{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:27:00Z","timestamp":1773188820822,"version":"3.50.1"},"reference-count":115,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T00:00:00Z","timestamp":1569801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA","award":["NNX16AT79G"],"award-info":[{"award-number":["NNX16AT79G"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m2 were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500\u20131500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high\/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.<\/jats:p>","DOI":"10.3390\/rs11192284","type":"journal-article","created":{"date-parts":[[2019,9,30]],"date-time":"2019-09-30T05:58:33Z","timestamp":1569823113000},"page":"2284","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal"],"prefix":"10.3390","volume":"11","author":[{"given":"Pukar","family":"Amatya","sequence":"first","affiliation":[{"name":"Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD 21046, USA"},{"name":"Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]},{"given":"Dalia","family":"Kirschbaum","sequence":"additional","affiliation":[{"name":"Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]},{"given":"Thomas","family":"Stanley","sequence":"additional","affiliation":[{"name":"Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD 21046, USA"},{"name":"Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s11069-006-9100-3","article-title":"Trends in landslide occurrence in Nepal","volume":"43","author":"Petley","year":"2007","journal-title":"Nat. Hazards"},{"key":"ref_2","unstructured":"(2018, April 13). Central Bureau of Statistics Nepal Population and Housing Census 2011, Available online: https:\/\/cbs.gov.np\/wp-content\/upLoads\/2019\/07\/pulationandhousing-census-2011.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1080\/09614524.2018.1424802","article-title":"Access to infrastructure and human well-being: Evidence from rural Nepal","volume":"28","author":"Sapkota","year":"2018","journal-title":"Dev. Pract."},{"key":"ref_4","unstructured":"Ligal, P.R. (2018, March 20). Karnali Area Development: A Strategic Frame-Work. Available online: http:\/\/prad-nepal.com\/wp-content\/uploads\/2015\/09\/Karnali-area-development-Strategic-framework1.pdf."},{"key":"ref_5","unstructured":"(2018, April 13). World Food Programme A Sub-Regional Hunger Index for Nepal. Available online: http:\/\/neksap.org.np\/uploaded\/resources\/Publications-and-Research\/Reports\/A Sub-Regional Hunger Index for Nepal, July 2009.pdf."},{"key":"ref_6","unstructured":"Ahmed, F., and Regmi, P.P. (2018, April 03). Study on the Transport Constrains in Western Nepal (Karnali Highway Transport Corridor). Available online: http:\/\/archive.rapnepal.com\/report-publication\/study-transport-constrains-western-nepal-karnali-highway-transport-corridor."},{"key":"ref_7","unstructured":"(2018, March 20). World Food Programme More than Roads: Using Markets to Feed the Hungry in Nepal. Available online: http:\/\/www.cashlearning.org\/downloads\/resources\/documents\/more-than-roads_using-markest-to-feed-the-hungry-in-nepal-_july-2010.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.geomorph.2005.06.002","article-title":"Probabilistic landslide hazard assessment at the basin scale","volume":"72","author":"Guzzetti","year":"2005","journal-title":"Geomorphology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.enggeo.2004.06.001","article-title":"Use of fuzzy relations to produce landslide susceptibility map of a landslide prone area (West Black Sea Region, Turkey)","volume":"75","author":"Ercanoglu","year":"2004","journal-title":"Eng. Geol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s100640050066","article-title":"Landslide hazard assessment: Summary review and new perspectives","volume":"58","author":"Aleotti","year":"1999","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_11","first-page":"39","article-title":"Geo-information tools for landslide risk assessment: An overview of recent developments","volume":"1","year":"2004","journal-title":"Landslides Eval. Stab."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0169-555X(99)00078-1","article-title":"Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy","volume":"31","author":"Guzzetti","year":"1999","journal-title":"Geomorphology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1007\/s11069-012-0163-z","article-title":"Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal","volume":"63","author":"Kayastha","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s11707-012-0337-8","article-title":"Application of fuzzy logic approach for landslide susceptibility mapping in Garuwa sub-basin, East Nepal","volume":"6","author":"Kayastha","year":"2012","journal-title":"Front. Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/s10346-012-0361-5","article-title":"Evaluation of the consistency of landslide susceptibility mapping: A case study from the Kankai watershed in east Nepal","volume":"10","author":"Kayastha","year":"2013","journal-title":"Landslides"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s12594-013-0147-y","article-title":"GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola area, Eastern Nepal","volume":"82","author":"Kayastha","year":"2013","journal-title":"J. Geol. Soc. India"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.cageo.2012.11.003","article-title":"Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal","volume":"52","author":"Kayastha","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8601","DOI":"10.1007\/s12517-015-1831-6","article-title":"Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: A case study from Garuwa sub-basin, East Nepal","volume":"8","author":"Kayastha","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1007\/s12594-013-0025-7","article-title":"Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal","volume":"81","author":"Kayastha","year":"2013","journal-title":"J. Geol. Soc. India"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s00254-007-0818-3","article-title":"GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping","volume":"54","author":"Dahal","year":"2008","journal-title":"Environ. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.geomorph.2008.05.041","article-title":"Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence","volume":"102","author":"Dahal","year":"2008","journal-title":"Geomorphology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1007\/s11629-013-2847-6","article-title":"Landslide susceptibility mapping along Bhalubang\u2014Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models","volume":"11","author":"Regmi","year":"2014","journal-title":"J. Mt. Sci."},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s11069-010-9569-7","article-title":"Landslide occurrence and its relation with terrain factors in the Siwalik Hills, Nepal: Case study of susceptibility assessment in three basins","volume":"56","author":"Ghimire","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1007\/s12517-012-0569-7","article-title":"A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi\u2013Dhad Khola, east Nepal","volume":"6","author":"Bijukchhen","year":"2013","journal-title":"Arab. J. Geosci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s12665-009-0426-5","article-title":"Landslide susceptibility maps comparing frequency ratio and artificial neural networks: A case study from the Nepal Himalaya","volume":"61","author":"Poudyal","year":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2307\/3674109","article-title":"Landslide hazard mapping and the application of GIS in the Kulekhani watershed, Nepal","volume":"19","author":"Dhakal","year":"1999","journal-title":"Mt. Res. Dev."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.geomorph.2014.05.031","article-title":"Distribution probability of large-scale landslides in central Nepal","volume":"226","author":"Timilsina","year":"2014","journal-title":"Geomorphology"},{"key":"ref_30","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_31","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_32","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.geomorph.2006.09.023","article-title":"Comparing landslide inventory maps","volume":"94","author":"Galli","year":"2008","journal-title":"Geomorphology"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, R.S., Tiede, D., and Aryal, J. (2019). Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sens., 11.","DOI":"10.3390\/rs11020196"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111235","DOI":"10.1016\/j.rse.2019.111235","article-title":"Landslide mapping from multi-sensor data through improved change detection-based Markov random field","volume":"231","author":"Lu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10346-018-1109-7","article-title":"Evaluation of landslides process and potential in Shenmu sub-watersheds, central Taiwan","volume":"16","author":"Lin","year":"2019","journal-title":"Landslides"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.geomorph.2009.10.004","article-title":"Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods","volume":"116","author":"Martha","year":"2010","journal-title":"Geomorphology"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sun, W., Tian, Y., Mu, X., Zhai, J., Gao, P., and Zhao, G. (2017). Loess landslide inventory map based on GF-1 satellite imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040314"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"687","DOI":"10.14358\/PERS.72.6.687","article-title":"High spatial resolution satellite imagery, DEM derivatives, and image segmentation for the detection of mass wasting processes","volume":"72","author":"Barlow","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_40","unstructured":"Moine, M., Puissant, A., and Malet, J.-P. (2009). Detection of landslides from aerial and satellite images with a semi-automatic method. Application to the Barcelonnette basin (Alpes-de-Hautes-Provence, France). Landslide Processes\u2014From Geomorphologic Mapping to Dynamic Modelling, HAL."