{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T18:04:37Z","timestamp":1773684277983,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,19]],"date-time":"2020-04-19T00:00:00Z","timestamp":1587254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["88881.187406\/2018-01"],"award-info":[{"award-number":["88881.187406\/2018-01"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["303559\/2019-5"],"award-info":[{"award-number":["303559\/2019-5"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["433783\/2018-4"],"award-info":[{"award-number":["433783\/2018-4"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["153854\/2016-2"],"award-info":[{"award-number":["153854\/2016-2"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2013\/50426-4"],"award-info":[{"award-number":["2013\/50426-4"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["327861"],"award-info":[{"award-number":["327861"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep neural networks are currently the focus of many remote sensing approaches related to forest management. Although they return satisfactory results in most tasks, some challenges related to hyperspectral data remain, like the curse of data dimensionality. In forested areas, another common problem is the highly-dense distribution of trees. In this paper, we propose a novel deep learning approach for hyperspectral imagery to identify single-tree species in highly-dense areas. We evaluated images with 25 spectral bands ranging from 506 to 820 nm taken over a semideciduous forest of the Brazilian Atlantic biome. We included in our network\u2019s architecture a band combination selection phase. This phase learns from multiple combinations between bands which contributed the most for the tree identification task. This is followed by a feature map extraction and a multi-stage model refinement of the confidence map to produce accurate results of a highly-dense target. Our method returned an f-measure, precision and recall values of 0.959, 0.973, and 0.945, respectively. The results were superior when compared with a principal component analysis (PCA) approach. Compared to other learning methods, ours estimate a combination of hyperspectral bands that most contribute to the mentioned task within the network\u2019s architecture. With this, the proposed method achieved state-of-the-art performance for detecting and geolocating individual tree-species in UAV-based hyperspectral images in a complex forest.<\/jats:p>","DOI":"10.3390\/rs12081294","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T04:49:38Z","timestamp":1587444578000},"page":"1294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8571-1383","authenticated-orcid":false,"given":"Gabriela Takahashi","family":"Miyoshi","sequence":"first","affiliation":[{"name":"Graduate Program in Cartographic Sciences, S\u00e3o Paulo State University (UNESP), Presidente Prudente 19060-900, SP,  Brazil"}]},{"given":"Mauro dos Santos","family":"Arruda","sequence":"additional","affiliation":[{"name":"Graduate Program in Computer Sciences, Faculty of Computer Science, Federal University of Mato Grosso do Sul (UFMS), Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0258-536X","authenticated-orcid":false,"given":"Lucas Prado","family":"Osco","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Architecture and Urbanism, University of Western S\u00e3o Paulo (UNOESTE), R. Jos\u00e9 Bongiovani, Cidade Universit\u00e1ria, Presidente Prudente 19050-920, SP, Brazil"},{"name":"Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul (UFMS), Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9096-6866","authenticated-orcid":false,"given":"Jos\u00e9","family":"Marcato Junior","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul (UFMS), Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"given":"Diogo Nunes","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Graduate Program in Computer Sciences, Faculty of Computer Science, Federal University of Mato Grosso do Sul (UFMS), Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]},{"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), Presidente Prudente 19060-900, SP,  Brazil"},{"name":"Department of Cartography, S\u00e3o Paulo State University (UNESP), Presidente Prudente 19060-900, SP, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0483-1103","authenticated-orcid":false,"given":"Antonio Maria Garcia","family":"Tommaselli","sequence":"additional","affiliation":[{"name":"Graduate Program in Cartographic Sciences, S\u00e3o Paulo State University (UNESP), Presidente Prudente 19060-900, SP,  Brazil"},{"name":"Department of Cartography, S\u00e3o Paulo State University (UNESP), Presidente Prudente 19060-900, SP, Brazil"}]},{"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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8815-6653","authenticated-orcid":false,"given":"Wesley Nunes","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Graduate Program in Computer Sciences, Faculty of Computer Science, Federal University of Mato Grosso do Sul (UFMS), Av. Costa e Silva, Campo Grande 79070-900, Brazil"},{"name":"Faculty of Engineering, Architecture, and Urbanism and Geography, Federal University of Mato Grosso do Sul (UFMS), Av. Costa e Silva, Campo Grande 79070-900, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P.J. