{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T00:31:35Z","timestamp":1774485095233,"version":"3.50.1"},"reference-count":130,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,18]],"date-time":"2022-04-18T00:00:00Z","timestamp":1650240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslide susceptibility is a contemporary method for delineation of landslide hazard zones and holistically mitigating the future landslides risks for planning and decision-making. The significance of this study is that it would be the first instance when the \u2018geon\u2019 model will be attempted to delineate landslide susceptibility map (LSM) for the complex lesser Himalayan topography as a contemporary LSM technique. This study adopted the per-pixel-based ensemble approaches through modified frequency ratio (MFR) and fuzzy analytical hierarchy process (FAHP) and compared it with the \u2018geons\u2019 (object-based) aggregation method to produce an LSM for the lesser Himalayan Kalsi-Chakrata road corridor. For the landslide susceptibility models, 14 landslide conditioning factors were carefully chosen; namely, slope, slope aspect, elevation, lithology, rainfall, seismicity, normalized differential vegetation index, stream power index, land use\/land cover, soil, topographical wetness index, and proximity to drainage, road, and fault. The inventory data for the past landslides were derived from preceding satellite images, intensive field surveys, and validation surveys. These inventory data were divided into training and test datasets following the commonly accepted 70:30 ratio. The GIS-based statistical techniques were adopted to establish the correlation between landslide training sites and conditioning factors. To determine the accuracy of the model output, the LSMs accuracy was validated through statistical methods of receiver operating characteristics (ROC) and relative landslide density index (R-index). The accuracy results indicate that the object-based geon methods produced higher accuracy (geon FAHP: 0.934; geon MFR: 0.910) over the per-pixel approaches (FAHP: 0.887; MFR: 0.841). The results noticeably showed that the geon method constructs significant regional units for future mitigation strategies and development. The present study may significantly benefit the decision-makers and regional planners in selecting the appropriate risk mitigation procedures at a local scale to counter the potential damages and losses from landslides in the area.<\/jats:p>","DOI":"10.3390\/rs14081953","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"1953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Predicting Landslides Susceptible Zones in the Lesser Himalayas by Ensemble of Per Pixel and Object-Based Models"],"prefix":"10.3390","volume":"14","author":[{"given":"Ujjwal","family":"Sur","sequence":"first","affiliation":[{"name":"Amity Institute of Geo-Informatics and Remote Sensing, Amity University-Sector 125, Noida 201313, India"}]},{"given":"Prafull","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Geology, Central University of South Bihar, Gaya 824236, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6175-6491","authenticated-orcid":false,"given":"Sansar Raj","family":"Meena","sequence":"additional","affiliation":[{"name":"Department of Geosciences, University of Padova, 35131 Padova, Italy"}]},{"given":"Trilok Nath","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, IIT Bombay, Mumbai 400076, India"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,18]]},"reference":[{"key":"ref_1","first-page":"628","article-title":"Weights of evidence modeling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand","volume":"92","author":"Mathew","year":"2007","journal-title":"Curr. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.catena.2015.05.019","article-title":"Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines","volume":"133","author":"Hong","year":"2015","journal-title":"Catena"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10346-006-0036-1","article-title":"Global landslide and avalanche hotspots","volume":"3","author":"Nadim","year":"2006","journal-title":"Landslides"},{"key":"ref_4","unstructured":"(2022, January 11). Geological Survey of India, Geological Map, Available online: https:\/\/www.gsi.gov.in\/webcenter\/portal\/OCBIS?_afrLoop=50017052507508016&_adf.ctrl-state=1bmlw2iaje_1#!%40%40%3F_afrLoop%3D50017052507508016%26_adf.ctrl-state%3D1bmlw2iaje_5."},{"key":"ref_5","first-page":"139","article-title":"Landslide susceptibility modelling using different advanced decision trees methods","volume":"35","author":"Prakash","year":"2019","journal-title":"Civ. Eng. Environ. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s41324-017-0105-7","article-title":"Comparative Evaluation of Various Approaches for Landslide Hazard Zoning: A Critical Review in Indian Perspectives","volume":"25","author":"Kaur","year":"2017","journal-title":"Spat. Inf. Res."},{"key":"ref_7","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":"J. Eng. Geol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.quaint.2014.05.005","article-title":"Tectonic uplift and landslides triggered by the Wenchuan earthquake and constraints on orogenic growth: A case study from Hongchun Gully, Longmen Mountains, Sichuan, China","volume":"349","author":"Li","year":"2014","journal-title":"Quat. Int."