| dc.contributor.author | Gil-Docampo, M. L. | |
| dc.contributor.author | Ortiz-Sanz, J. | |
| dc.contributor.author | Martínez-Rodríguez, S. | |
| dc.contributor.author | Marcos-Robles, J. L. | |
| dc.contributor.author | Arza-García, M. | |
| dc.contributor.author | Sánchez-Sastre, L. F. | |
| dc.date.accessioned | 2025-12-29T19:30:38Z | |
| dc.date.available | 2025-12-29T19:30:38Z | |
| dc.date.issued | 2020 | |
| dc.identifier.citation | Geocarto International, 2020, 35(2), 128–140. | es |
| dc.identifier.issn | 1010-6049 | es |
| dc.identifier.uri | https://uvadoc.uva.es/handle/10324/81092 | |
| dc.description | Producción Científica | es |
| dc.description.abstract | Water supply devices enable afforestation in dry climates and on poor lands with generally high success rates. Previous survival analyses have been based on the direct observation of each individual plant in the field, which entails considerable effort and costs. This study provides a low-cost method to discriminate between live and dead plants in afforestation that can efficiently replace traditional field inspections through the use of unmanned aerial vehicles (UAVs) equipped with RGB and NIR sensors. The method combines the use of a conventional camera with an identical camera modified to record the NIR channel. Survival analysis was performed with digital image processing techniques based on calculated indices associated with plant vigour and PCA-based decorrelation. The method yielded results with high global accuracy rates (∼96.2%) with a minimum percentage of doubtful plants, even in young plantations (seedlings <30 cm tall). The procedure could be particularly useful in hazardous areas. | es |
| dc.format.mimetype | application/pdf | es |
| dc.language.iso | spa | es |
| dc.publisher | Taylor&Francis | es |
| dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | es |
| dc.subject.classification | UAV | es |
| dc.subject.classification | plant control | es |
| dc.subject.classification | degraded region | es |
| dc.subject.classification | gap detection | es |
| dc.subject.classification | Waterboxx | es |
| dc.title | Plant survival monitoring with UAVs and multispectral data in difficult access afforested areas | es |
| dc.type | info:eu-repo/semantics/article | es |
| dc.identifier.doi | 10.1080/10106049.2018.1508312 | es |
| dc.relation.publisherversion | https://www.tandfonline.com/doi/full/10.1080/10106049.2018.1508312 | es |
| dc.identifier.publicationfirstpage | 128 | es |
| dc.identifier.publicationissue | 2 | es |
| dc.identifier.publicationlastpage | 140 | es |
| dc.identifier.publicationtitle | Geocarto International | es |
| dc.identifier.publicationvolume | 35 | es |
| dc.peerreviewed | SI | es |
| dc.description.project | This work was supported by the Xunta de Galicia under the Grant ‘Financial aid for the consolidation and structure of competitive units of investigation in the universities of the University Galician System (2016-18)’ [ED431B 2016/030, ED341D R2016/023] and the European Program Life + [LIFE/ENV/ES/000447] ‘The Green Deserts: New planting techniques for tree cultivation in desertified environments to face Climate Change’. | es |
| dc.identifier.essn | 1752-0762 | es |
| dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion | es |