{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T06:57:28Z","timestamp":1772261848151,"version":"3.50.1"},"reference-count":71,"publisher":"Copernicus GmbH","issue":"4","license":[{"start":{"date-parts":[[2016,4,26]],"date-time":"2016-04-26T00:00:00Z","timestamp":1461628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PTDC\/ECM\/116611\/2010"],"award-info":[{"award-number":["PTDC\/ECM\/116611\/2010"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat. Hazards Earth Syst. Sci."],"abstract":"<jats:p>Abstract. A\u00a0method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a\u00a0support vector machine classifier and is tested using a\u00a0GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20\u00a0February\u00a02010), and a\u00a0pre-event lidar digital terrain model. The testing is developed in a\u00a015\u202fkm2 wide study area, where 95\u202f% of the number of landslides scars are detected by this supervised approach. The classifier presents a\u00a0good performance in the delineation of the overall landslide area, with commission errors below 26\u202f% and omission errors below 24\u202f%. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.<\/jats:p>","DOI":"10.5194\/nhess-16-1035-2016","type":"journal-article","created":{"date-parts":[[2016,4,26]],"date-time":"2016-04-26T02:27:52Z","timestamp":1461637672000},"page":"1035-1048","source":"Crossref","is-referenced-by-count":30,"title":["Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island"],"prefix":"10.5194","volume":"16","author":[{"given":"Sandra","family":"Heleno","sequence":"first","affiliation":[]},{"given":"Magda","family":"Matias","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3199-7961","authenticated-orcid":false,"given":"Pedro","family":"Pina","sequence":"additional","affiliation":[]},{"given":"Ant\u00f3nio Jorge","family":"Sousa","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2016,4,26]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Aksoy,\u00a0B. and Ercanoglu,\u00a0M.: Landslide identification and classification by object-based image analysis and fuzzy logic: an example from the Azdavay region (Kastamonu, Turkey), Comput. 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