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s10346-013-0461-x","article-title":"A case study on an open hillside landslide impacting on a flexible rock fall barrier at Jordan Valley, Hong Kong","volume":"11","author":"Kwan","year":"2014","journal-title":"Landslides"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1016\/j.scitotenv.2019.03.415","article-title":"The human cost of global warming: Deadly landslides and their triggers (1995\u20132014)","volume":"682","author":"Haque","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1080\/19475705.2020.1836038","article-title":"Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data","volume":"11","author":"Sur","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_12","first-page":"1","article-title":"Landslide hazard and its mapping using Remote Sensing & GIS techniques","volume":"58","author":"Rai","year":"2014","journal-title":"J. Sci. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5233","DOI":"10.1007\/s10668-020-00811-0","article-title":"Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India","volume":"23","author":"Singh","year":"2020","journal-title":"Environ. Dev. Sustain."},{"key":"ref_14","unstructured":"NASA (2022, February 14). Climate change could trigger more landslides in High Mountain Asia. Science News, 11 February 2020, Available online: https:\/\/www.sciencedaily.com\/releases\/2020\/02\/200211121512.htm."},{"key":"ref_15","unstructured":"Varnes, D.J. (1984). Landslide Hazard Zonation: A Review of Principles and Practice, UNESCO. Available online: https:\/\/www.scirp.org\/(S(351jmbntvnsjt1aadkposzje))\/reference\/ReferencesPapers.aspx?ReferenceID=1768332."},{"key":"ref_16","first-page":"2018","article-title":"Landslide Susceptibility Indexing using geospatial and geostatistical techniques along Chakrata-Kalsi road corridor, India","volume":"38","author":"Sur","year":"2019","journal-title":"J. Indian Natl. Cartogr. Assoc. INCA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10346-013-0386-4","article-title":"Assessing landslide exposure in areas with limited landslide information","volume":"11","author":"Pellicani","year":"2014","journal-title":"Landslides"},{"key":"ref_18","unstructured":"Singh, P., and Sharma, A. (2015, January 2\u20134). Probabilistic Landslide susceptibility mapping using binary logistic regression model and Geospatial Techniques: A case study of Uttarakhand. Proceedings of the 16th ESRI User Conference, New Delhi, India."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-94-017-3490-5_12","article-title":"Landslide Hazard Assessment and Historical Landslide Data\u2014An Inseparable Couple? The Use of Historical Data in Natural Hazard Assessments","volume":"17","author":"Glade","year":"2001","journal-title":"Adv. Nat. Technol. Hazards Res."},{"key":"ref_20","first-page":"7082594","article-title":"Direct Interval Prediction of Landslide Displacements Using Least Squares Support Vector Machines","volume":"2020","author":"Wang","year":"2020","journal-title":"Complexity"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Aryal, J., and Gholaminia, K. (2018). A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J. Spat. Sci., 401\u2013418.","DOI":"10.1080\/14498596.2018.1505564"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1023\/B:NHAZ.0000007097.42735.9e","article-title":"Use of geomorphological information in indirect landslide susceptibility assessment","volume":"30","author":"Rengers","year":"2003","journal-title":"Nat. Hazards"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1007\/s11069-012-0218-1","article-title":"A new approach to use AHP in landslide susceptibility mapping: A case study at Yenice (Karabuk, NW Turkey)","volume":"63","author":"Ercanoglu","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yan, T., Shen, S., Zhou, A., and Chen, J. (2019). A Brief Report of Pingdi Landslide (23 July 2019) in Guizhou Province, China. Geosciences, 9.","DOI":"10.3390\/geosciences9090368"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tavakkoli Piralilou, S., Shahabi, H., Jarihani, B., Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S.R., and Aryal, J. (2019). Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas. Remote Sens., 11.","DOI":"10.3390\/rs11212575"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104451","DOI":"10.1016\/j.catena.2019.104451","article-title":"A spatially explicit deep learning neural network model for the prediction of landslide susceptibility","volume":"188","author":"Dao","year":"2020","journal-title":"Catena"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Meena, S.R., Mishra, B., and Tavakkoli, P.S. (2019). A hybrid spatial multi-criteria evaluation method for mapping landslide susceptible areas in Kullu Valley, Himalayas. Geosciences, 9.","DOI":"10.3390\/geosciences9040156"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104249","DOI":"10.1016\/j.catena.2019.104249","article-title":"Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment","volume":"186","author":"Sameen","year":"2020","journal-title":"Catena"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/s10346-006-0047-y","article-title":"Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models","volume":"4","author":"Lee","year":"2007","journal-title":"Landslides"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.cageo.2012.03.003","article-title":"Application