Evaluation of classification techniques in Very-High-Resolution (VHR) imagery: A case study of the identification of deadwood in the Chilean Central-Patagonian Forests

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datacite.alternateIdentifier.citationECOLOGICAL INFORMATICS,Vol.69,,2022
datacite.alternateIdentifier.doi10.1016/j.ecoinf.2022.101685
datacite.creatorEsse, Carlos
datacite.creatorCondal, Alfonso
datacite.creatorDe los Rios Escalante, Patricio
datacite.creatorCorrea Araneda, Francisco
datacite.creatorMoreno Garcia, Roberto
datacite.creatorJara Falcon, Roderick
datacite.date2022
datacite.subject.englishWorldView-2 image
datacite.subject.englishFirewood industry
datacite.subject.englishSupervised classification
datacite.subject.englishKappa index
datacite.titleEvaluation of classification techniques in Very-High-Resolution (VHR) imagery: A case study of the identification of deadwood in the Chilean Central-Patagonian Forests
dc.date.accessioned2023-06-08T15:48:06Z
dc.date.available2023-06-08T15:48:06Z
dc.description.abstractDuring the past three decades, various methods have been developed to improve the classification accuracy in very high resolution (< 2 m) satellite data. This study's main goal was to evaluate and select the most suitable classification approach for detecting deadwood potentially useful for energy projects that would satisfy part of the demand for heating in the area. We compare five classification approaches using a WorldView-2 (Digital Global, Ins) standard, an orthorectified image of the Ays ' en region of the Chilean Patagonia. The five classifiers were evaluated and selecting the best one was carried out through a confusion matrix and Kappa coefficient. The results showed that the two non-parametric classifiers used (neural net and support vector machine) offered the best performance (98%) and the best Kappa coefficient (0.97). We conclude that it is essential to promote the development of innovative projects in native forests by local owners can contribute, to the formulation of public policies that directly benefit the Ays ' en region's inhabitants.
dc.identifier.urihttps://repositoriodigital.uct.cl/handle/10925/5196
dc.language.isoen
dc.publisherELSEVIER
dc.sourceECOLOGICAL INFORMATICS
oaire.resourceTypeWOS
oaire.resourceType.enArticle
uct.indizacionSCI
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