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dc.contributor.author | Carbonell-Rivera, Juan Pedro | es_ES |
dc.contributor.author | Torralba, Jesús | es_ES |
dc.contributor.author | Estornell Cremades, Javier | es_ES |
dc.contributor.author | Ruiz Fernández, Luis Ángel | es_ES |
dc.contributor.author | Crespo-Peremarch, Pablo | es_ES |
dc.date.accessioned | 2023-05-12T18:02:06Z | |
dc.date.available | 2023-05-12T18:02:06Z | |
dc.date.issued | 2022-01 | es_ES |
dc.identifier.issn | 2072-4292 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/193327 | |
dc.description.abstract | [EN] Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species¿ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models. | es_ES |
dc.description.sponsorship | Grants BES-2017-081920 and PID2020-117808RB-C21 funded by MCIN/AEI/10.13039/5011 00011033 and by ESF Investing in your future. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Remote Sensing | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Unmanned Aerial Vehicles (UAVs) | es_ES |
dc.subject | Digital Aerial Photogrammetry (DAP) | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Point cloud labelling | es_ES |
dc.subject | Mediterranean forest | es_ES |
dc.subject.classification | INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA | es_ES |
dc.title | Classification of Mediterranean Shrub Species from UAV Point Clouds | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/rs14010199 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117808RB-C21/ES/CARTOGRAFIADO ESPECTRAL Y ESTRUCTURAL 3D DE COMBUSTIBLE MEDITERRANEO PARA LA MODELIZACION DEL COMPORTAMIENTO DEL FUEGO/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//BES-2017-081920//AYUDA PARA CONTRATOS PREDOCTORALES PARA LA FORMACION DE DOCTORES-CARBONELL RIVERA. PROYECTO: ANALISIS Y VALIDACION DE PARAMETROS DE ESTRUCTURA FORESTAL DERIVADOS DE LIDAR Y OTRAS TECNICAS EMERGENTES/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica - Escola Tècnica Superior d'Enginyeria Geodèsica, Cartogràfica i Topogràfica | es_ES |
dc.description.bibliographicCitation | Carbonell-Rivera, JP.; Torralba, J.; Estornell Cremades, J.; Ruiz Fernández, LÁ.; Crespo-Peremarch, P. (2022). Classification of Mediterranean Shrub Species from UAV Point Clouds. Remote Sensing. 14(1):1-22. https://doi.org/10.3390/rs14010199 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/rs14010199 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 22 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 14 | es_ES |
dc.description.issue | 1 | es_ES |
dc.relation.pasarela | S\452932 | es_ES |
dc.contributor.funder | European Social Fund | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.subject.ods | 13.- Tomar medidas urgentes para combatir el cambio climático y sus efectos | es_ES |
dc.subject.ods | 15.- Proteger, restaurar y promover la utilización sostenible de los ecosistemas terrestres, gestionar de manera sostenible los bosques, combatir la desertificación y detener y revertir la degradación de la tierra, y frenar la pérdida de diversidad biológica | es_ES |