of an evidential belief function model in landslide susceptibility mapping","volume":"44","author":"Althuwaynee","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1007\/s11069-012-0217-2","article-title":"Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran","volume":"63","author":"Pourghasemi","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jhydrol.2014.02.053","article-title":"Application of GIS based data driven evidential belief function model to predict groundwater potential zonation","volume":"513","author":"Nampak","year":"2014","journal-title":"J. Hydrol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Meena, S.R., Ghorbanzadeh, O., and Blaschke, T. (2019). A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020094"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.5194\/nhess-18-1919-2018","article-title":"Application of a physically based model to forecast shallow landslides at a regional scale","volume":"18","author":"Salvatici","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1080\/19475705.2018.1549111","article-title":"Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor","volume":"10","author":"Wang","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105213","DOI":"10.1016\/j.catena.2021.105213","article-title":"Fast physically-based model for rainfall-induced landslide susceptibility assessment at regional scale","volume":"201","author":"Medina","year":"2021","journal-title":"Catena"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10346-021-01775-6","article-title":"Impacts of future climate and land cover changes on landslide susceptibility: Regional scale modelling in the Val d\u2019Aran region (Pyrenees, Spain)","volume":"19","author":"Guo","year":"2022","journal-title":"Landslides"},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.5194\/nhess-13-2815-2013","article-title":"Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues","volume":"13","author":"Catani","year":"2013","journal-title":"Nat. Hazard Earth Syst. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"012032","DOI":"10.1088\/1755-1315\/20\/1\/012032","article-title":"Landslide susceptibility mapping using decision-tree based CHi-squared automatic interaction detection (CHAID) and Logistic regression (LR) integration","volume":"20","author":"Althuwaynee","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s10346-014-0466-0","article-title":"A novel ensemble decision tree-based CHi-squared Automatic Interaction Detection (CHAID) and multivariate logistic regression models in landslide susceptibility mapping","volume":"11","author":"Althuwaynee","year":"2014","journal-title":"Landslides"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1925","DOI":"10.1007\/s12665-014-3100-5","article-title":"Landslide susceptibility analysis based on ArcGIS and Artificial Neural Network for a large catchment in Three Gorges region, China","volume":"72","author":"Bi","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1007\/s13753-013-0021-y","article-title":"Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya","volume":"4","author":"Mondal","year":"2013","journal-title":"Int. J. Disaster Risk Sci."},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Hay, G.J., and Castilla, G. (2008). Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9_4"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, Y., and Chen, W. (2020). Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water, 12.","DOI":"10.3390\/w12010113"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, X., and Chen, W. (2020). Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens., 12.","DOI":"10.3390\/rs12142180"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-018-3488-4","article-title":"Analytic Hierarchy Process and Information Value Method based Landslide Susceptibility Mapping and Vehicle Vulnerability Assessment along a highway in Sikkim Himalaya","volume":"11","author":"Banerjee","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s41062-019-0245-9","article-title":"Landslide susceptibility mapping along national highway-1 in Jammu and Kashmir State (India)","volume":"4","author":"Hussain","year":"2019","journal-title":"Innov. Infrastruct. Solut."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1515\/geo-2019-0056","article-title":"GIS-based landslide susceptibility mapping using bivariate statistical methods in North-western Tunisia","volume":"11","author":"Anis","year":"2019","journal-title":"Open Geosci."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Pasang, S., and Kub\u00ed\u010dek, P. (2020). Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan. Geosciences, 10.","DOI":"10.3390\/geosciences10110430"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"101626","DOI":"10.1016\/j.asej.2021.10.021","article-title":"Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India","volume":"13","author":"Panchal","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_56","first-page":"338","article-title":"Landslide Susceptibility Assessment at a Part of Uttarakhand Himalaya, India using GIS\u2013based Statistical Approach of Frequency Ratio Method","volume":"4","author":"Prakash","year":"2015","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_57","first-page":"167","article-title":"Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in Kaski District, Nepal","volume":"11","author":"Baral","year":"2021","journal-title":"Int. J. Eng. Manag. