dc.contributor | Facultad de Ciencias Biologicas y Ambientales | es_ES |
dc.contributor.author | Pérez Rodríguez, Luis Alfonso | |
dc.contributor.author | Quintano Pastor, Carmen | |
dc.contributor.author | Marcos Porras, Elena María | |
dc.contributor.author | Suárez Seoane, Susana | |
dc.contributor.author | Calvo Galván, María Leonor | |
dc.contributor.author | Fernández Manso, Alfonso | |
dc.contributor.other | Ecologia | es_ES |
dc.date | 2020 | |
dc.date.accessioned | 2024-05-02T07:05:06Z | |
dc.date.available | 2024-05-02T07:05:06Z | |
dc.identifier.citation | Pérez-Rodríguez, L. A., Quintano, C., Marcos, E., Suarez-Seoane, S., Calvo, L. & Fernández-Manso, A. (2020). Evaluation of prescribed fires from unmanned aerial vehicles (UAVs) imagery and machine learning algorithms. Remote Sensing, 12(8) Article e1295. https://doi.org/10.3390/RS12081295 | es_ES |
dc.identifier.other | https://www.mdpi.com/2072-4292/12/8/1295 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/20227 | |
dc.description | This article belongs to the Special Issue New Remote Sensing Technologies in Forest Fire Analysis, Prevention and Mitigation | es_ES |
dc.description.abstract | [EN] Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes | es_ES |
dc.language | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Ecología. Medio ambiente | es_ES |
dc.subject | Ingeniería forestal | es_ES |
dc.subject.other | Unmanned Aerial Vehicles (UAVs) | es_ES |
dc.subject.other | Fire severity | es_ES |
dc.subject.other | Probabilistic Neural Network Classifier (PNN) | es_ES |
dc.subject.other | Prescribed burning | es_ES |
dc.subject.other | Parrot SEQUOIA | es_ES |
dc.title | Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.3390/rs12081295 | |
dc.description.peerreviewed | SI | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Programa Estatal de I+D+i Orientada a los Retos de la Sociedad/AGL2017-86075-C2-1-R/ES/Severidad de grandes incendios en sistemas forestales propensos al fuego: condicionantes, efectos en la provision de servicios y soluciones de gestion pre- y post-incendio/FIRESEVES | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/Junta de Castilla y León//LE001P17/ES/Identificación de estructuras forestales relacionadas con la severidad en grandes incendios y sus efectos en la provisión de servicios ecosistémicos con importancia socioeconómica en Castilla y León /SEFIRECYL | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 2072-4292 | |
dc.journal.title | Remote Sensing | es_ES |
dc.volume.number | 12 | es_ES |
dc.issue.number | 8 | es_ES |
dc.page.initial | 1295 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.subject.unesco | 2417.13 Ecología Vegetal | es_ES |
dc.subject.unesco | 3106.99 Otras (Incendios forestales) | es_ES |
dc.subject.unesco | 3106.06 Protección | es_ES |
dc.subject.unesco | 2506.16 Teledetección (Geología) | es_ES |
dc.description.project | FIRESEVES (AGL2017-86075-C2-1-R) project funded by the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund, and SEFIRECYL (LE001P17) | es_ES |