{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T22:16:48Z","timestamp":1776377808423,"version":"3.51.2"},"reference-count":74,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,10]],"date-time":"2022-02-10T00:00:00Z","timestamp":1644451200000},"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 Aperfeicoamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","award":["Finance Code 001"],"award-info":[{"award-number":["Finance Code 001"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming powerful tools for vegetation monitoring, this study aims to use DL to automatically map individual crowns of Vismia (low resilience recovery indicator), Cecropia (fast recovery indicator), and trees in general (this study refers to individual crowns of all trees regardless of species as All Trees). Since All Trees can be accurately mapped, this study also aims to propose a tree crown heterogeneity index (TCHI), which estimates species diversity based on: the heterogeneity attributes\/parameters of the RPA image inside the All Trees results; and the Shannon index measured by traditional fieldwork. Regarding the DL methods, this work evaluated the accuracy of the detection of individual objects, the quality of the delineation outlines and the area distribution. Except for Vismia delineation (IoU = 0.2), DL results presented accurate values in general, as F1 and IoU were always greater than 0.7 and 0.55, respectively, while Cecropia presented the most accurate results: F1 = 0.85 and IoU = 0.77. Since All Trees results were accurate, the TCHI was obtained through regression analysis between the canopy height model (CHM) heterogeneity attributes and the field plot data. Although TCHI presented robust parameters, such as p-value &lt; 0.05, its results are considered preliminary because more data are needed to include different FR situations. Thus, the results of this work show that low-cost RPA has great potential for monitoring FR quality in the Amazon, because Vismia, Cecropia, and All Trees can be automatically mapped. Moreover, the TCHI preliminary results showed high potential in estimating species diversity. Future studies must assess domain adaptation methods for the DL results and different FR situations to improve the TCHI range of action.<\/jats:p>","DOI":"10.3390\/rs14040830","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T02:40:17Z","timestamp":1644547217000},"page":"830","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Mapping Key Indicators of Forest Restoration in the Amazon Using a Low-Cost Drone and Artificial Intelligence"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4294-5876","authenticated-orcid":false,"given":"Rafael Walter","family":"Albuquerque","sequence":"first","affiliation":[{"name":"Spatial Analysis and Modelling Lab (SPAMLab), Institute of Energy and Environment, University of S\u00e3o Paulo, Prof. Luciano Gualberto Avenue, 1289, S\u00e3o Paulo 05508-010, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2709-5308","authenticated-orcid":false,"given":"Daniel Luis Mascia","family":"Vieira","sequence":"additional","affiliation":[{"name":"Embrapa Genetic Resources and Biotechnology, Parque Esta\u00e7\u00e3o Biol\u00f3gica, PqEB, Av. W5 Norte, Cx. Postal 02372, Bras\u00edlia 70770-917, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4516-6373","authenticated-orcid":false,"given":"Manuel Eduardo","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Laborat\u00f3rio de Processamento de Imagens e Geoprocessamento\u2014LAPIG\/Pro-Vant, Instituto de Estudos Socioambientais\u2014IESA, Campus II, Universidade Federal de Goi\u00e1s\u2014UFG, Cx. Postal 131, Goi\u00e2nia 74001-970, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6980-597X","authenticated-orcid":false,"given":"Lucas Pedrosa","family":"Soares","sequence":"additional","affiliation":[{"name":"Institute of Geosciences, University of S\u00e3o Paulo, Rua do Lago, 562, S\u00e3o Paulo 05508-080, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-1914","authenticated-orcid":false,"given":"S\u00f8ren Ingvor","family":"Olsen","sequence":"additional","affiliation":[{"name":"Department of Computer Science (DIKU), University of Copenhagen, Universitetsparken 1, 2100 \u00d8 Copenhagen, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9980-4927","authenticated-orcid":false,"given":"Luciana Spinelli","family":"Araujo","sequence":"additional","affiliation":[{"name":"Embrapa Meio Ambiente, Rodovia SP 340, KM 127 S\/N, Jaguari\u00fana 13820-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9851-2527","authenticated-orcid":false,"given":"Luiz Eduardo","family":"Vicente","sequence":"additional","affiliation":[{"name":"Embrapa Meio Ambiente, Rodovia SP 340, KM 127 S\/N, Jaguari\u00fana 13820-000, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7510-0143","authenticated-orcid":false,"given":"Julio Ricardo Caetano","family":"Tymus","sequence":"additional","affiliation":[{"name":"The Nature Conservancy Brasil\u2014TNC, Av. 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