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4928","DOI":"10.1109\/TGRS.2011.2151866","article-title":"Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis","volume":"49","author":"Martha","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.isprsjprs.2011.11.004","article-title":"Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories","volume":"67","author":"Martha","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s12524-015-0532-7","article-title":"Identification of new Landslides from High Resolution Satellite Data Covering a Large Area Using Object-Based Change Detection Methods","volume":"44","author":"Martha","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.5194\/nhess-11-2715-2011","article-title":"Landslide mapping with multi-scale object-based image analysis\u2013a case study in the Baichi watershed, Taiwan","volume":"11","author":"Lahousse","year":"2011","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_47","first-page":"30","article-title":"Object-oriented identification of forested landslides with derivatives of single pulse LiDAR data","volume":"173","author":"Kerle","year":"2012","journal-title":"Geomorphology"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1007\/s12145-015-0217-3","article-title":"An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan","volume":"8","author":"Friedl","year":"2015","journal-title":"Earth Sci. Inform."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.3390\/rs4051310","article-title":"A semi-automated object-based approach for landslide detection validated by persistent scatterer interferometry measures and landslide inventories","volume":"4","author":"Antolini","year":"2012","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1007\/s11069-012-0505-x","article-title":"A new approach of combining aerial photography with satellite imagery for landslide detection","volume":"66","author":"Li","year":"2013","journal-title":"Nat. Hazards"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"9705","DOI":"10.3390\/rs70809705","article-title":"Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8026","DOI":"10.3390\/rs6098026","article-title":"Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data","volume":"6","author":"Behling","year":"2014","journal-title":"Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1109\/TGRS.2013.2250293","article-title":"Semiautomatic object-oriented landslide recognition scheme from multisensor optical imagery and DEM","volume":"52","author":"Rau","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/JSTARS.2014.2350036","article-title":"Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, Iran","volume":"7","author":"Blaschke","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"4318","DOI":"10.3390\/rs70404318","article-title":"Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm","volume":"7","author":"Dou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.5194\/nhess-16-1035-2016","article-title":"Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island","volume":"16","author":"Heleno","year":"2016","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, T., Trinder, C.J., and Niu, R. (2017). Object-Oriented Landslide Mapping Using ZY-3 Satellite Imagery, Random Forest and Mathematical Morphology, for the Three-Gorges Reservoir, China. Remote Sens., 9.","DOI":"10.3390\/rs9040333"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.geomorph.2013.09.012","article-title":"Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method","volume":"204","author":"Moosavi","year":"2014","journal-title":"Geomorphology"},{"key":"ref_59","first-page":"1","article-title":"A comparative analysis of pixel-and object-based detection of landslides from very high-resolution images","volume":"64","author":"Keyport","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinform."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.cageo.2016.12.007","article-title":"A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal","volume":"100","author":"Yu","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s10346-017-0884-x","article-title":"A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: A case study of national Nepal","volume":"15","author":"Chen","year":"2018","journal-title":"Landslides"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s12665-018-7516-1","article-title":"Analysis of satellite-derived landslide at Central Nepal from 2011 to 2016","volume":"77","author":"Yu","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sharma, K., Saraf, A.K., Das, J., Baral, S.S., Borgohain, S., and Singh, G. (2017). Mapping and Change Detection Study of Nepal-2015 Earthquake Induced Landslides. J. Indian Soc. Remote Sens.","DOI":"10.1007\/s12524-017-0720-8"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"185","DOI":"10.5194\/nhess-18-185-2018","article-title":"Satellite-based emergency mapping using optical imagery: Experience and reflections from the 2015 Nepal earthquakes","volume":"18","author":"Williams","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1002\/2013EO130002","article-title":"High-resolution satellite data open for government research","volume":"94","author":"Neigh","year":"2013","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_66","unstructured":"(2019, August 13). NASA Evaluates Commercial Small-Sat Earth Data for Science, Available online: https:\/\/www.nasa.gov\/press-release\/nasa-evaluates-commercial-small-sat-earth-data-for-science."},{"key":"ref_67","unstructured":"Amatya, K.M., Jnawali, B.M., and Shrestha, P.L. (1994). Geological Map of Nepal: Kathmandu, 1994: Scale: 1:1,000,000, Department of Mines & Geology."