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Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms

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dc.contributor.authorValdivieso Ros, Carmen
dc.contributor.authorAlonso Sarria, Francisco
dc.contributor.authorGomariz Castillo, Francisco
dc.contributor.departmentGeografía
dc.date.accessioned2024-01-15T12:23:11Z
dc.date.available2024-01-15T12:23:11Z
dc.date.issued2023-01-05
dc.description© 2023 by the authors.. This document is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the published version of a published work that appeared in final form in Remote Sensinges
dc.description.abstractLand cover classification in semiarid areas is a difficult task that has been tackled using different strategies, such as the use of normalized indices, texture metrics, and the combination of images from different dates or different sensors. In this paper we present the results of an experiment using three sensors (Sentinel-1 SAR, Sentinel-2 MSI and LiDAR), four dates and different normalized indices and texture metrics to classify a semiarid area. Three machine learning algorithms were used: Random Forest, Support Vector Machines and Multilayer Perceptron; Maximum Likelihood was used as a baseline classifier. The synergetic use of all these sources resulted in a significant increase in accuracy, Random Forest being the model reaching the highest accuracy. However, the large amount of features (126) advises the use of feature selection to reduce this figure. After using Variance Inflation Factor and Random Forest feature importance, the amount of features was reduced to 62. The final overall accuracy obtained was 0.91 & PLUSMN; 0.005 (alpha = 0.05) and kappa index 0.898 & PLUSMN; 0.006 (alpha = 0.05). Most of the observed confusions are easily explicable and do not represent a significant difference in agronomic terms.es
dc.formatapplication/pdfes
dc.format.extent29es
dc.identifier.citationRemote Sensing, 15,312. 2023
dc.identifier.doihttps://doi.org/ 10.3390/rs15020312
dc.identifier.issnElectronic: 2072-4292
dc.identifier.urihttp://hdl.handle.net/10201/137317
dc.languageenges
dc.publisherMultidisciplinary Digital Publishing Institutees
dc.relationSpanish Ministry of Economy, Industry and Competitiveness/Agencia Estatal de Investigacion/FEDER (Fondo Europeo de Desarrollo Regional). Proyect "Impulsores de cambio de los servicios ecosistemicos de los cauces mediterraneos efimeros: cambio climatico vs cambio de usos de suelo", Grant Number CGL2017-84625-C2-2-R.es
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/15/2/312es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRandom forestes
dc.subjectSupport vector machineses
dc.subjectMultilayer perceptrones
dc.subjectFeature selectiones
dc.subjectSentinel missionses
dc.subjectMultisensores
dc.subject.otherCDU::9 - Geografía e historiaes
dc.titleEffect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithmses
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
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