dc.contributorUniversidade Federal de São Carlos (UFSCar)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2021-06-25T10:32:33Z
dc.date.accessioned2022-12-19T22:17:27Z
dc.date.available2021-06-25T10:32:33Z
dc.date.available2022-12-19T22:17:27Z
dc.date.created2021-06-25T10:32:33Z
dc.date.issued2021-01-01
dc.identifierEarth Science Informatics.
dc.identifier1865-0481
dc.identifier1865-0473
dc.identifierhttp://hdl.handle.net/11449/206468
dc.identifier10.1007/s12145-021-00639-w
dc.identifier2-s2.0-85107737248
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5387065
dc.description.abstractUrban Structure Types (USTs) stand for areas with homogeneous appearance over the urban matrix. The use of spatial metrics rises as a convenient alternative to quantify the homogeneity of areas on a specific scale. Remote sensing imagery is largely used to assess and study the urban environment, and its classification is a way to recreate the Earth’s surface digitally, both natural and urban spaces. This study proposes a method for city-scale UST mapping using remote sensing images as the unique source of information. Such a proposal comprehends the classification of images that express spatial metrics derived from previous land use and land cover (LULC) classification. We carried two case studies to assess the proposed method under different image resolutions and urban complexity conditions. For this purpose, Landsat-8 OLI and Sentinel-2 MSI images acquired from different cities in Brazil are submitted to the proposed method. An alternative object-based image classification method is included as a comparison baseline. The proposed method shows efficiency in the UST mapping process, which is highly influenced by the neighborhood size considered over the process. Also, it is verified that the proposed method is superior at a significance level of 5%.
dc.languageeng
dc.relationEarth Science Informatics
dc.sourceScopus
dc.subjectImage classification
dc.subjectRemote sensing
dc.subjectSpatial metrics
dc.subjectUrban mapping
dc.subjectUrban structure types
dc.titleUrban structure type mapping method using spatial metrics and remote sensing imagery classification
dc.typeArtículos de revistas


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