{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:49:56Z","timestamp":1784764196905,"version":"3.55.0"},"reference-count":63,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T00:00:00Z","timestamp":1625356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precise assessments of forest species\u2019 composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras\u2013Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.<\/jats:p>","DOI":"10.3390\/rs13132627","type":"journal-article","created":{"date-parts":[[2021,7,4]],"date-time":"2021-07-04T22:35:22Z","timestamp":1625438122000},"page":"2627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery"],"prefix":"10.3390","volume":"13","author":[{"given":"Marks Melo","family":"Moura","sequence":"first","affiliation":[{"name":"Department of Forest Engineering, Federal University of Paran\u00e1, Av. Loth\u00e1rio Meissner, 900, Curitiba 80270-170, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luiz Eduardo Soares","family":"de Oliveira","sequence":"additional","affiliation":[{"name":"Department of Informatics, Federal University of Paran\u00e1, Av. Cel. Francisco H. dos Santos, 100, Curitiba 81530-000, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos Roberto","family":"Sanquetta","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Federal University of Paran\u00e1, Av. Loth\u00e1rio Meissner, 900, Curitiba 80270-170, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexis","family":"Bastos","sequence":"additional","affiliation":[{"name":"Cultural and Environmental Study Center of the Amazon Region\u2014RIOTERRA, Rua Padre Chiquinho, 1651, Porto Velho 76803-786, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Midhun","family":"Mohan","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California-Berkeley, Berkeley, CA 94709, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8529-5554","authenticated-orcid":false,"given":"Ana Paula Dalla","family":"Corte","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Federal University of Paran\u00e1, Av. Loth\u00e1rio Meissner, 900, Curitiba 80270-170, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1139\/juvs-2020-0014","article-title":"Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV","volume":"8","author":"Natesan","year":"2020","journal-title":"J. Unmanned Veh. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Casapia, X.T., Falen, L., Bartholomeus, H., C\u00e1rdenas, R., Flores, G., Herold, M., Coronado, E.N.H., and Baker, T.R. (2019). Identifying and Quantifying the Abundance of Economically Important Palms in Tropical Moist Forest Using UAV Imagery. 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