Identification of significative lidar metrics and comparison of machine learning approaches for estimating stand and diversity variables in heterogeneous brazilian atlantic forest

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorFederal Rural University of Amazonia (UFRA)-
Autor(es): dc.contributorERATOSTHENES Centre of Excellence-
Autor(es): dc.contributorCyprus University of Technology-
Autor(es): dc.contributorNational Land Survey of Finland-
Autor(es): dc.creatorMartins-Neto, Rorai Pereira-
Autor(es): dc.creatorTommaselli, Antonio Maria Garcia-
Autor(es): dc.creatorImai, Nilton Nobuhiro-
Autor(es): dc.creatorDavid, Hassan Camil-
Autor(es): dc.creatorMiltiadou, Milto-
Autor(es): dc.creatorHonkavaara, Eija-
Data de aceite: dc.date.accessioned2025-08-21T21:16:55Z-
Data de disponibilização: dc.date.available2025-08-21T21:16:55Z-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2022-04-29-
Data de envio: dc.date.issued2021-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs13132444-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/229090-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/229090-
Descrição: dc.descriptionData 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–Waver (H’) and Simpson’s 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’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% 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 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionSão Paulo State University (UNESP), Roberto Simonsen 305-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP), Roberto Simonsen 305-
Descrição: dc.descriptionDepartment of Forestry Federal Rural University of Amazonia (UFRA), Tv. Pau Amarelo s/n-
Descrição: dc.descriptionERATOSTHENES Centre of Excellence-
Descrição: dc.descriptionLaboratory of Remote Sensing and Geo-Environment Department of Civil Engineering and Geomatics School of Engineering and Technology Cyprus University of Technology-
Descrição: dc.descriptionFinnish Geospatial Research Institute (FGI) National Land Survey of Finland, Geodeetinrinne 2-
Descrição: dc.descriptionSão Paulo State University (UNESP), Roberto Simonsen 305-
Descrição: dc.descriptionDepartment of Cartography São Paulo State University (UNESP), Roberto Simonsen 305-
Descrição: dc.descriptionFAPESP: 2013/50426-4-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAirborne laser scanning-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectForest attributes-
Palavras-chave: dc.subjectForest structure-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectMultiple linear regression-
Palavras-chave: dc.subjectNeural network-
Palavras-chave: dc.subjectRandom forest-
Palavras-chave: dc.subjectSupport vector machine-
Palavras-chave: dc.subjectTropical forests-
Título: dc.titleIdentification of significative lidar metrics and comparison of machine learning approaches for estimating stand and diversity variables in heterogeneous brazilian atlantic forest-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.