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dc.contributor.authorMorales, Narkis S. [Univ Mayor, Fac Ciencias, Ctr Modelac & Monitoreo Ecosistemas, Santiago, Chile]es_CL
dc.contributor.authorFernández, Ignacio C. [Univ Mayor, Fac Ciencias, Ctr Modelac & Monitoreo Ecosistemas, Santiago, Chile]es_CL
dc.date.accessioned2020-04-12T14:11:55Z
dc.date.accessioned2020-04-14T15:37:41Z
dc.date.available2020-04-12T14:11:55Z
dc.date.available2020-04-14T15:37:41Z
dc.date.issued2019es_CL
dc.identifier.citationFernández, I. C., & Morales, N. S. (2019). One-class land-cover classification using MaxEnt: the effect of modelling parameterization on classification accuracy. PeerJ, 7, e7016.es_CL
dc.identifier.issn2167-8359es_CL
dc.identifier.urihttps://doi.org/10.7717/peerj.7016es_CL
dc.identifier.urihttp://repositorio.umayor.cl/xmlui/handle/sibum/6416
dc.description.abstractMultiple-class land-cover classification approaches can be inefficient when the main goal is to classify only one or a few classes. Under this scenario one-class classification algorithms could be a more efficient alternative. Currently there are several algorithms that can fulfil this task, with MaxEnt being one of the most promising. However, there is scarce information regarding parametrization for performing land-cover classification using MaxEnt. In this study we aimed to understand how MaxEnt parameterization affects the classification accuracy of four different land-covers (i.e., built-up, irrigated grass, evergreen trees and deciduous trees) in the city of Santiago de Chile. We also evaluated if MaxEnt manual parameterization outperforms classification results obtained when using MaxEnt default parameters setting. To accomplish our objectives, we generated a set of 25,344 classification maps (i.e., 6,336 for each assessed landcover), which are based on all the potential combination of 12 different classes of features restrictions, four regularization multipliers, four different sample sizes, three training/testing proportions, and 11 thresholds for generating the binary maps. Our results showed that with a good parameterization, MaxEnt can effectively classify different land covers with kappa values ranging from 0.68 for deciduous trees to 0.89 for irrigated grass. However, the accuracy of classification results is highly influenced by the type of land-cover being classified. Simpler models produced good classification outcomes for homogenous land-covers, but not for heterogeneous covers, where complex models provided better outcomes. In general, manual parameterization improves the accuracy of classification results, but this improvement will depend on the threshold used to generate the binary map. In fact, threshold selection showed to be the most relevant factor impacting the accuracy of the four land-cover classification. The number of sampling points for training the model also has a positive effect on classification results. However, this effect followed a logarithmic distribution, showing an improvement of kappa values when increasing the sampling from 40 to 60 points, but showing only a marginal effect if more than 60 sampling points are used. In light of these results, we suggest testing different parametrization and thresholds until satisfactory kappa or other accuracy metrics values are achieved. Our results highlight the huge potential that MaxEnt has a as a tool for one-class classification, but a good understanding of the software settings and model parameterization is needed to obtain reliable results.es_CL
dc.language.isoenes_CL
dc.publisherPEERJ INCes_CL
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile
dc.sourcePeerJ, MAY, 2019. 7
dc.subjectMultidisciplinary Scienceses_CL
dc.titleOne-class land-cover classification using MaxEnt: the effect of modelling parameterization on classification accuracyes_CL
dc.typeArtículoes_CL
umayor.facultadCIENCIAS
umayor.politicas.sherpa/romeoDOAJ Gold, Green Publishedes_CL
umayor.indexadoWOS:000469010000007es_CL
umayor.indexadoPMID: 31179194es_CL
dc.identifier.doiDOI: 10.7717/peerj.7016es_CL]
umayor.indicadores.wos-(cuartil)Q2es_CL
umayor.indicadores.scopus-(scimago-sjr)SCIMAGO/ INDICE H: 45 Hes_CL


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