."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","first-page":"101317","DOI":"10.1016\/j.gsf.2021.101317","article-title":"Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions","volume":"13","author":"Huang","year":"2022","journal-title":"Geosci. Front."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s11069-012-0267-5","article-title":"Producing landslide-susceptibility maps for regional planning in data-scarce regions","volume":"64","author":"Romesburg","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"107436","DOI":"10.1016\/j.enggeo.2024.107436","article-title":"Advanced integration of ensemble learning and MT-InSAR for enhanced slow-moving landslide susceptibility zoning","volume":"331","author":"Zeng","year":"2024","journal-title":"Eng. Geol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s10346-021-01790-7","article-title":"Characteristics of a rapid landsliding area along Jinsha River revealed by multi-temporal remote sensing and its risks to Sichuan-Tibet railway","volume":"19","author":"Yao","year":"2022","journal-title":"Landslides"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10346-021-01744-z","article-title":"Characterization of pre-failure deformation and evolution of a large earthflow using InSAR monitoring and optical image interpretation","volume":"19","author":"Yi","year":"2022","journal-title":"Landslides"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2905","DOI":"10.1007\/s10346-022-01954-z","article-title":"An integrated framework for wide-area active landslide detection with InSAR observations and SAR pixel offsets","volume":"19","author":"Zhang","year":"2022","journal-title":"Landslides"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Luo, W., Dou, J., Fu, Y., Wang, X., He, Y., Ma, H., Wang, R., and Xing, K. (2023). A Novel Hybrid LMD-ETS-TCN Approach for Predicting Landslide Displacement Based on GPS Time Series Analysis. Remote Sens., 15.","DOI":"10.3390\/rs15010229"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, X., Achilli, V., Fabris, M., Menin, A., Monego, M., Tessari, G., and Floris, M. (2021). Combining Sentinel-1 Interferometry and Ground-Based Geomatics Techniques for Monitoring Buildings Affected by Mass Movements. Remote Sens., 13.","DOI":"10.3390\/rs13030452"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1080\/15481603.2022.2100054","article-title":"Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements","volume":"59","author":"Dai","year":"2022","journal-title":"Gisci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4514205","DOI":"10.1109\/LGRS.2022.3207064","article-title":"Rapid and Automatic Detection of New Potential Landslide Based on Phase-Gradient DInSAR","volume":"19","author":"Shen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"111983","DOI":"10.1016\/j.rse.2020.111983","article-title":"InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal","volume":"249","author":"Bekaert","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"113231","DOI":"10.1016\/j.rse.2022.113231","article-title":"Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia","volume":"281","author":"Kim","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Liu, X., Yao, X., and Yao, J. (2022). Accelerated Movements of Xiaomojiu Landslide Observed with SBAS-InSAR and Three-Dimensional Measurements, Upper Jinsha River, Eastern Tibet. Appl. Sci., 12.","DOI":"10.3390\/app12199758"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Su, X., Zhang, Y., Meng, X., Rehman, M.U., Khalid, Z., and Yue, D. (2022). Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR. Remote Sens., 14.","DOI":"10.3390\/rs14194907"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xiao, B., Zhao, J., Li, D., Zhao, Z., Zhou, D., Xi, W., and Li, Y. (2022). Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China. Sensors, 22.","DOI":"10.3390\/s22208041"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"107150","DOI":"10.1016\/j.ecoleng.2023.107150","article-title":"Development of an integrated model for assessing landslide susceptibility on vegetated slopes under random rainfall scenarios","volume":"199","author":"Jiang","year":"2024","journal-title":"Ecol. Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"107342","DOI":"10.1016\/j.enggeo.2023.107342","article-title":"Landslide susceptibility assessment in multiple urban slope settings with a landslide inventory augmented by InSAR techniques","volume":"327","author":"Chen","year":"2023","journal-title":"Eng. Geol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Dong, X., Yin, T., Dai, K., Pirasteh, S., Zhuo, G., Li, Z., Yu, B., and Xu, Q. (2022). Identifying Potential Landslides on Giant Niexia Slope (China) Based on Integrated Multi-Remote Sensing Technologies. Remote Sens., 14.","DOI":"10.3390\/rs14246328"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hamdi, L., Defaflia, N., Merghadi, A., Fehdi, C., Yunus, A.P., Dou, J., Pham, Q.B., Abdo, H.G., Almohamad, H., and Al-Mutiry, M. (2023). Ground Surface Deformation Analysis Integrating InSAR and GPS Data in the Karstic Terrain of Cheria Basin, Algeria. Remote Sens., 15.","DOI":"10.3390\/rs15061486"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Merghadi, A., Abderrahmane, B., and Tien Bui, D. (2018). Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods. Isprs Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7070268"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Hamdi, L., Defaflia, N., Fehdi, C., and Merghadi, A. (October, January 26). InSAR Investigation on DRAA-Douamis Sinkholes in Cheria Northeastern of Algeria. Proceedings of the IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323835"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2394\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:07:48Z","timestamp":1760108868000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,29]]},"references-count":67,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132394"],"URL":"https:\/\/doi.org\/10.3390\/rs16132394","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,29]]}}}