Mapping coastal wetlands using satellite imagery and machine learning in a highly urbanized landscape

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dc.article.number5700
dc.contributor.authorMunizaga, Juan
dc.contributor.authorGarcía, Mariano
dc.contributor.authorUreta, Fernando
dc.contributor.authorNovoa, Vanessa
dc.contributor.authorRojas, Octavio
dc.contributor.authorRojas Quezada, Carolina Alejandra
dc.contributor.otherCEDEUS (Chile)
dc.date.accessioned2022-11-25T15:41:57Z
dc.date.available2022-11-25T15:41:57Z
dc.date.issued2022
dc.description.abstractCoastal wetlands areas are heterogeneous, highly dynamic areas with complex interactions between terrestrial and marine ecosystems, making them essential for the biosphere and the development of human activities. Remote sensing offers a robust and cost-efficient mean to monitor coastal landscapes. In this paper, we evaluate the potential of using high resolution satellite imagery to classify land cover in a coastal area in Concepción, Chile, using a machine learning (ML) approach. Two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), were evaluated using four different scenarios: (I) using original spectral bands; (II) incorporating spectral indices; (III) adding texture metrics derived from the grey-level covariance co-occurrence matrix (GLCM); and (IV) including topographic variables derived from a digital terrain model. Both methods stand out for their excellent results, reaching an average overall accuracy of 88% for support vector machine and 90% for random forest. However, it is statistically shown that random forest performs better on this type of landscape. Furthermore, incorporating Digital Terrain Model (DTM)-derived metrics and texture measures was critical for the substantial improvement of SVM and RF. Although DTM did not increase the accuracy in SVM, this study makes a methodological contribution to the monitoring and mapping of water bodies’ landscapes in coastal cities with weak governance and data scarcity in coastal management.
dc.format.extent19 páginas
dc.fuente.origenSIPA
dc.identifier.doi10.3390/su14095700
dc.identifier.urihttps://doi.org/10.3390/su14095700
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/65765
dc.information.autorucInstituto de estudios urbanos y territoriales ; Rojas Quezada, Carolina Alejandra ; 0000-0001-9505-4252 ; 1085840
dc.language.isoen
dc.nota.accesoContenido completo
dc.revistaSustainabilityes_ES
dc.rightsacceso abierto
dc.subjectCoastal wetlandses_ES
dc.subjectRemote sensinges_ES
dc.subjectCoastal citieses_ES
dc.subjectRapidEyees_ES
dc.subjectMachine learninges_ES
dc.subject.ods15 Life on land
dc.subject.ods09 Industry, innovation and infrastructure
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleMapping coastal wetlands using satellite imagery and machine learning in a highly urbanized landscapees_ES
dc.typeartículo
dc.volumen14
sipa.codpersvinculados1085840
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