},{"key":"ref_68","unstructured":"(2019, February 05). Planet Team Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. Available online: https:\/\/api.planet.com."},{"key":"ref_69","unstructured":"(2018, March 20). DigitalGlobe DigitalGlobe\u2019s Core Imagery Products Guide V1.1. Available online: https:\/\/geomatics.planet.com\/upload\/digitalglobe\/DigitalGlobe Core Imagery Products Guide.pdf."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"125","DOI":"10.5194\/isprs-archives-XLI-B4-125-2016","article-title":"NASADEM global elevation model: Methods and progress","volume":"41","author":"Crippen","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_71","unstructured":"(2018, January 10). Polar Geospatial Center\u2019s Orthorectification Tools. Available online: https:\/\/github.com\/PolarGeospatialCenter\/imagery_utils."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/B:NHAZ.0000026786.85589.4a","article-title":"Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques","volume":"32","author":"Ercanoglu","year":"2004","journal-title":"Nat. Hazards"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.13101\/ijece.5.1","article-title":"Rainfall-induced landslides in Nepal","volume":"5","author":"Dahal","year":"2012","journal-title":"Int. J. Eros. Control Eng."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.envsoft.2009.10.016","article-title":"Landslide susceptibility assessment and factor effect analysis: Backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling","volume":"25","author":"Pradhan","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.geomorph.2008.03.003","article-title":"GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region","volume":"101","author":"Kamp","year":"2008","journal-title":"Geomorphology"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/S0013-7952(97)81260-4","article-title":"Landslide susceptibility mapping of the slopes in the residual soils of the Mengen region (Turkey) by deterministic stability analyses and image processing techniques","volume":"44","author":"Aksoy","year":"1996","journal-title":"Eng. Geol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s11069-017-2757-y","article-title":"A heuristic approach to global landslide susceptibility mapping","volume":"87","author":"Stanley","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jenvman.2014.07.047","article-title":"Development of 2010 national land cover database for the Nepal","volume":"148","author":"Uddin","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_79","unstructured":"(2015, June 07). OpenStreetMap Contributors OpenStreetMap. Available online: http:\/\/osm-x-tractor.org\/Data.aspx."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Burnett, C., and Pekkarinen, A. (2004). Image segmentation methods for object-based analysis and classification. Remote Sensing Image Analysis: Including the Spatial Domain, Springer.","DOI":"10.1007\/978-1-4020-2560-0_12"},{"key":"ref_81","unstructured":"Strobl, J., Blaschke, T., and Griesebner, G. (2000). Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informationsverarbeitung XII, Wichmann-Verlag."},{"key":"ref_82","unstructured":"(2019, August 30). Trimble eCognition 2017. Available online: http:\/\/www.ecognition.com\/."},{"key":"ref_83","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_84","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_85","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12524-017-0685-7","article-title":"A tool assessing optimal multi-scale image segmentation","volume":"46","author":"Vamsee","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4209","DOI":"10.3390\/rs5094209","article-title":"Transferability of object-oriented image analysis methods for slum identification","volume":"5","author":"Kohli","year":"2013","journal-title":"Remote Sens."},{"key":"ref_87","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Oakland, CA, USA."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B. (2011, January 20\u201324). Finding a \u201ckneedle\u201din a haystack: Detecting knee points in system behavior. Proceedings of the 31st International Conference on Distributed Computing Systems, Minneapolis, MN, USA. Available online: http:\/\/www1.icsi.berkeley.edu\/barath\/papers\/kneedle-simplex11.pdf.","DOI":"10.1109\/ICDCSW.2011.20"},{"key":"ref_89","unstructured":"(2018, January 10). Knee-Point Detection in Python. Available online: https:\/\/github.com\/arvkevi\/kneed."},{"key":"ref_90","unstructured":"Strahler, A.N. (1965). Introduction to Physical Geography, Food and Agriculture Organization."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Haralick, R.M., and Shanmugam, K. (1973). Textural features for image classification. IEEE Trans. Syst. Man. Cybern., 610\u2013621.","DOI":"10.1109\/TSMC.1973.4309314"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S0098-3004(97)00117-9","article-title":"Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy","volume":"24","author":"Atkinson","year":"1998","journal-title":"Comput. Geosci."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"H\u00f6lbling, D., Betts, H., Spiekermann, R., and Phillips, C. (2016). Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography. Geoscience, 6.","DOI":"10.3390\/geosciences6040048"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.geomorph.2005.12.003","article-title":"Prediction of landslide susceptibility using rare events logistic regression: A case-study in the Flemish Ardennes (Belgium)","volume":"76","author":"Vanwalleghem","year":"2006","journal-title":"Geomorphology"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"105","DOI":"10.5194\/nhess-18-105-2018","article-title":"Field-based landslide susceptibility assessment in a data-scarce environment: The populated areas of the Rwenzori Mountains","volume":"18","author":"Jacobs","year":"2018","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"127","DOI":"10.2113\/gseegeosci.16.2.127","article-title":"Mapping landslide hazards in western Nepal: Comparing qualitative and quantitative approaches","volume":"16","author":"Regmi","year":"2010","journal-title":"Environ. Eng. Geosci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.geomorph.2004.06.010","article-title":"The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan","volume":"65","author":"Ayalew","year":"2005","journal-title":"Geomorphology"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.geomorph.2009.09.025","article-title":"GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China","volume":"115","author":"Bai","year":"2010","journal-title":"Geomorphology"},{"key":"ref_100","unstructured":"Jenks, G.F. (1977). Optimal Data Classification for Choropleth Maps, Department of Geographiy, University of Kansas Occasional Paper."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1126\/science.3287615","article-title":"Measuring the accuracy of diagnostic systems","volume":"240","author":"Swets","year":"1988","journal-title":"Science"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/clinchem\/39.4.561","article-title":"Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine","volume":"39","author":"Zweig","year":"1993","journal-title":"Clin. Chem."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Chen, Q., Liu, X., Liu, C., and Ji, R. (2013, January 23\u201325). Impact analysis of different spatial resolution DEM on object-oriented landslide extraction from high resolution remote sensing images. Proceedings of the 2013 Ninth International Conference on Natural Computation (ICNC), Shenyang, China.","DOI":"10.1109\/ICNC.2013.6818111"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"723","DOI":"10.5194\/nhess-15-723-2015","article-title":"Amalgamation in landslide maps: Effects and automatic detection","volume":"15","author":"Marc","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1002\/2013GC005067","article-title":"Seismic mountain building: Landslides associated with the 2008 Wenchuan earthquake in the context of a generalized model for earthquake volume balance","volume":"15","author":"Li","year":"2014","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Golovko, D., Roessner, S., Behling, R., Wetzel, H.-U., and Kleinschmit, B. (2017). Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan. Remote Sens., 9.","DOI":"10.3390\/rs9090943"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/j.geomorph.2009.09.023","article-title":"Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India)","volume":"114","author":"Das","year":"2010","journal-title":"Geomorphology"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.enggeo.2018.02.020","article-title":"Landslide hazard assessment in the Himalayas (Nepal and Bhutan) based on Earth-Observation data","volume":"237","author":"Ambrosi","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Sun, Q., Hu, J., Zhang, L., and Ding, X. (2016). Towards slow-moving landslide monitoring by integrating multi-sensor InSAR time series datasets: The Zhouqu case study, China. Remote Sens., 8.","DOI":"10.3390\/rs8110908"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s40677-017-0071-3","article-title":"Landslide hazard map: Tool for optimization of low-cost mitigation","volume":"4","author":"Dahal","year":"2017","journal-title":"Geoenviron. Disasters"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.jtrangeo.2009.06.016","article-title":"GIS-based highway maintenance prioritization model: An integrated approach for highway maintenance in Nepal mountains","volume":"18","author":"Pantha","year":"2010","journal-title":"J. Transp. Geogr."},{"key":"ref_113","first-page":"30","article-title":"NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG)","volume":"4","author":"Huffman","year":"2015","journal-title":"Algorithm Theor. basis Doc. Version"},{"key":"ref_114","unstructured":"Bright, E.A., Rose, A.N., and Urban, M.L. (2016). Landscan 2015 High-Resolution Global Population Data Set, Oak Ridge National Lab. (ORNL)."},{"key":"ref_115","unstructured":"CIESIN (2005). Gridded Population of the World Version 3 (GPWV3): Population Density Grids, Columbia University."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2284\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:26:10Z","timestamp":1760189170000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2284"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,30]]},"references-count":115,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11192284"],"URL":"https:\/\/doi.org\/10.3390\/rs11192284","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,30]]}}}