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote. Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Guimar\u00e3es, N., P\u00e1dua, L., Marques, P., Silva, N., Peres, E., and Sousa, J.J. (2020). Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities. Remote. Sens., 12.","DOI":"10.3390\/rs12061046"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15467","DOI":"10.3390\/rs71115467","article-title":"Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level","volume":"7","author":"Honkavaara","year":"2015","journal-title":"Remote. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Saarinen, N., Vastaranta, M., N\u00e4si, R., Rosnell, T., Hakala, T., Honkavaara, E., Wulder, M.A., Luoma, V., Tommaselli, A.M.G., and Imai, N.N. (2018). Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote. Sens., 10.","DOI":"10.3390\/rs10020338"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Reis, B.P., Martins, S.V., Filho, E.I.F., Sarcinelli, T.S., Gleriani, J.M., Marcatti, G.E., Leite, H.G., and Halassy, M. (2019). Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR. Remote. Sens., 11.","DOI":"10.3390\/rs11131508"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111747","DOI":"10.1016\/j.rse.2020.111747","article-title":"The application of Unmanned Aerial Vehicles (UAVs) to estimate above-ground biomass of mangrove ecosystems","volume":"242","author":"Navarro","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_7","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. Remote. Sens., 12.","DOI":"10.3390\/rs12010009"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Li, L., Chen, J., Mu, X., Li, W., Yan, G., Xie, D., and Zhang, W. (2020). Quantifying Understory and Overstory Vegetation Cover Using UAV-Based RGB Imagery in Forest Plantation. Remote. Sens., 12.","DOI":"10.3390\/rs12020298"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyypp\u00e4, J., Saari, H., P\u00f6l\u00f6nen, I., and Imai, N.N. (2017). Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote. Sens., 9.","DOI":"10.3390\/rs9030185"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tuominen, S., N\u00e4si, R., Honkavaara, E., Balazs, A., Hakala, T., Viljanen, N., P\u00f6l\u00f6nen, I., Saari, H., and Ojanen, H. (2018). Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity. Remote. Sens., 10.","DOI":"10.3390\/rs10050714"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","article-title":"Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images","volume":"50","author":"Raczko","year":"2017","journal-title":"Eur. J. Remote. Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xie, Z., Chen, Y., Lu, D., Li, G., and Chen, E. (2019). Classification of Land Cover, Forest, and Tree Species Classes with ZiYuan-3 Multispectral and Stereo Data. Remote. Sens., 11.","DOI":"10.3390\/rs11020164"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Osco, L.P., Ramos, A.P.M., Pereira, D.R., Moriya, \u00c9., Imai, N.N., Matsubara, E., Estrabis, N., De Souza, M., Marcato, J., and Goncalves, W.N. (2019). Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery. Remote. Sens., 11.","DOI":"10.3390\/rs11242925"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Yokoya, N., Bui, D.T., Yoshino, K., and Friess, D.A. (2019). Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges. Remote. Sens., 11.","DOI":"10.3390\/rs11030230"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Miyoshi, G.T., Imai, N.N., Tommaselli, A.M.G., De Moraes, M.V.A., and Honkavaara, E. (2020). Evaluation of Hyperspectral Multitemporal Information to Improve Tree Species Identification in the Highly Diverse Atlantic Forest. Remote. Sens., 12.","DOI":"10.3390\/rs12020244"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Marrs, J., and Ni-Meister, W. (2019). Machine Learning Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data. Remote. Sens., 11.","DOI":"10.3390\/rs11070819"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Imangholiloo, M., Saarinen, N., Markelin, L., Rosnell, T., N\u00e4si, R., Hakala, T., Honkavaara, E., Holopainen, M., Hyypp\u00e4, J., and Vastaranta, M. (2019). Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle. Forests, 10.","DOI":"10.3390\/f10050415"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models. Remote. Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ufug.2018.01.010","article-title":"Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft","volume":"30","author":"Honkavaara","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Nezami, S., Khoramshahi, E., Nevalainen, O., P\u00f6l\u00f6nen, I., and Honkavaara, E. (2020). Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks. Remote. Sens., 12.","DOI":"10.20944\/preprints202002.0334.v1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1080\/15481603.2020.1712102","article-title":"Comparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data","volume":"57","author":"Sothe","year":"2020","journal-title":"GIScience Remote. Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., and Herrera, F. (2019). Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning. Remote. Sens., 11.","DOI":"10.3390\/rs11060643"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2017). Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12400","DOI":"10.1111\/exsy.12400","article-title":"A systematic review on deep learning architectures and applications","volume":"36","author":"Khamparia","year":"2019","journal-title":"Expert Syst."},{"key":"ref_29","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R.B., He, K., and Doll\u00e1r, P. (2017). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_31","unstructured":"Ren, S., He, K., Girshick, R.B., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dos Santos, A.A., Marcato Junior, J., Ara\u00fajo, M.S., Di Martini, D.R., Tetila, E.C., Siqueira, H.L., Aoki, C., Eltner, A., Matsubara, E.T., and Pistori, H. (2019). Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs. Sensors, 19.","DOI":"10.3390\/s19163595"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lobo Torres, D., Feitosa, R., Nigri Happ, P., Cue La Rosa, L., Junior, J., Martins, J., Bressan, P., Gon\u00e7alves, W., and Liesenberg, V. (2020). Applying Fully Convolutional Architectures for Semantic Segmentation of a Single Tree Species in Urban Environment on High Resolution UAV Optical Imagery. Sensors, 20.","DOI":"10.3390\/s20020563"},{"key":"ref_34","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2019.07.010","article-title":"Mapping dead forest cover using a deep convolutional neural network and digital aerial photography","volume":"156","author":"Sylvain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., and White, E. (2019). Individual tree-crown detection in RGB imagery using self-supervised deep learning neural networks. bioRxiv.","DOI":"10.1101\/532952"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Hartling, S., Sagan, V., Sidike, P., Maimaitijiang, M., and Carron, J. (2019). Urban Tree Species Classification Using a WorldView-2\/3 and LiDAR Data Fusion Approach and Deep Learning. Sensors, 19.","DOI":"10.3390\/s19061284"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hennessy, A., Clarke, K., and Lewis, M. (2020). Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability. Remote. Sens., 12.","DOI":"10.3390\/rs12010113"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep Learning for Classification of Hyperspectral Data: A Comparative Review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (2005). Remote Sensing Digital Image Analysis: An Introduction, Springer. [4th ed.].","DOI":"10.1007\/3-540-29711-1"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Maschler, J., Atzberger, C., and Immitzer, M. (2018). Individual Tree Crown Segmentation and Classification of 13 Tree Species Using Airborne Hyperspectral Data. Remote. Sens., 10.","DOI":"10.3390\/rs10081218"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, L., Song, B., Zhang, S., and Liu, X. (2017). A Novel Principal Component Analysis Method for the Reconstruction of Leaf Reflectance Spectra and Retrieval of Leaf Biochemical Contents. Remote. Sens., 9.","DOI":"10.3390\/rs9111113"},{"key":"ref_46","unstructured":"Johnson, R.A., and Wichern, D.W. (2007). Applied Multivariate Statistical Analysis, Pearson Prentice Hall."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1080\/2150704X.2017.1322733","article-title":"Tree crown detection and delineation in satellite images using probabilistic voting","volume":"8","author":"Hisar","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Csillik, O., Cherbini, J., Johnson, R., Lyons, A., and Kelly, M. (2018). Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones, 2.","DOI":"10.3390\/drones2040039"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ampatzidis, Y., and Partel, V. (2019). UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence. Remote. Sens., 11.","DOI":"10.3390\/rs11040410"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2019.12.010","article-title":"A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery","volume":"160","author":"Osco","year":"2020","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_51","unstructured":"(2016, October 15). Brasil Descreto s\/n de 16 de julho de 2002, Available online: http:\/\/www.planalto.gov.br\/ccivil_03\/dnn\/2002\/Dnn9609.htm."},{"key":"ref_52","unstructured":"(2016, October 15). Brasil Descreto s\/n de 14 de maio de 2004, Available online: http:\/\/www.planalto.gov.br\/CCIVIL_03\/_Ato2004-2006\/2004\/Decreto\/_quadro.htm."