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"13526","DOI":"10.1007\/s10668-021-01226-1","article-title":"Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India","volume":"23","author":"Sur","year":"2021","journal-title":"Environ. Dev. Sustain."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/s10661-022-09851-7","article-title":"GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya","volume":"194","author":"Das","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.envsoft.2009.10.016","article-title":"Landslide susceptibility assessment and factor effect analysis: Back propagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modeling","volume":"25","author":"Pradhan","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_61","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. J","volume":"124","author":"Kumar","year":"2015","journal-title":"Earth Syst. Sci."},{"key":"ref_62","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_63","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1007\/s12665-018-7451-1","article-title":"GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed, Saudi Arabia","volume":"77","author":"Mallick","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"137","DOI":"10.15406\/ijh.2018.02.00063","article-title":"Landslide modeling and susceptibility mapping using AHP and fuzzy approaches","volume":"2","author":"Noorollahi","year":"2018","journal-title":"Int. J. Hydrol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1080\/19475705.2020.1736190","article-title":"Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping","volume":"11","author":"Nachappa","year":"2020","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/0377-2217(95)00300-2","article-title":"Application of the extent analysis method on fuzzy AHP","volume":"95","author":"Chang","year":"1996","journal-title":"Eur. J. Oper. Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2013towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1080\/15230406.2014.902755","article-title":"Geons\u2014domain-specific regionalization of space","volume":"41","author":"Lang","year":"2014","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4771","DOI":"10.5194\/hess-22-4771-2018","article-title":"Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization","volume":"22","author":"Khosravi","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Deng, X., Li, L., and Tan, Y. (2017). Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity. ISPRS Int. J. Geogr. Inf. Syst., 6.","DOI":"10.3390\/ijgi6040103"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"145935","DOI":"10.1016\/j.scitotenv.2021.145935","article-title":"Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling\u2014Benefits of exploring landslide data collection effects","volume":"776","author":"Steger","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Marinos, V., Stoumpos, G., and Papazachos, C. (2019). Landslide hazard and risk assessment for a natural gas pipeline project: The case of the Trans Adriatic Pipeline, Albania Section. Geosciences, 9.","DOI":"10.3390\/geosciences9020061"},{"key":"ref_75","unstructured":"(2017, February 20). National Remote Sensing Centre (NRSC) Database, Available online: https:\/\/www.nrsc.gov.in\/EOP_irsdata_DOI\/page_1."},{"key":"ref_76","unstructured":"(2016, June 15). Indian Meteorological Department (IMD) Database, Available online: https:\/\/mausam.imd.gov.in\/."},{"key":"ref_77","unstructured":"(2017, February 20). National Bureau of Soil Survey (NBSS) Database, Available online: https:\/\/nbsslup.icar.gov.in\/soil-resource-studiessrs\/."},{"key":"ref_78","unstructured":"(2017, February 20). Bureau of Indian Standards (BIS) Database, Available online: https:\/\/pib.gov.in\/PressReleasePage.aspx?PRID=1740656."},{"key":"ref_79","unstructured":"(2016, June 15). U.S. Geological Survey (USGS) Database, Available online: https:\/\/earthquake.usgs.gov\/data\/vs30\/."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2478\/jlecol-2020-0014","article-title":"Assessment of Landscape Change of Lesser Himalayan Road Corridor of Uttarakhand, India","volume":"13","author":"Sur","year":"2020","journal-title":"J. Landsc. Ecol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1080\/19475705.2018.1445664","article-title":"Risk assessment model of expansive soil slope based on Fuzzy-AHP method and its engineering application","volume":"9","author":"Zhang","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/19475705.2013.843206","article-title":"A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)","volume":"6","author":"Pradhan","year":"2015","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"29797","DOI":"10.1038\/srep29797","article-title":"Optimized volume models of earthquake-triggered landslides","volume":"6","author":"Xu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Liu, S., Yin, K., Zhou, C., Gui, L., Liang, X., Lin, W., and Zhao, B. (2021). Susceptibility Assessment for Landslide