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Unmanned aerial vehicles (UAVs) and miniaturized lightweight sensors can rapidly provide accurate monitoring information. The objective of this study was to investigate the use of multitemporal, UAV-based hyperspectral imagery for tree species identification in the highly diverse Brazilian Atlantic forest. Datasets were captured over three years to identify eight different tree species. The study area comprised initial to medium successional stages of the Brazilian Atlantic forest. Images were acquired with a spatial resolution of 10 cm, and radiometric adjustment processing was performed to reduce the variations caused by different factors, such as the geometry of acquisition. The random forest classification method was applied in a region-based classification approach with leave-one-out cross-validation, followed by computing the area under the receiver operating characteristic (AUCROC) curve. When using each dataset alone, the influence of different weather behaviors on tree species identification was evident. When combining all datasets and minimizing illumination differences over each tree crown, the identification of three tree species was improved. These results show that UAV-based, hyperspectral, multitemporal remote sensing imagery is a promising tool for tree species identification in tropical forests.<\/jats:p>","DOI":"10.3390\/rs12020244","type":"journal-article","created":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T10:20:29Z","timestamp":1578651629000},"page":"244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Evaluation of Hyperspectral Multitemporal Information to Improve Tree Species Identification in the Highly Diverse Atlantic Forest"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8571-1383","authenticated-orcid":false,"given":"Gabriela","family":"Takahashi Miyoshi","sequence":"first","affiliation":[{"name":"Graduate Program in Cartographic Sciences, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0516-0567","authenticated-orcid":false,"given":"Nilton Nobuhiro","family":"Imai","sequence":"additional","affiliation":[{"name":"Graduate Program in Cartographic Sciences, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil"},{"name":"Department of Cartography, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0483-1103","authenticated-orcid":false,"given":"Antonio Maria","family":"Garcia Tommaselli","sequence":"additional","affiliation":[{"name":"Graduate Program in Cartographic Sciences, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil"},{"name":"Department of Cartography, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2024-1197","authenticated-orcid":false,"given":"Marcus Vin\u00edcius","family":"Antunes de Moraes","sequence":"additional","affiliation":[{"name":"Graduate Program in Cartographic Sciences, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente SP 19060-900, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-2145","authenticated-orcid":false,"given":"Eija","family":"Honkavaara","sequence":"additional","affiliation":[{"name":"Finnish Geospatial Research Institute, National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3462","DOI":"10.3390\/rs4113462","article-title":"Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data","volume":"4","author":"Colgan","year":"2012","journal-title":"Remote Sens."},{"key":"ref_3","first-page":"101","article-title":"Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation","volume":"18","author":"Heinzel","year":"2012","journal-title":"Int. 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