{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T14:54:54Z","timestamp":1781708094941,"version":"3.54.5"},"reference-count":93,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T00:00:00Z","timestamp":1624406400000},"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":["88882.433953\/2019-01"],"award-info":[{"award-number":["88882.433953\/2019-01"]}],"id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior - Programa Institucional de Internacionaliza\u00e7\u00e3o","award":["88881.310314\/2018-01"],"award-info":[{"award-number":["88881.310314\/2018-01"]}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["404379\/2016-8"],"award-info":[{"award-number":["404379\/2016-8"]}],"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":["303670\/2018-5"],"award-info":[{"award-number":["303670\/2018-5"]}],"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":["273806"],"award-info":[{"award-number":["273806"]}],"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>Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon\u2013Waver (H\u2019) and Simpson\u2019s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H\u2019and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H\u2019and D, the RMSE was 5.2\u201310% with a bias between \u22121.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2\u201327.6% and the bias between \u221212.4% and \u22120.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data.<\/jats:p>","DOI":"10.3390\/rs13132444","type":"journal-article","created":{"date-parts":[[2021,6,22]],"date-time":"2021-06-22T23:44:02Z","timestamp":1624405442000},"page":"2444","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5318-2627","authenticated-orcid":false,"given":"Rorai Pereira","family":"Martins-Neto","sequence":"first","affiliation":[{"name":"Graduate Program in Cartographic Sciences, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente 19060-900, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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), Roberto Simonsen 305, Presidente Prudente 19060-900, SP, Brazil"},{"name":"Department of Cartography, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente 19060-900, SP, 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 19060-900, SP, Brazil"},{"name":"Department of Cartography, S\u00e3o Paulo State University (UNESP), Roberto Simonsen 305, Presidente Prudente 19060-900, SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan Camil","family":"David","sequence":"additional","affiliation":[{"name":"Department of Forestry, Federal Rural University of Amazonia (UFRA), Tv. Pau Amarelo s\/n, Capit\u00e3o Po\u00e7o 68650-000, PA, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4715-5048","authenticated-orcid":false,"given":"Milto","family":"Miltiadou","sequence":"additional","affiliation":[{"name":"ERATOSTHENES Centre of Excellence, Limassol 3036, Cyprus"},{"name":"Laboratory of Remote Sensing and Geo-Environment, Department of Civil Engineering and Geomatics, School of Engineering and Technology, Cyprus University of Technology, Limassol 3036, Cyprus"}],"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 (FGI), National Land Survey of Finland, Geodeetinrinne 2, 02430 Masala, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"188","DOI":"10.4322\/natcon.2011.024","article-title":"Distribution and Endemism of Angiosperms in the Atlantic Forest","volume":"9","author":"Sobral","year":"2011","journal-title":"Nat. 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