Initiated along Power Transmission Lines. Remote Sens., 13.","DOI":"10.3390\/rs13245068"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1937","DOI":"10.1007\/s10346-020-01602-4","article-title":"Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach","volume":"18","author":"Meena","year":"2021","journal-title":"Landslides"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12517-015-2150-7","article-title":"A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping","volume":"9","author":"Chen","year":"2016","journal-title":"Arab. J. Geosci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s12665-018-7524-1","article-title":"Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: A comparative study of Nojian watershed in Lorestan province, Iran","volume":"77","author":"Abedini","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1007\/s10346-016-0771-x","article-title":"A modified frequency ratio method for landslide susceptibility assessment","volume":"14","author":"Li","year":"2016","journal-title":"Landslides"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1080\/01431161.2016.1148282","article-title":"A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison","volume":"37","author":"Althuwaynee","year":"2016","journal-title":"Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Sifa, S.F., Mahmud, T., Tarin, M.A., and Haque, D.M.E. (2019). Event-based landslide susceptibility mapping using weights of evidence (WoE) and modified frequency ratio (MFR) model: A case study of Rangamati district in Bangladesh. Geol. Ecol. Landsc., 222\u2013235.","DOI":"10.1080\/24749508.2019.1619222"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/0377-2217(90)90057-I","article-title":"How to make a decision: The analytic hierarchy process","volume":"48","author":"Saaty","year":"1990","journal-title":"Eur. J. Oper. Res."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Ayhan, M.B. (2013). A Fuzzy AHP approach for supplier selection problem: A case study in a Gearmotor Company. Intl. J.  Manag. Value Supply Chains IJMVSC, 4.","DOI":"10.5121\/ijmvsc.2013.4302"},{"key":"ref_93","first-page":"199","article-title":"A fuzzy extension of Saaty\u2019s priority theory","volume":"11","author":"Laarhoven","year":"1983","journal-title":"Fuzzy Sets Syst."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.asoc.2009.11.017","article-title":"Fuzzy MCDM approach for selecting the best environment-watershed plan","volume":"11","author":"Chen","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.cageo.2014.08.001","article-title":"A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping","volume":"73","author":"Feizizadeh","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Shu, H., Guo, Z., Qi, S., Song, D., Pourghasemi, H.R., and Ma, J. (2021). Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China. Remote Sens., 13.","DOI":"10.3390\/rs13183623"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"5675","DOI":"10.1007\/s10706-021-01855-3","article-title":"Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria)","volume":"39","author":"Abdi","year":"2021","journal-title":"Geotech. Geol. Eng."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1016\/j.ecolmodel.2009.09.005","article-title":"A fuzzy analytic hierarchy process (FAHP) approach to eco-environmental vulnerability assessment for the Danjiangkou Reservoir area, China","volume":"220","author":"Li","year":"2009","journal-title":"Ecol. Model."},{"key":"ref_99","unstructured":"Hagenlocher, M., Kienberger, S., Lang, S., and Blaschke, T. (2014). Implications of spatial scales and reporting units for the spatial modelling of vulnerability to vector-borne diseases. GI_Forum, 197."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"193","DOI":"10.14358\/PERS.76.2.193","article-title":"Object-Based Class Modeling for Cadastre Constrained Delineation of Geo-Objects","volume":"2","author":"Tiede","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_101","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2000). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation. Angewandte Geographische Informationsverarbeitung XII, Herbert Wichmann."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object Based Image Analysis for Remote Sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated Parameterisation for Multi-Scale Image Segmentation on Multiple Layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"767","DOI":"10.5194\/nhess-9-767-2009","article-title":"Spatial Vulnerability Units\u2014Expert-Based Spatial Modelling of Socio-Economic Vulnerability in the Salzach Catchment, Austria","volume":"9","author":"Kienberger","year":"2009","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_106","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_107","doi-asserted-by":"crossref","unstructured":"Qianqian, B., Yumin, C., Susa, D., Qianjiao, W., Jiaxin, Y., and Jingyi, Z. (2017). An improved information value model based on gray clustering for landslide susceptibility mapping. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6010018"},{"key":"ref_108","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_109","doi-asserted-by":"crossref","unstructured":"Dikshit, A., Sarkar, R., Pradhan, B., Segoni, S., and Alamri, A.M. (2020). Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review. Appl. Sci., 10.","DOI":"10.3390\/app10072466"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s10346-013-0438-9","article-title":"Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region, Garhwal Himalayas, India","volume":"11","author":"Kanungo","year":"2014","journal-title":"Landslides"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s41062-018-0132-9","article-title":"Estimation of rainfall thresholds for landslide occurrences in Kalimpong, India","volume":"3","author":"Dikshit","year":"2018","journal-title":"Innov. Infrastruct. Solut."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1007\/s11069-018-3339-3","article-title":"Hazard evaluation of progressive Pawari landslide zone, Satluj valley, Himachal Pradesh, India","volume":"93","author":"Kumar","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/0013-7952(92)90053-2","article-title":"Landslide hazard evaluation and zonation mapping in mountainous terrain","volume":"32","author":"Anbalagan","year":"1992","journal-title":"Eng. Geol."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1007\/s11069-019-03714-0","article-title":"Comparative analysis of two rainfall retrieval algorithms during extreme rainfall event: A case study on cloudburst, 2010 over Ladakh (Leh), Jammu and Kashmir","volume":"97","author":"Banerjee","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1144\/GSL.QJEGH.1997.030.P1.03","article-title":"Slope stability of Tehri Dam Reservoir Area, India, using landslide hazard zonation (LHZ) mapping","volume":"30","author":"Gupta","year":"1997","journal-title":"Q. J. Eng. Geol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1080\/19475705.2015.1101026","article-title":"Hydrologically complemented deterministic slope stability analysis in part of Indian Lesser Himalaya","volume":"7","author":"Mathew","year":"2016","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.catena.2016.02.009","article-title":"Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India","volume":"142","author":"Sarkar","year":"2016","journal-title":"Catena"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10346-008-0134-3","article-title":"Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas","volume":"5","author":"Kanungo","year":"2008","journal-title":"Landslides"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.geomorph.2011.04.019","article-title":"Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India)","volume":"131","author":"Ghosh","year":"2011","journal-title":"Geomorphology"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.geomorph.2012.08.004","article-title":"Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models","volume":"179","author":"Das","year":"2012","journal-title":"Geomorphology"},{"key":"ref_121","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":"Modeling Earth Syst. Environ."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s11069-012-0365-4","article-title":"Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India","volume":"65","author":"Ramakrishnan","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Roy, J., Saha, S., Arabameri, A., Blaschke, T., and Tien Bui, D. (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_124","doi-asserted-by":"crossref","unstructured":"Batar, A.K., and Watanabe, T. (2021). Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms andWeights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030114"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s10668-021-01449-2","article-title":"Landslide hazard, vulnerability, and risk assessment (HVRA), Mussoorie township, lesser himalaya, India","volume":"24","author":"Ram","year":"2022","journal-title":"Environ. Dev. Sustain."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1016\/j.asr.2021.10.021","article-title":"A multi-criteria landslide susceptibility mapping using deep multi-layer perceptron network: A case study of Srinagar-Rudraprayag region (India)","volume":"69","author":"Meghanadh","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s10346-022-01861-3","article-title":"Landslide detection in the Himalayas using machine learning algorithms and U-Net","volume":"19","author":"Meena","year":"2022","journal-title":"Landslides"},{"key":"ref_128","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_129","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geomorph.2012.03.036","article-title":"Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale","volume":"161","author":"Schicker","year":"2012","journal-title":"Geomorphology"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S0013-7952(01)00087-4","article-title":"An objective method to rank the importance of the factors pre- disposing to landslides with the GIS methodology: Application to an area of the Apennines (Valnerina; Perugia, Italy)","volume":"63","author":"Donati","year":"2002","journal-title":"Eng. Geol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1953\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:56:18Z","timestamp":1760136978000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,18]]},"references-count":130,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081953"],"URL":"https:\/\/doi.org\/10.3390\/rs14081953","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,18]]}}}