000125255 001__ 125255
000125255 005__ 20241125101136.0
000125255 0247_ $$2doi$$a10.3390/rs15030722
000125255 0248_ $$2sideral$$a132985
000125255 037__ $$aART-2023-132985
000125255 041__ $$aeng
000125255 100__ $$0(orcid)0000-0002-8362-7559$$aDomingo, Dario
000125255 245__ $$aAssessing the efficacy of phenological spectral differences to detect invasive alien acacia dealbata using Sentinel-2 data in Southern Europe
000125255 260__ $$c2023
000125255 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125255 5203_ $$aInvasive alien plants are transforming the landscapes, threatening the most vulnerable elements of local biodiversity across the globe. The monitoring of invasive species is paramount for minimizing the impact on biodiversity. In this study, we aim to discriminate and identify the spatial extent of Acacia dealbata Link from other species using RGB-NIR Sentinel-2 data based on phenological spectral peak differences. Time series were processed using the Earth Engine platform and random forest importance was used to select the most suitable Sentinel-2 derived metrics. Thereafter, a random forest machine learning algorithm was trained to discriminate between A. dealbata and native species. A flowering period was detected in March and metrics based on the spectral difference between blooming and the pre flowering (January) or post flowering (May) months were highly suitable for A. dealbata discrimination. The best-fitted classification model shows an overall accuracy of 94%, including six Sentinel-2 derived metrics. We find that 55% of A. dealbata presences were widely widespread in patches replacing Pinus pinaster Ait. stands. This invasive alien species also creates continuous monospecific stands representing 33% of the presences. This approach demonstrates its value for detecting and mapping A. dealbata based on RGB-NIR bands and phenological peak differences between blooming and pre or post flowering months providing suitable information for an early detection of invasive species to improve sustainable forest management.
000125255 536__ $$9info:eu-repo/grantAgreement/ES/NextGenerationEU/MS-240621
000125255 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125255 590__ $$a4.2$$b2023
000125255 592__ $$a1.091$$b2023
000125255 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b34 / 254 = 0.134$$c2023$$dQ1$$eT1
000125255 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2023$$dQ1
000125255 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b10 / 36 = 0.278$$c2023$$dQ2$$eT1
000125255 591__ $$aREMOTE SENSING$$b16 / 63 = 0.254$$c2023$$dQ2$$eT1
000125255 591__ $$aENVIRONMENTAL SCIENCES$$b110 / 358 = 0.307$$c2023$$dQ2$$eT1
000125255 594__ $$a8.3$$b2023
000125255 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125255 700__ $$aPérez Rodríguez, Fernando
000125255 700__ $$aGómez García, Esteban
000125255 700__ $$aRodríguez Puerta, Francisco
000125255 773__ $$g15, 3 (2023), 722 [12 pp]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000125255 8564_ $$s8654758$$uhttps://zaguan.unizar.es/record/125255/files/texto_completo.pdf$$yVersión publicada
000125255 8564_ $$s2743111$$uhttps://zaguan.unizar.es/record/125255/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125255 909CO $$ooai:zaguan.unizar.es:125255$$particulos$$pdriver
000125255 951__ $$a2024-11-22-12:00:50
000125255 980__ $$aARTICLE