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5385","DOI":"10.1109\/JSTARS.2016.2606320","article-title":"Identification of Successional Stages and Cover Changes of Tropical Forest Based on Digital Surface Model Analysis","volume":"9","author":"Berveglieri","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.isprsjprs.2018.11.002","article-title":"Successional stages and their evolution in tropical forests using multi-temporal photogrammetric surface models and superpixels","volume":"146","author":"Berveglieri","year":"2018","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1038\/hdy.2016.130","article-title":"Early genetic consequences of defaunation in a large-seeded vertebrate-dispersed palm (Syagrus romanzoffiana)","volume":"118","author":"Giombini","year":"2017","journal-title":"Heredity"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Elias, G., Colares, R., Rocha Antunes, A., Padilha, P., Tucker Lima, J., and Santos, R. (2019). Palm (Arecaceae) Communities in the Brazilian Atlantic Forest: A Phytosociological Study. Floresta e Ambiente, 26.","DOI":"10.1590\/2179-8087.041318"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1080\/01650521.2011.617065","article-title":"Seed dispersal and predation in the palm Syagrus romanzoffiana on two islands with different faunal richness, southern Brazil","volume":"46","author":"Begnini","year":"2011","journal-title":"Stud. Neotrop. Fauna Environ."},{"key":"ref_58","unstructured":"Brasil, D.F. (2020, March 03). Esp\u00e9cies Nativas da Flora Brasileira de Valor Econ\u00f4mico Atual ou Potencial: Plantas para o Futuro-Regi\u00e3o Centro-Oeste. Available online: https:\/\/www.alice.cnptia.embrapa.br\/handle\/doc\/1073295."},{"key":"ref_59","unstructured":"Lorenzi, H. (1992). \u00c1rvores Brasileiras, Instituto Plantarum de Estudos da Flora. [1st ed.]."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Mendes, C., Ribeiro, M., and Galetti, M. (2015). Patch size, shape and edge distance influence seed predation on a palm species in the Atlantic forest. Ecography, 39.","DOI":"10.1111\/ecog.01592"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sica, Y., Bravo, S.P., and Giombini, M. (2014). Spatial Pattern of Pind\u00f3 Palm (Syagrus romanzoffiana) Recruitment in Argentinian Atlantic Forest: The Importance of Tapir and Effects of Defaunation. Biotropica, 46.","DOI":"10.1111\/btp.12152"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5006","DOI":"10.3390\/rs5105006","article-title":"Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture","volume":"5","author":"Honkavaara","year":"2013","journal-title":"Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2017.10.014","article-title":"Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes","volume":"134","author":"Honkavaara","year":"2017","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Honkavaara, E., and Khoramshahi, E. (2018). Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment. Remote. Sens., 10.","DOI":"10.3390\/rs10020256"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2653","DOI":"10.1080\/014311699211994","article-title":"The use of the empirical line method to calibrate remotely sensed data to reflectance","volume":"20","author":"Smith","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"4910","DOI":"10.1080\/01431161.2018.1425570","article-title":"Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment","volume":"39","author":"Miyoshi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","unstructured":"Aich, S., and Stavness, I. (2018). Improving Object Counting with Heatmap Regulation. arXiv."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_69","first-page":"397","article-title":"Accuracy assessment: A user\u2019s perspective","volume":"52","author":"Story","year":"1986","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_70","unstructured":"Jensen, J.R. (2007). Remote Sensing of the Environment: An Earth Resource Perspective, Pearson Prentice Hall. Prentice Hall Series in Geographic Information Science."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.3390\/rs4061820","article-title":"Species-Level Differences in Hyperspectral Metrics among Tropical Rainforest Trees as Determined by a Tree-Based Classifier","volume":"4","author":"Clark","year":"2012","journal-title":"Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote. Sens. Environ."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Natesan, S., Armenakis, C., and Vepakomma, U. (2019). RESNET-Based tree species classification using UAV images. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.","DOI":"10.5194\/isprs-archives-XLII-2-W13-475-2019"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1294\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:09:03Z","timestamp":1760364543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/8\/1294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,19]]},"references-count":73,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12081294"],"URL":"https:\/\/doi.org\/10.3390\/rs12081294","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,19]]}}}